This document describes a study that used low-cost electrochemical sensors to monitor air quality in San Isidro district, Lima, Peru. Preliminary results from 6 days of monitoring carbon dioxide, volatile organic compounds, carbon monoxide, sulfur dioxide, ozone, and nitrogen dioxide are presented. Neural networks were used to predict carbon dioxide and sulfur dioxide levels. The sensors provided real-time air pollution data at a low-cost compared to traditional fixed monitoring stations.
Estimation of Weekly Reference Evapotranspiration using Linear Regression and...IDES Editor
The study investigates the applicability of linear
regression and ANN models for estimating weekly reference
evapotranspiration (ET0) at Tirupati, Nellore, Rajahmundry,
Anakapalli and Rajendranagar regions of Andhra Pradesh.
The climatic parameters influencing ET0 were identified
through multiple and partial correlation analysis. The
sunshine, temperature, wind velocity and relative humidity
mostly influenced the study area in the weekly ET0 estimation.
Linear regression models in terms of the climatic parameters
influencing the regions and, optimal neural network
architectures considering these climatic parameters as inputs
were developed. The models’ performance was evaluated with
respect to ET0 estimated by FAO-56 Penman-Monteith method.
The linear regression models showed a satisfactory
performance in the weekly ET0 estimation in the regions
selected for the present study. The ANN (4,4,1) models,
however, consistently showed a slightly improved performance
over linear regression models.
Prediction of Extreme Wind Speed Using Artificial Neural Network ApproachScientific Review SR
Prediction of an accurate wind speed of wind farms is necessary because of the intermittent nature
of wind for any region. Number of methods such as persistence, physical, statistical, spatial correlation, artificial
intelligence network and hybrid are generally available for prediction of wind speed. In this paper, ANN based
methods viz., Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are used. The
performance of the networks applied for prediction of wind speed is evaluated by model performance indicators
viz., Correlation Coefficient (CC), Model Efficiency (MEF) and Mean Absolute Percentage Error (MAPE).
Meteorological parameters such as maximum and minimum temperature, air pressure, solar radiation and
altitude are considered as input units for MLP and RBF networks to predict the extreme wind speed at Delhi.
The study shows the values of CC, MEF and MAPE between the observed and predicted wind speed (using
MLP) are computed as 0.992, 95.4% and 4.3% respectively while training the network data. For RBF network,
the values of CC, MEF and MAPE are computed as 0.992, 95.9% and 3.0% respectively. The model
performance analysis indicates the RBF is better suited network among two different networks studied for
prediction of extreme wind speed at Delhi.
This document presents a study on using an artificial neural network (ANN) and multi-sensor data to predict toxic gases. Researchers developed a multi-layer perceptron (MLP) neural network model using backpropagation to predict hydrogen sulfide, nitrogen dioxide, and their mixture using data from intelligent gas sensors. The network was trained on 160 samples and tested on the remaining 20 samples. Results showed the model with one hidden layer of 75 nodes achieved high prediction accuracy, approving the robustness of the developed ANN model for electronic nose prediction in challenging conditions like low gas concentrations and complex gas mixtures.
This document presents research using artificial neural networks to identify toxic gases in real time. A multi-layer perceptron neural network was trained using data from a multi-sensor system that detected hydrogen sulfide, nitrogen dioxide, and their mixture. Features extracted from the sensor responses were used as inputs to the neural network. The network was trained online using backpropagation and achieved 100% accuracy classifying gases during training and 96.6% accuracy during testing, with low error rates. This model achieved better performance than previous methods and can identify low concentrations of toxic gases in real time, which has applications for air quality monitoring and safety.
Applications of Artificial Neural Networks in Cancer PredictionIRJET Journal
This document discusses applications of artificial neural networks in cancer prediction and prognosis. It summarizes several studies that have used ANNs to predict breast cancer prognosis and recurrence, as well as classify types of lung cancer.
For breast cancer prognosis, a Maximum Entropy Estimation model was shown to outperform multi-layer perceptrons and probabilistic neural networks. For predicting breast cancer recurrence, an ANN achieved the best performance compared to other machine learning algorithms based on accuracy and AUC.
An ANN combined with a genetic algorithm was also able to successfully identify genes that classify lung cancer status. The ANN-GA model achieved over 97% accuracy in classifying different types of lung cancer based on gene expression data.
Cooperative Spectrum Sensing Technique Based on Blind Detection MethodINFOGAIN PUBLICATION
1) The document proposes cooperative spectrum sensing techniques based on blind detection methods for cognitive radio networks. It studies eigenvalue-based and covariance-based spectrum sensing algorithms that do not require prior knowledge of the primary signal or noise.
2) The algorithms analyze the sample covariance matrix of the received signal to extract test statistics for detecting primary signal presence. Thresholds for the algorithms are determined using statistical theories to achieve desired probabilities of detection and false alarm.
3) Simulations evaluate the performance of the techniques under different conditions and signal types. Results show the proposed method has higher detection probability at low signal-to-noise ratios than maximum eigenvalue detection.
IRJET- Indian Water Pollution Monitoring and Forecasting for Anomaly with...IRJET Journal
This document describes a proposed system to monitor and forecast water pollution in India using wireless sensor networks and machine learning techniques. Sensors would collect data on various water quality parameters like pH, nitrates, fecal coliform, biochemical oxygen demand, dissolved oxygen, and temperature from multiple locations. The data would be analyzed using machine learning algorithms to predict future pollution levels and identify anomalies. The system aims to reduce pollution by providing advanced monitoring and early warnings. Redundant sensor nodes and failure detection would ensure the system's reliability.
Neural Network Implementation Control Mobile RobotIRJET Journal
This document describes the design and implementation of a neural network controlled mobile robot. The robot is equipped with IR sensors to detect obstacles and a microcontroller runs a neural network program. The neural network is trained offline using a backpropagation algorithm and sensor input patterns to navigate around obstacles. Experimental results showed the robot could successfully react to new obstacle configurations not in its training set. Potential applications of neural networks discussed include industrial process control, sales forecasting, and target marketing. The design could be improved by adding GPS and speed control to allow the robot to navigate to a target destination avoiding obstacles.
Estimation of Weekly Reference Evapotranspiration using Linear Regression and...IDES Editor
The study investigates the applicability of linear
regression and ANN models for estimating weekly reference
evapotranspiration (ET0) at Tirupati, Nellore, Rajahmundry,
Anakapalli and Rajendranagar regions of Andhra Pradesh.
The climatic parameters influencing ET0 were identified
through multiple and partial correlation analysis. The
sunshine, temperature, wind velocity and relative humidity
mostly influenced the study area in the weekly ET0 estimation.
Linear regression models in terms of the climatic parameters
influencing the regions and, optimal neural network
architectures considering these climatic parameters as inputs
were developed. The models’ performance was evaluated with
respect to ET0 estimated by FAO-56 Penman-Monteith method.
The linear regression models showed a satisfactory
performance in the weekly ET0 estimation in the regions
selected for the present study. The ANN (4,4,1) models,
however, consistently showed a slightly improved performance
over linear regression models.
Prediction of Extreme Wind Speed Using Artificial Neural Network ApproachScientific Review SR
Prediction of an accurate wind speed of wind farms is necessary because of the intermittent nature
of wind for any region. Number of methods such as persistence, physical, statistical, spatial correlation, artificial
intelligence network and hybrid are generally available for prediction of wind speed. In this paper, ANN based
methods viz., Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are used. The
performance of the networks applied for prediction of wind speed is evaluated by model performance indicators
viz., Correlation Coefficient (CC), Model Efficiency (MEF) and Mean Absolute Percentage Error (MAPE).
Meteorological parameters such as maximum and minimum temperature, air pressure, solar radiation and
altitude are considered as input units for MLP and RBF networks to predict the extreme wind speed at Delhi.
The study shows the values of CC, MEF and MAPE between the observed and predicted wind speed (using
MLP) are computed as 0.992, 95.4% and 4.3% respectively while training the network data. For RBF network,
the values of CC, MEF and MAPE are computed as 0.992, 95.9% and 3.0% respectively. The model
performance analysis indicates the RBF is better suited network among two different networks studied for
prediction of extreme wind speed at Delhi.
This document presents a study on using an artificial neural network (ANN) and multi-sensor data to predict toxic gases. Researchers developed a multi-layer perceptron (MLP) neural network model using backpropagation to predict hydrogen sulfide, nitrogen dioxide, and their mixture using data from intelligent gas sensors. The network was trained on 160 samples and tested on the remaining 20 samples. Results showed the model with one hidden layer of 75 nodes achieved high prediction accuracy, approving the robustness of the developed ANN model for electronic nose prediction in challenging conditions like low gas concentrations and complex gas mixtures.
This document presents research using artificial neural networks to identify toxic gases in real time. A multi-layer perceptron neural network was trained using data from a multi-sensor system that detected hydrogen sulfide, nitrogen dioxide, and their mixture. Features extracted from the sensor responses were used as inputs to the neural network. The network was trained online using backpropagation and achieved 100% accuracy classifying gases during training and 96.6% accuracy during testing, with low error rates. This model achieved better performance than previous methods and can identify low concentrations of toxic gases in real time, which has applications for air quality monitoring and safety.
Applications of Artificial Neural Networks in Cancer PredictionIRJET Journal
This document discusses applications of artificial neural networks in cancer prediction and prognosis. It summarizes several studies that have used ANNs to predict breast cancer prognosis and recurrence, as well as classify types of lung cancer.
For breast cancer prognosis, a Maximum Entropy Estimation model was shown to outperform multi-layer perceptrons and probabilistic neural networks. For predicting breast cancer recurrence, an ANN achieved the best performance compared to other machine learning algorithms based on accuracy and AUC.
An ANN combined with a genetic algorithm was also able to successfully identify genes that classify lung cancer status. The ANN-GA model achieved over 97% accuracy in classifying different types of lung cancer based on gene expression data.
Cooperative Spectrum Sensing Technique Based on Blind Detection MethodINFOGAIN PUBLICATION
1) The document proposes cooperative spectrum sensing techniques based on blind detection methods for cognitive radio networks. It studies eigenvalue-based and covariance-based spectrum sensing algorithms that do not require prior knowledge of the primary signal or noise.
2) The algorithms analyze the sample covariance matrix of the received signal to extract test statistics for detecting primary signal presence. Thresholds for the algorithms are determined using statistical theories to achieve desired probabilities of detection and false alarm.
3) Simulations evaluate the performance of the techniques under different conditions and signal types. Results show the proposed method has higher detection probability at low signal-to-noise ratios than maximum eigenvalue detection.
IRJET- Indian Water Pollution Monitoring and Forecasting for Anomaly with...IRJET Journal
This document describes a proposed system to monitor and forecast water pollution in India using wireless sensor networks and machine learning techniques. Sensors would collect data on various water quality parameters like pH, nitrates, fecal coliform, biochemical oxygen demand, dissolved oxygen, and temperature from multiple locations. The data would be analyzed using machine learning algorithms to predict future pollution levels and identify anomalies. The system aims to reduce pollution by providing advanced monitoring and early warnings. Redundant sensor nodes and failure detection would ensure the system's reliability.
Neural Network Implementation Control Mobile RobotIRJET Journal
This document describes the design and implementation of a neural network controlled mobile robot. The robot is equipped with IR sensors to detect obstacles and a microcontroller runs a neural network program. The neural network is trained offline using a backpropagation algorithm and sensor input patterns to navigate around obstacles. Experimental results showed the robot could successfully react to new obstacle configurations not in its training set. Potential applications of neural networks discussed include industrial process control, sales forecasting, and target marketing. The design could be improved by adding GPS and speed control to allow the robot to navigate to a target destination avoiding obstacles.
The document describes an intelligent fault diagnosis system for reciprocating pumps that uses pressure and flow signals as inputs. It consists of hardware for data acquisition and a software system for signal processing, feature extraction, and fault diagnosis using wavelet neural networks. The system was able to accurately diagnose three main fault types - seal ring faults, valve damage, and spring faults - based on differences observed in the pressure curves. Testing on over 12 samples of each fault type achieved a correct diagnosis rate of over 94%. The system provides a fast and effective means of remotely monitoring reciprocating pumps and identifying faults.
Wireless sensor network (WSN) are powered by batteries to perform various sensing tasks in a given
environment. The measurements made by the sensors are sometimes unreliable and erroneous due to noise
in the sensor or hardware failure. For a large scale WSN to be economically feasible, it is important to
ensure that the faulty node does not affect the overall behaviour of the system. In this paper a binary faulttolerant
event detection technique has been proposed for the non-symmetric errors and its performance has
been analysed. Theoretical analysis and simulation show that almost 97 percent of faults can be corrected
even when 10 percent sensor nodes are faulty.
Neural Network-Based Actuator Fault Diagnosis for a Non-Linear Multi-Tank SystemISA Interchange
The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H1 framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks.
The document discusses various applications of artificial neural networks (ANNs) including electrical load forecasting, system identification, control systems, and pattern recognition. It provides details on ANN approaches for each application area. For electrical load forecasting, ANNs can be used to classify forecasting into time spans and discuss techniques like fuzzy logic and regression models. ANNs are also discussed for system identification to determine system parameters from input-output data and for control system applications like predictive control and feedback linearization. The document concludes with ANN approaches for pattern recognition tasks involving classification, clustering, and regression.
This document discusses forecasting daily runoff using artificial neural networks (ANN). It presents research applying ANN models to the Gunjwani watershed in India. The document describes developing ANN and multiple linear regression models using rainfall, runoff, evaporation, humidity and temperature data from the watershed. It evaluates the models based on statistical performance criteria like mean square error, mean absolute error and correlation coefficient. The results show that the multi-layer perceptron ANN model provided a better forecast of runoff compared to the multiple linear regression models.
Pattern recognition system based on support vector machinesAlexander Decker
This document describes a study that uses support vector machines (SVM) to develop quantitative structure-activity relationship (QSAR) models for predicting the anti-HIV activity of 1,3,4-oxadiazole substituted naphthyridine derivatives based on their molecular descriptors. The SVM model achieved a cross-validation R2 value of 0.90 and RMSE of 0.145, outperforming artificial neural network and multiple linear regression models. An external validation on an independent test set found the SVM model had an R value of 0.96 and RMSE of 0.166, demonstrating good predictive ability.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3푈95 ≤ 푀푃퐸푉. This method can provide an
algorithm reference for the design of automatic calibration equipment.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3𝑈95 ≤ 𝑀𝑃𝐸𝑉. This method can provide an
algorithm reference for the design of automatic calibration equipment.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScscpconf
1) The document presents a new technique for fault detection and isolation that uses neural networks to generate models of normal and faulty system behaviors. A decision tree is then used to evaluate residuals and isolate faults.
2) The technique is demonstrated on a benchmark process for an electro-pneumatic valve actuator. Neural networks are used to generate models of the actuator's normal and 19 possible faulty behaviors.
3) A decision tree structure is proposed to simplify online fault diagnosis by only evaluating the most significant residuals needed at each step to isolate faults. This reduces computational effort compared to evaluating all residuals.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScsitconf
This paper presents a new idea for fault detection and isolation (FDI) technique which is
applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree
architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed
threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by
evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line
diagnosis. An application example is presented to illustrate and confirm the effectiveness and
the accuracy of the proposed approach.
At present, the research on fault detection and diagnosis technology is very significant to improve the reliability of the equipment, which can greatly improve the safety and efficiency of the equipment. This paper proposes a new fault detection and diagnosis means based on the FOA-LSSVM algorithm. Experimental results demonstrate that the algorithm is effective for the detection and diagnosis of analog circuit faults. In addition, the model also demonstrate good generalization ability.
Flood Prediction Model using Artificial Neural NetworkEditor IJCATR
This paper presents a Flood Prediction Model (FPM) to predict flood in rivers using Artificial Neural Network (ANN)
approach. This model predicts river water level from rainfall and present river water level data. Though numbers of factors are
responsible for changes in water level, only two of them are considered. Flood prediction problem is a non-linear problem and to solve
this nonlinear problem, ANN approach is used. Multi Linear Perceptron (MLP) based ANN’s Feed Forward (FF) and Back
Propagation (BP) algorithm is used to predict flood. Statistical analysis shows that data fit well in the model. We present our
simulation results for the predicted water level compared to the actual water level. Results show that our model successfully predicts
the flood water level 24 hours ahead of time.
IRJET- AI to Analyze and Extract Data to Gain Insights About the Spread o...IRJET Journal
This document describes a system that uses sensors and machine learning to analyze air quality and predict pollution levels. The system collects data from sensors that measure gases like carbon monoxide and nitrogen dioxide. This data is processed using machine learning algorithms to analyze current pollution levels and predict future levels over the next 24 hours. The system aims to provide reliable air quality information to help governments and organizations address pollution issues. It offers a low-cost and mobile alternative to existing stationary sensor systems.
IRJET- Wavelet Decomposition along with ANN used for Fault DetectionIRJET Journal
This document presents a technique for fault detection in systems using wavelet decomposition and artificial neural networks (ANN). It discusses using discrete wavelet transform (DWT) to extract features from signals for fault detection, which overcomes limitations of other techniques like fast Fourier transform (FFT) for non-stationary signals. The DWT decomposes signals into different frequency bands to analyze energy content, which is then fed as input to an ANN for fault classification and detection. The technique aims to provide earlier detection of faults than conventional methods through feature extraction and ANN pattern recognition of faults.
The document discusses using machine learning techniques like artificial neural networks, linear regression, and support vector regression to forecast daily oil production from an oil field. It analyzes production data from the Volve oil field in Norway using these three methods. All methods showed potential for production forecasting, though artificial neural networks performed best for one well. The performance of algorithms depends on the specific case, so each must be evaluated individually to select the best technique.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3푈95 ≤ 푀푃퐸푉. This method can provide an
algorithm reference for the design of automatic calibration equipment.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3푈95 ≤ 푀푃퐸푉. This method can provide an
algorithm reference for the design of automatic calibration equipment.
Este documento presenta la metodología para realizar estudios de suelos en campo. Describe las cuatro etapas clave del proceso: 1) búsqueda y recopilación de antecedentes previos, 2) exploración inicial rápida, 3) estudio analítico que incluye fases de campo, laboratorio y gabinete, y 4) síntesis final para emitir un diagnóstico y recomendaciones. Explica cada etapa en detalle, incluyendo aspectos como la identificación de unidades paisajísticas y el estudio morfol
Este documento modifica el Decreto Legislativo No 1278 que aprueba la Ley de Gestión Integral de Residuos Sólidos para contemplar disposiciones referidas al manejo de residuos sólidos en situaciones de emergencia y la prestación del servicio durante la vigencia del estado de emergencia sanitaria por el COVID-19. Se realizan cambios a 14 artículos para establecer normas sobre el transporte de material de descarte, el régimen especial de gestión de residuos priorizados, las funciones del Organismo de Evaluación y Fiscalización Ambiental y
The document describes an intelligent fault diagnosis system for reciprocating pumps that uses pressure and flow signals as inputs. It consists of hardware for data acquisition and a software system for signal processing, feature extraction, and fault diagnosis using wavelet neural networks. The system was able to accurately diagnose three main fault types - seal ring faults, valve damage, and spring faults - based on differences observed in the pressure curves. Testing on over 12 samples of each fault type achieved a correct diagnosis rate of over 94%. The system provides a fast and effective means of remotely monitoring reciprocating pumps and identifying faults.
Wireless sensor network (WSN) are powered by batteries to perform various sensing tasks in a given
environment. The measurements made by the sensors are sometimes unreliable and erroneous due to noise
in the sensor or hardware failure. For a large scale WSN to be economically feasible, it is important to
ensure that the faulty node does not affect the overall behaviour of the system. In this paper a binary faulttolerant
event detection technique has been proposed for the non-symmetric errors and its performance has
been analysed. Theoretical analysis and simulation show that almost 97 percent of faults can be corrected
even when 10 percent sensor nodes are faulty.
Neural Network-Based Actuator Fault Diagnosis for a Non-Linear Multi-Tank SystemISA Interchange
The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H1 framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks.
The document discusses various applications of artificial neural networks (ANNs) including electrical load forecasting, system identification, control systems, and pattern recognition. It provides details on ANN approaches for each application area. For electrical load forecasting, ANNs can be used to classify forecasting into time spans and discuss techniques like fuzzy logic and regression models. ANNs are also discussed for system identification to determine system parameters from input-output data and for control system applications like predictive control and feedback linearization. The document concludes with ANN approaches for pattern recognition tasks involving classification, clustering, and regression.
This document discusses forecasting daily runoff using artificial neural networks (ANN). It presents research applying ANN models to the Gunjwani watershed in India. The document describes developing ANN and multiple linear regression models using rainfall, runoff, evaporation, humidity and temperature data from the watershed. It evaluates the models based on statistical performance criteria like mean square error, mean absolute error and correlation coefficient. The results show that the multi-layer perceptron ANN model provided a better forecast of runoff compared to the multiple linear regression models.
Pattern recognition system based on support vector machinesAlexander Decker
This document describes a study that uses support vector machines (SVM) to develop quantitative structure-activity relationship (QSAR) models for predicting the anti-HIV activity of 1,3,4-oxadiazole substituted naphthyridine derivatives based on their molecular descriptors. The SVM model achieved a cross-validation R2 value of 0.90 and RMSE of 0.145, outperforming artificial neural network and multiple linear regression models. An external validation on an independent test set found the SVM model had an R value of 0.96 and RMSE of 0.166, demonstrating good predictive ability.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3푈95 ≤ 푀푃퐸푉. This method can provide an
algorithm reference for the design of automatic calibration equipment.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3𝑈95 ≤ 𝑀𝑃𝐸𝑉. This method can provide an
algorithm reference for the design of automatic calibration equipment.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScscpconf
1) The document presents a new technique for fault detection and isolation that uses neural networks to generate models of normal and faulty system behaviors. A decision tree is then used to evaluate residuals and isolate faults.
2) The technique is demonstrated on a benchmark process for an electro-pneumatic valve actuator. Neural networks are used to generate models of the actuator's normal and 19 possible faulty behaviors.
3) A decision tree structure is proposed to simplify online fault diagnosis by only evaluating the most significant residuals needed at each step to isolate faults. This reduces computational effort compared to evaluating all residuals.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScsitconf
This paper presents a new idea for fault detection and isolation (FDI) technique which is
applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree
architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed
threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by
evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line
diagnosis. An application example is presented to illustrate and confirm the effectiveness and
the accuracy of the proposed approach.
At present, the research on fault detection and diagnosis technology is very significant to improve the reliability of the equipment, which can greatly improve the safety and efficiency of the equipment. This paper proposes a new fault detection and diagnosis means based on the FOA-LSSVM algorithm. Experimental results demonstrate that the algorithm is effective for the detection and diagnosis of analog circuit faults. In addition, the model also demonstrate good generalization ability.
Flood Prediction Model using Artificial Neural NetworkEditor IJCATR
This paper presents a Flood Prediction Model (FPM) to predict flood in rivers using Artificial Neural Network (ANN)
approach. This model predicts river water level from rainfall and present river water level data. Though numbers of factors are
responsible for changes in water level, only two of them are considered. Flood prediction problem is a non-linear problem and to solve
this nonlinear problem, ANN approach is used. Multi Linear Perceptron (MLP) based ANN’s Feed Forward (FF) and Back
Propagation (BP) algorithm is used to predict flood. Statistical analysis shows that data fit well in the model. We present our
simulation results for the predicted water level compared to the actual water level. Results show that our model successfully predicts
the flood water level 24 hours ahead of time.
IRJET- AI to Analyze and Extract Data to Gain Insights About the Spread o...IRJET Journal
This document describes a system that uses sensors and machine learning to analyze air quality and predict pollution levels. The system collects data from sensors that measure gases like carbon monoxide and nitrogen dioxide. This data is processed using machine learning algorithms to analyze current pollution levels and predict future levels over the next 24 hours. The system aims to provide reliable air quality information to help governments and organizations address pollution issues. It offers a low-cost and mobile alternative to existing stationary sensor systems.
IRJET- Wavelet Decomposition along with ANN used for Fault DetectionIRJET Journal
This document presents a technique for fault detection in systems using wavelet decomposition and artificial neural networks (ANN). It discusses using discrete wavelet transform (DWT) to extract features from signals for fault detection, which overcomes limitations of other techniques like fast Fourier transform (FFT) for non-stationary signals. The DWT decomposes signals into different frequency bands to analyze energy content, which is then fed as input to an ANN for fault classification and detection. The technique aims to provide earlier detection of faults than conventional methods through feature extraction and ANN pattern recognition of faults.
The document discusses using machine learning techniques like artificial neural networks, linear regression, and support vector regression to forecast daily oil production from an oil field. It analyzes production data from the Volve oil field in Norway using these three methods. All methods showed potential for production forecasting, though artificial neural networks performed best for one well. The performance of algorithms depends on the specific case, so each must be evaluated individually to select the best technique.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A NEW METHOD OF SMALL-SIGNAL CALIBRATION BASED ON KALMAN FILTERijcseit
The basic principle of Kalman filter (KF) is introduced in this paper, based on which, it presents a new
method for high precision measurement of small-signal instead of the unreal direct one. We have designed a
method of multi-meter information infusion. With this method, we filter the measured value of a type of
special equipment and extract the optimal estimate for true value. Experimental results show that this
method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3푈95 ≤ 푀푃퐸푉. This method can provide an
algorithm reference for the design of automatic calibration equipment.
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method can effectively eliminate the random error of the measurement process. The optimal estimate error
meets the basic requirements of conformity assessment, 3푈95 ≤ 푀푃퐸푉. This method can provide an
algorithm reference for the design of automatic calibration equipment.
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TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
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### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
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1. Ana Luna
Profesor e investigador del CIUP
ae.lunaa@up.edu.pe
Álvaro Talavera
Profesor e investigador del CIUP
ag.talaveral@up.edu.pe
Luis Cano
Centro de Investigación - CIUP
ag.talaveral@up.edu.pe
Documento de Discusión
CIUP
DD1705
2017
Uso de sensores
electroquímicos de bajo
costo para el monitoreo
de la calidad del aire
en el distrito de
San Isidro - Lima - Perú
2. Las opiniones expresadas en este documento son de exclusiva responsabilidad del autor y
no expresan necesariamente aquellas del Centro de Investigación de la Universidad del
Pacífico o de la Universidad misma.
The opinions expressed here in are those of the authors and do not necessarily reflect
those of the Research Center of the Universidad del Pacifico or the University itself.
3. Uso de sensores electroquímicos de bajo costo
para el monitoreo de la calidad del aire en el
distrito de San Isidro - Lima - Perú
Ana Luna1
, Álvaro Talavera2
, Luis Cano3
Departamento de Ingeniería, Universidad del Pacífico
Lima, Perú
1
ae.lunaa@up.edu.pe, 2
ag.talaveral@up.edu.pe, 3
l.canovasquez@up.edu.pe
Abstract— En este trabajo se muestran los resultados preliminares del monitoreo y de la
estimación de contaminantes atmosféricos en un punto estratégico dentro del distrito de San
Isidro, Lima - Perú. Se emplearon sensores electroquímicos de bajo costo, portátiles,
inalámbricos y geo-localizables para la captura de los niveles de contaminación que permiten
obtener en tiempo real valores confiables que podrían emplearse no sólo para cuantificar la
exposición de contaminación atmosférica sino también para prevención y control, e incluso para
fines legislativos.
Para la previsión de los niveles de CO2 y de SO2 se aplicaron algoritmos de inteligencia
computacional, específicamente redes neuronales.
I. INTRODUCCIÓN
En las últimas décadas han sido publicados varios trabajos de investigación en donde se
muestra la correlación entre la disminución de la calidad de vida y el aumento de las
enfermedades respiratorias y cardiovasculares con la contaminación atmosférica [1]-[2]-[3].
Según el reporte del año 2014 de la Organización Mundial de la Salud (OMS), Lima es una
de las ciudades que tiene al aire más contaminado en América Latina. Por tal motivo, el
monitoreo de la calidad del aire debería ser una de las prioridades de nuestra sociedad actual.
El último reporte del Organismo de Evaluación y Fiscalización Ambiental (OEFA) elaborado
en el año 2015, muestra que sólo el 29% de las municipalidades existentes en Lima
Metropolitana supervisa y fiscaliza la contaminación del aire en su jurisdicción.
En la actualidad existen diez estaciones fijas de monitoreo de calidad del aire en Lima
Metropolitana y el Callao para evaluar permanentemente la contaminación atmosférica. El
4. Servicio Nacional de Meteorología e Hidrología del Perú (SENAMHI) es el ente que informa
mensualmente los resultados de dichas estaciones automáticas.
La adquisición de los niveles de contaminación, usualmente se llevan a cabo a través
de estaciones estáticas de monitoreo que poseen instrumentos de medición muy precisos y
que requieren de un mantenimiento y calibración permanente y cuyo costo supera
ampliamente las decenas de miles de dólares cada uno [4]. En consecuencia, esto trae
aparejado una gran limitación en el número de redes de monitoreo posibles.
Sin embargo el creciente avance de la tecnología ha permitido el desarrollo de una
nueva generación de sensores, económicos y pequeños.
En este trabajo se presenta el diseño, equipamiento y puesta en marcha de una estación de
monitoreo de contaminación atmosférica portátil y de bajo costo en el distrito de San Isidro,
Lima, Perú. Se detalla además cómo fueron procesados los valores adquiridos y se muestran
las gráficas de los valores de los contaminantes en función de la franja horaria registrada. A
continuación, se esbozan algunas observaciones, se emplea inteligencia artificial,
específicamente redes neuronales para la predicción y validación de los contaminantes
críticos medidos y se analizan los resultados obtenidos. Finalmente, en la última sección, se
presentan las conclusiones.
II. Teoría
Las Redes Neuronales son un campo dentro de la Inteligencia Artificial que se inspira en las
conexiones entre neuronas del cerebro humano, intentando crear modelos artificiales que
emplean técnicas algorítmicas convencionales para hallar la solución de un problema en
particular. Las redes neuronales son, en síntesis, unidades de procesamiento que
intercambian datos o información y que se utilizan para reconocer patrones, como por
ejemplo, tendencias financieras. Tienen la capacidad de aprender y mejorar su
funcionamiento [5].
El modelo de neurona artificial consta básicamente de un conjunto de entradas de un número
determinado de componentes, en forma de vector, que genera una única salida. Existe
además, un conjunto de pesos sinápticos que representan la interacción entre las neuronas
5. pre sináptica y post sináptica. La regla de propagación brinda el potencial post sináptico y la
función de activación proporciona el estado de activación de la neurona en función del estado
anterior y del valor post sináptico. Finalmente, la función de salida se obtiene a partir del
estado de activación.
Una red neuronal artificial (RNA) está compuesta por nodos (elementos del proceso) y
conexiones. Cada nodo posee una función de transferencia. Las entradas son externas y las
salidas se obtienen a partir de las conexiones salientes de la RNA.
Una RNA posee una determinada estructura que permite visualizar cómo son las conexiones
sinápticas de la red. Las neuronas suelen agruparse en capas, una de entrada, la cual recibe
los datos del entorno; otra de salida, que proporciona la respuesta de la red a los estímulos de
la entrada y capas ocultas que forman parte del procesamiento interno de la red y no tienen
contacto directo con el entorno exterior. (Fig. 1).
Fig. 1: Ejemplo de una red neuronal totalmente conectada.
El proceso de aprendizaje de una RNA consiste en actualizar los pesos a través de algoritmos
de aprendizaje, que es un procedimiento numérico de ajuste de pesos con el fin de minimizar
el error cometido.
6. El algoritmo de entrenamiento empleado en este trabajo fue el que se conoce con el nombre
de Levenberg-Marquardt, el cual se aplica principalmente a redes neuronales con varias capas
debido a que la velocidad de convergencia es muy rápida, minimizando velozmente la
función de error.
Finalizado el proceso de entrenamiento, se debe comprobar si la red neuronal puede resolver
el problema que se desea solucionar; es decir, se busca validar el resultado obtenido. Para
ello, se requiere de otro conjunto de datos, denominado conjunto de validación o testeo. Cada
ejemplo del conjunto de evaluación contiene los valores de las variables de entrada, con su
correspondiente solución tomada; pero la solución no se le otorga a la red neuronal. Luego
se compara la solución estimada por la red con la real para finalizar el proceso de validación.
Para evaluar el desempeño de la red se usó como indicador el error porcentual absoluto medio
(MAPE o Mean Absolute Percentage Error), que mide el tamaño del error (absoluto) en
términos porcentuales. La ecuación (1) fue empleada para el cálculo del MAPE:
𝑀𝐴𝑃𝐸 =
1
𝑁
∑
|𝑥𝑖−𝑥̅𝑖|
|𝑥𝑖|
𝑁
𝑖=1 (1)
Siendo N el número total de valores; 𝑥𝑖 y 𝑥̅𝑖 los valores reales y previstos por la red
respectivamente.
También se empleó la raíz del error cuadrático medio o RMSE (Root Mean Squared Error),
la cual es una medida de desempeño cuantitativa utilizada para evaluar métodos de
pronóstico, es un estimador que mide el promedio de los errores al cuadrado, es decir, la
diferencia entre el estimador y lo que se estima, evaluando la calidad de un conjunto de
predicciones en cuanto a su variación y el grado de sesgo. La fórmula de cálculo del RMSE
se muestra en la ecuación (2):
𝑅𝑀𝑆𝐸 = √
1
𝑁
∑ (𝑥𝑖 − 𝑥̅𝑖)2
𝑁
𝑖=1 (2)
III. MATERIALES Y MÉTODOS
Las mediciones se llevaron a cabo en las instalaciones de la Municipalidad de San Isidro
(Augusto Tamayo 180) Lima, Perú (Fig. 2). Las coordenadas geográficas en el Distrito de
7. San Isidro donde se ubicaron los sensores son: Latitud: -12.1167, Longitud: -77.05 y Altura
(m.s.n.m): 109 Metros (Base: Parque el Olivar).
El distrito de San Isidro tiene una extensión de 9.78 km2
y una población de
aproximadamente 58.056 habitantes.
Fig. 2: Mapa donde se observa el punto en el cual fueron instalados los sensores de contaminación atmosférica y sonora.
A III. Sensores electroquímicos de bajo costo
Los sensores outdoor empleados para la medición de calidad de aire son de la marca αSense
(Essex – Reino Unido) [6]-[7]-[8]. Son inalámbricos, alimentados por una batería y un panel
solar. Poseen conexión wifi (interfaz celular GSM) además de ser geo-localizables (GPS
incorporado). Los datos se envían a la nube y pueden leerse desde cualquier PC con conexión
a internet (M2M). Son sensores electroquímicos que poseen 4 electrodos diseñados para
medir los niveles del gas en nmol/mol. El primero de los electrodos se denomina “Electrodo
de Trabajo”, el segundo se llama “Electrodo de Referencia”, el tercero es el “Electrodo
Contador” y el último, el “Electrodo Auxiliar” se utiliza para corregir el cero ante los
cambios de corriente. Éste no está en contacto con el gas; por lo tanto, proporciona
información útil sobre el efecto de la temperatura ambiental. Cada sensor brinda dos señales,
la segunda de ellas es la señal de fondo del electrodo que debe ser restada a los valores
suministrados por el sensor del Electrodo de Trabajo.
Los contaminantes atmosféricos registrados fueron: CO2, VOC (alcoholes, aldehídos,
hidrocarburos alifáticos, aminas, hidrocarburos aromáticos, CH4, LPG, ketones y ácidos
orgánicos), CO, SO2, Ozono y Dióxido de Nitrógeno (O3 + NO2); y las variables
8. meteorológicas adquiridas en forma simultánea fueron la temperatura ambiental, la humedad
relativa y la presión barométrica (Fig. 3).
Fig. 3: Sensores de calidad de aire (CO2, VOC, SO2, O3 + NO2, CO) y meteorológicos: temperatura ambiental, humedad
relativa y presión barométrica.
B III. Mediciones de campo
Los datos almacenados para los diferentes contaminantes atmosféricos fueron registrados
desde las 8:00 am hasta las 12:30 pm durante los días miércoles 5, jueves 6, viernes 7, lunes
10, martes 11 y miércoles 12 de abril de 2017. Se registra 1 dato por minuto, dando como
máximo un total de 270 valores registrados diariamente.
La información adquirida para los contaminantes de medición indirecta, CO, SO2 y O3 + NO2,
debe procesarse. Para ello, se generaron varios códigos en lenguaje de programación Python;
el primero de ellos realiza la conversión de los datos registrados en unidades de voltaje
y proporcionados por el dashboard Valarm a valores en partes por millón (ppm) usando
la ecuación (3) que se muestra a continuación,
𝑉𝑎𝑙𝑜𝑟 (𝑝𝑝𝑚) =
(𝑊𝐸−𝑊𝐸0)−(𝐴𝐸−𝐴𝐸0)
𝑆𝑒𝑛𝑠𝑖𝑏𝑖𝑙𝑖𝑑𝑎𝑑
(3)
9. donde WE son los datos registrados por el “Electrodo de Trabajo”, AE son los valores del
“Electrodo Auxiliar” y WEo y AEo corrigen las señales de fondo de los respectivos
electrodos. El valor de la sensibilidad para cada contaminante se obtiene a partir de la
siguiente fórmula (ec. 4):
𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑑𝑎𝑑 = 𝑊𝐸𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 (
𝑛𝐴
𝑝𝑝𝑚
) ∗ 𝐺𝑎𝑛𝑎𝑛𝑐𝑖𝑎 (
𝑚𝑉
𝑛𝐴
) (4)
siendo los valores de sus factores, datos aportados por el fabricante de los sensores.
La siguiente etapa consiste en el tratamiento de los datos, que ahora están expresados
en unidades de ppm. El nuevo programa elimina tanto los valores negativos como los
ceros y también aquellos valores que se apartan tres veces del valor de la desviación estándar
correspondiente a la media de cada uno de los contaminantes respectivamente. Los valores
descartados son reemplazados por aquellos obtenidos mediante una interpolación lineal entre
el dato previo y posterior de dicho valor registrado. En el caso de los primeros y últimos
valores que han sido eliminados, se los completa con el dato del promedio de la adquisición.
Finalmente, se procesan todos los datos obtenidos y tratados hasta el momento con un tercer
programa que gráfica los niveles de contaminación en función del tiempo, además inserta en
cada figura el valor de las variables meteorológicas (presión, humedad y temperatura) y el
Valor del Estándar de Calidad Ambiental (ECA) para cada contaminante.
IV. RESULTADOS
Los valores de los niveles de concentración de los contaminantes medidos se muestran, a
modo de ejemplo, sólo para uno de los días de adquisición en la Fig. 4. El resto de los días
medidos son cualitativamente análogos y los respectivos resultados de sus valores medios se
resumen en la Fig. 5.
10. Fig 4: Series temporales de los niveles de contaminantes medidos en la franja horaria de 8:00 am a 12:30 pm el día jueves
6 de abril de 2017
En la Tabla I se informan las variables meteorológicas de cada uno de los días en los cuales
se realizaron las mediciones.
Tabla I: Valores medidos de la temperatura, humedad relativa y presión en los días y horas en los que se registraron los
niveles de concentración de los contaminantes atmosféricos.
En la Fig. 5 se observan los niveles medios de contaminantes atmosféricos registrados en la
franja horaria de 8:00 am a 12:30 pm durante los días especificados en las gráficas.
Variables
meteorológicas
5 de abril de
2017
6 de abril de
2017
7 de abril de
2017
10 de abril de
2017
11 de abril de
2017
12 de abril de
2017
Temperatura 30°C 28°C 32°C 32°C 29°C 32°C
Humedad relativa 62% 65% 60% 60% 64% 59%
Presión 999hPa 1000.7 hPa 999.8hPa 1000hPa 1000hPa 999hPa
11. Fig 5: Valores medios de niveles de contaminantes atmosféricos diarios (CO2, CO, NO2, O3, SO2 y VOC).
Franja horaria de registro: 8:00 am a 12:30 pm. Año 2017.
Durante las mediciones realizadas en el distrito de San Isidro se observa que el valor
promedio del dióxido de carbono excede en casi un 32% los valores máximos dentro de los
estándares de calidad de aire para dicho contaminante (Figs. 4 y 5). Existe una relación
directa entre el calentamiento global o cambio climático y el aumento de las emisiones de
gases de efecto invernadero provocado por las sociedades humanas tanto industrializadas
como en desarrollo. Los últimos estudios han demostrado que la concentración de CO2 en la
atmósfera está aumentando de forma constante debido al uso de carburantes fósiles como
fuente de energía [10]. Esto provoca cambios importantes sobre los seres vivos y los
ecosistemas, como así también consecuencias económicas, sociales y ambientales de gran
magnitud (la temperatura media de la superficie terrestre se ha incrementado, aumentaron los
fenómenos de erosión y salinización en áreas costeras, se intensificaron las enfermedades
infecciosas, etc.).
Tanto el dióxido de carbono como el dióxido de azufre son contaminantes gaseosos primarios
y se emiten directamente a la atmósfera. El SO2 registra también valores muy por encima de
12. los estándares de calidad (Figs. 4 y 5), el promedio de los valores medidos supera en más de
un 270% el valor informado por el ECA.
Son variadas las fuentes que producen estos compuestos químicos, pero las principales
fuentes artificiales son la quema de combustible fósil y la proveniente de fábricas, industrias,
centrales eléctricas y los automóviles.
Cuando el dióxido de azufre está en la atmósfera reacciona con la humedad y forma aerosoles
de ácido sulfúrico y sulfuroso que luego forman parte de la llamada lluvia ácida y sus
componentes son altamente nocivos para la vegetación.
El SO2 también ataca a los materiales de construcción que suelen estar formados por
minerales carbonatados. Además, el dióxido de azufre forma sulfatos, y la exposición a éstos
como a los ácidos derivados del SO2, es de extremo riesgo para la salud debido a que ingresan
directamente al sistema circulatorio humano a través de las vías respiratorias, siendo es muy
irritante para los pulmones.
El exceso de SO2 trae aparejado consecuencias graves a nivel salud, como por ejemplo, el
aumento de la cantidad de hospitalizaciones debido a enfermedades pulmonares obstructivas
crónicas (EPOCs) tal como se registra en la referencia [10].
En el artículo [11] también se demuestra el aumento de la incidencia de rinitis alérgica,
bronquitis y asma, en niños como consecuencia de la contaminación por SO2. Más aún, en
el trabajo [12] se analizan diferentes efectos de las exposiciones a largo plazo a SO2, entre
otros, sobre todo en adultos jóvenes que son gravemente afectados por el asma. Sin duda,
lo que respiramos afecta nuestra salud, y puede aumentar la morbilidad y mortalidad en
personas mayores y niños; por lo que debería ser prioritario reducir la contaminación del aire
en pos de una mejora de la salud.
A IV. Redes neuronales
Este modelo de inteligencia computacional fue usado para pronosticar los niveles de
concentración de los dos contaminantes atmosféricos que sobrepasan los valores de ECA
establecidos, el CO2 y el SO2 (Figs. 4 y 5).
13. Para la predicción del dióxido de carbono se construyó, como primera medida la red
neuronal. La topología óptima hallada para la red fue de 14 neuronas en la primera capa
oculta y de 3 neuronas en la segunda capa oculta (Fig. 6). Éste resultado se obtuvo mediante
un algoritmo de búsqueda (en el software RapidMiner) en donde se probaron todas las
combinaciones posibles entre neuronas en una estructura de 2 capas ocultas y un máximo de
20 neuronas en cada capa. Los datos de entrenamiento de la red empleados fueron las
concentraciones de los contaminantes atmosféricos medidos durante los 5 días anteriores al
día de predicción, estos datos fueron divididos en 3 conjuntos, 70% fueron usados para el
entrenamiento, 20% fueron considerados para la validación y el 10% restante fueron tenidos
en cuenta para el teste. El algoritmo de entrenamiento fue Levenberg-Marquardt. La
convergencia se dio en la iteración número 14, en la que ya no hubo variación significativa
en el error del entrenamiento de la red entre el paso previo y el posterior a esta última
iteración.
Fig. 6: Arquitectura de la red neuronal empleada para la predicción del CO2. Se puede apreciar que la convergencia se dio
en la iteración número 14, y ya no hubo variación significativa entre la performance obtenida en una iteración previa y la
iteración inmediatamente posterior.
En la Fig. 7 se observa la gráfica de los valores medidos y de la predicción de los niveles de
concentración del CO2 para el día miércoles 12 de abril de 2017. Éstos últimos se obtuvieron
a partir del entrenamiento previo del modelo con la incorporación de los valores medidos del
resto de los contaminantes salvo el CO2. Los datos de temperatura, humedad relativa y
14. presión atmosférica también fueron considerados. El RMSE obtenido fue de 20 ppm y el
MAPE alcanzó casi el 3%.
08:30 09:00 09:30 10:00 10:30 11:00 11:30 12:00 12:30
480
500
520
540
560
580
600
620
Prediccion del CO2
Valores medidos del CO2
CO
2
(ppm)
Franja horaria medida (Miércoles 12 de abril de 2017)
Fig. 7: Pronóstico del dióxido de carbono.
Para encontrar la arquitectura óptima de la red neuronal empleada para la predicción de los
niveles de concentración del SO2 se usó nuevamente el algoritmo de búsqueda probando
todas las combinaciones posibles entre neuronas en una estructura de 2 capas ocultas y un
máximo de 20 neuronas en cada capa. El resultado fue una red compuesta por 8 neuronas en
la primera capa oculta y 5 neuronas en la segunda capa oculta (Fig. 8).
Fig. 8: Arquitectura de la red neuronal empleada para la predicción del SO2.
15. Los datos de entrenamiento de la red fueron las concentraciones de los contaminantes de 5
días previos: miércoles 5, jueves 6, viernes 7, lunes 10 y martes 11 de abril de 2017, y se
predijo los niveles de ppm (partes por millón) del SO2 del día miércoles 12 de abril de 2017.
Los valores medidos con los sensores de bajo costo fueron divididos en 3 conjuntos, el 70%
fue empleado para el entrenamiento, el 20% se usó para validación y el 10% restante para el
teste. El algoritmo de entrenamiento elegido, como se explicó en la sección II fue el de
Levenberg-Marquardt. La convergencia se logró en la iteración número 12.
En la Tabla II se observan los resultados del MAPE y del RMSE obtenidos para las
predicciones de los niveles de concentración del dióxido de azufre del día miércoles 12 de
abril de 2017 entre las 8:30 am y 12:30 pm. La diferencia entre cada una de ellas son los
valores de entrada tomados en cuenta por la red neuronal previamente entrenada, los cuales
se marcan con una cruz (X) en la Tabla II. En todos los casos las variables meteorológicas:
temperatura, humedad relativa y presión atmosférica fueron consideradas en el
entrenamiento.
Variables Predicción 1
del SO2
Predicción 2
del SO2
Predicción 3
del SO2
Predicción 4
del SO2
Predicción 5
del SO2
Predicción 6
del SO2
SO2
CO X X X X X
CO2 X X X X X
NO2 X X X X X
O3 X X X X X
VOC X X X X X
MAPE 45% 49% 51% 51% 29% 54%
RSME (ppm) 0.30 0.36 0.43 0.36 0.27 0.42
Tabla II: Resultados del MAPE y del RSM para cada predicción del SO2.
Como se puede observar en la Tabla II, la predicción del dióxido de azufre que arrojó los
valores de MAPE y de RSME más bajos, fue aquella en la cual no se contempló el O3 (Fig.
9).
El coeficiente de correlación lineal entre la predicción y los valores medidos de SO2 fue de
0.89.
16. 08:30 09:00 09:30 10:00 10:30 11:00 11:30 12:00 12:30
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
Predicción del SO2
(ppm)
Valores medidos del SO2
(ppm)
SO
2
(ppm)
Franja horaria (Miércoles 12 de abril de 2017)
Fig. 9: Pronóstico del dióxido de azufre.
Por lo tanto, como primera aproximación, el empleo de inteligencia artificial teniendo
sensores atmosféricos de bajo costo es una herramienta potencial para pronosticar niveles de
concentración confiables de contaminantes.
V.CONCLUSIONES
Los experimentos de campo son esenciales para poder comprender ciertos
aspectos del tiempo y el clima de una región. En este working paper se mostraron y
analizaron los datos adquiridos de diversos contaminantes atmosféricos en el distrito de San
Isidro.
Los sensores de bajo costo empleados en este trabajo tienen un gran potencial para ser usados
como complemento de las escasas estaciones estáticas de monitoreo; aumentando, de esta
manera, la resolución espacial de las mediciones. Si bien las mediciones de los sensores
electroquímicos no necesariamente poseen la precisión o exactitud de los instrumentos
tradicionales in situ, son lo suficientemente versátiles y económicos para usarlos en la
adquisición de valores de contaminación atmosférica y obtener información preliminar
sobre las emisiones provenientes de fuentes fijas y móviles e incluso podrían identificarse en
17. tiempo real los puntos críticos de contaminación atmosférica, es decir, las áreas con deterioro
de la calidad del aire y proponer medidas para revertir esta situación ambiental, o bien mitigar
los episodios críticos de contaminación atmosférica sobre los asentamientos poblacionales.
RECONOCIMIENTOS
Quisiera agradecer a la Universidad del Pacífico y en particular al Departamento de Ingeniería por la
compra de los sensores de contaminación atmosférica y sonora; y de las tarjetas SIM usadas para la
transmisión de datos. También quisiera transmitir mi gratitud a las Sras. Jimena Sánchez Velarde,
Directora de datosabiertosperu.com y Pamela Olenka Peña, Gerente de Sostenibilidad de la Municipalidad de
San Isidro, quienes autorizaron el uso de las instalaciones dentro de la Municipalidad permitiendo la
realización de este trabajo de investigación.
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