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SRI INDU INSTITUTE OF ENGINEERING AND
TECHNOLOGY
DEPARTMENT OF ELETRONICS AND COMMUNICATION ENGINEERING
BATCH:2020-2024
PREPARED BY:
M.BHANU PRAKASH REDDY
(20X31A0463)
V. VEDANIDHI (20X31A04C3)
N. SAMUEL JOSHUA (20X31A0474)
V. SANDEEP (20X31A04B7)
M. VIGNESHWARA CHARY (20X31A0466)
PREDICTING RIVER WATER QUALITY
PARAMETERS
USING
SUPERVISED
MACHINE LEARNING
TECHNIQUES UNDER THE GUIDANCE OF:
Dr. K. SRINIVASA REDDY
professor
CONTENTS
1
ABSTRACT
2
INTRODUCTION
3
PREDICTING WATER QUALITY USING
MACHINE LEARNING ALGORITHMS
4
5
6
7
8
DATA VISUALIZATION
9
ANALYSIS OF ALGORITHM
10
WORKING OF ALGORITHM
11
WATER QUALITY PREDICTION
METHODOLOGY
12
DESIGN SPECIFICATION
13
CONCLUSION
14
PARAMETERS OF WATER
QUALITY
FINDINGS AND PROPOSALS
DATA SET
ANALYSIS OF FUTURE VARIABLES
FUTURE SCOPE
ABSTRACT
 The quality of our water is essential to human health and to our ecosystem.
 Pollution in water can cause humans to become ill and wildlife to die.
 In this study, five supervised machine learning models were applied to a river water quality
dataset that were collected from a river.
 ML Models are Multiple linear regression, Random Forest, Decision Tree, support vector
machine, Extreme Gradient Boosting.
 Four popular river quality parameters were predicted, they are Dissolved Sodium, Dissolved
Nitrate, Gran Alkalinity and Electrical Conductivity.
 The best performing algorithm was found to be Decision Tree when predicting all parameters
with an R-Squared value of between 87% and 98%.
 The results found in this study can help to support the monitoring of river water quality in a fast
and inexpensive way and improve the existing testing system in place.
INTRODUCTION
 Rivers have become one of the most used natural water sources globally.
 This is due to the accessibility and the location of cities being built close to riverbanks. Water has
also become increasingly popular for recreational activities since the Covid-19 lockdown' with more
and more people entering the coastal waters, rivers and lakes that surround us.
 This means that more people are being exposed to waters that may be polluted and
unsafe for human use.
 According to a recent survey of World Health Organization (WHO), more than 2.2 billion people in
India face problems due to unsafe drinking water and 21% of the diseases are related
to impure water.
PREDICTING WATER QUALITY USING MACHINE
LEARNING ALGORITHMS
 Machine learning (ML) is a branch of artificial intelligence and a rapidly growing technical field
that lies between computer science and statistics.
 Machine Learning works by mimicking human behaviour and uses data and algorithms to improve
its accuracy.
 ML is widely used within environmental studies and with the continuous improvement of machine
learning methods, even more researchers are using ML for the prediction of water quality.
 Predicting water quality through machine learning involves collecting relevant data (e.g., pH,
temperature, pollutant levels) and using algorithms to analyze patterns.
 Common models include decision trees, random forests, and neural networks.
 Feature selection is crucial for accurate predictions. Regularly update the model with new data for
improved accuracy over time.
PARAMETERS OF WATER QUALITY
 Turbidity
 Temperature
 Solids
 pH (Hydrogen Ion Concentration)
 Dissolved Oxygen (DO)
 Nitrate and Nitrite
 Dissolved sodium
 Dissolved chlorine
 Dissolved Sulphate
 Dissolved calcium
FINDINGS AND PROPOSALS
 It gives a brief methodology to predict unknown parameters such Alkalinity, Chloride,
Sulphate values using known parameters such as pH, Electrical Conductivity, TDS etc. using
Decision Tree algorithm, which helps in further classification of water bodies for different
application.
 Results gave accuracy of 83.94%, 87.9%, 81.736%, 79.48% in predicting chloride, total-
hardness, sulphate, total alkalinity respectively.
 It gives a conclusion that Decision Tree method gives the best accuracy when it comes to
water quality prediction against various other machine learning methods.
DATA SET
Feature lists
are:
 pH
 Hardness
 Solids
 Chloramines
 Sulfate
 Conductivity
 Organic Carbon
 Trihalomethanes
 Turbidity
DATA VISUALIZATION
WORKING OF ALGORITHM
Step 1: Select random samples from a given data or training set.
Step 2: This algorithm will construct a decision tree for every training data.
Step 3: Voting will take place by averaging the decision tree.
Step 4: Finally, select the most voted prediction result as the final prediction result.
This combination of multiple models is called Ensemble.
The following steps explain the working Decision Tree Algorithm:
WATER QUALITY PREDICTION METHODOLOGY
 Data collection: A dataset with appropriate parameters like pH, Hardness, Solids,
Chloramines, Sulfate, Conductivity, Organic Carbon, Trihalomethanes and class
variable Potability is used.
 Data Pre-processing: Make the acquired data set in an organized format. Data
Cleaning is the data pre-processing method we choose. Missing values are filled in this
phase.
 Split Data: In this phase we split the data that is preprocessed into training and test
data. 80% data is taken for training and the remaining 20% data is taken for testing.
 Load Train Data: The training data is loaded for training the model using the Random
Forest algorithm.
Train Model: The loaded data is provided for training and a model is created using the
Random Forest algorithm and it is saved for further use.
Confusion Matrix: Confusion matrix is plotted using the algorithm to determine True
Positive, True Negative, False Positive, False Negative metrics.
Export trained model: The trained model is now exported for the testing purposes.
 Load trained model: The trained model is exported and then loaded for testing.
Load test data: Finally test data(input) is provided to predict whether the water
sample is contaminated or not by analyzing the provided parameters.
DESIGN SPECIFICATION
OUTPUT
parameter Mean count std minimum
Dissolved sulfate 7.1222 3276 1.5830 0.352
Dissolved
chloramines
22014.097 3276 8768.57 320.9426
Gran alkalinity 196 3276 32.844 47.43
conductivity 426.205 3276 80.823 181
turbidity 3.9667 3276 0.7803 1.4534
Dissolved carbon 14.26 3276 3.325 2.2000
CONCLUSION
 In conclusion, employing supervised machine learning for water quality prediction
offers a promising and effective approach to address contemporary challenges in
water management.
 The use of advanced algorithms allows for the analysis of complex datasets,
providing valuable insights into the factors influencing water quality.
 This predictive modeling not only enhances our understanding of the dynamics of
water systems but also facilitates proactive decision-making and resource allocation.
 The ability to forecast water quality conditions empowers stakeholders to
implement preventive measures, mitigate potential risks, and optimize water
treatment processes.
FUTURE SCOPE
 The future scope for a water quality prediction project is promising.
 Enhancements could include integrating real-time sensor data, expanding
predictive models for broader geographical coverage, and incorporating machine
learning for adaptive and accurate predictions.
 Collaborations with environmental agencies could lead to the development of
comprehensive water management systems, contributing to sustainable resource
utilization.
 Additionally, exploring mobile applications or online platforms to disseminate water
quality information can empower communities and authorities to make informed
decisions about water usage and conservation.
THANK YOU

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PREDICTING RIVER WATER QUALITY ppt presentation

  • 1. SRI INDU INSTITUTE OF ENGINEERING AND TECHNOLOGY DEPARTMENT OF ELETRONICS AND COMMUNICATION ENGINEERING BATCH:2020-2024 PREPARED BY: M.BHANU PRAKASH REDDY (20X31A0463) V. VEDANIDHI (20X31A04C3) N. SAMUEL JOSHUA (20X31A0474) V. SANDEEP (20X31A04B7) M. VIGNESHWARA CHARY (20X31A0466) PREDICTING RIVER WATER QUALITY PARAMETERS USING SUPERVISED MACHINE LEARNING TECHNIQUES UNDER THE GUIDANCE OF: Dr. K. SRINIVASA REDDY professor
  • 2. CONTENTS 1 ABSTRACT 2 INTRODUCTION 3 PREDICTING WATER QUALITY USING MACHINE LEARNING ALGORITHMS 4 5 6 7 8 DATA VISUALIZATION 9 ANALYSIS OF ALGORITHM 10 WORKING OF ALGORITHM 11 WATER QUALITY PREDICTION METHODOLOGY 12 DESIGN SPECIFICATION 13 CONCLUSION 14 PARAMETERS OF WATER QUALITY FINDINGS AND PROPOSALS DATA SET ANALYSIS OF FUTURE VARIABLES FUTURE SCOPE
  • 3. ABSTRACT  The quality of our water is essential to human health and to our ecosystem.  Pollution in water can cause humans to become ill and wildlife to die.  In this study, five supervised machine learning models were applied to a river water quality dataset that were collected from a river.  ML Models are Multiple linear regression, Random Forest, Decision Tree, support vector machine, Extreme Gradient Boosting.  Four popular river quality parameters were predicted, they are Dissolved Sodium, Dissolved Nitrate, Gran Alkalinity and Electrical Conductivity.  The best performing algorithm was found to be Decision Tree when predicting all parameters with an R-Squared value of between 87% and 98%.  The results found in this study can help to support the monitoring of river water quality in a fast and inexpensive way and improve the existing testing system in place.
  • 4. INTRODUCTION  Rivers have become one of the most used natural water sources globally.  This is due to the accessibility and the location of cities being built close to riverbanks. Water has also become increasingly popular for recreational activities since the Covid-19 lockdown' with more and more people entering the coastal waters, rivers and lakes that surround us.  This means that more people are being exposed to waters that may be polluted and unsafe for human use.  According to a recent survey of World Health Organization (WHO), more than 2.2 billion people in India face problems due to unsafe drinking water and 21% of the diseases are related to impure water.
  • 5. PREDICTING WATER QUALITY USING MACHINE LEARNING ALGORITHMS  Machine learning (ML) is a branch of artificial intelligence and a rapidly growing technical field that lies between computer science and statistics.  Machine Learning works by mimicking human behaviour and uses data and algorithms to improve its accuracy.  ML is widely used within environmental studies and with the continuous improvement of machine learning methods, even more researchers are using ML for the prediction of water quality.  Predicting water quality through machine learning involves collecting relevant data (e.g., pH, temperature, pollutant levels) and using algorithms to analyze patterns.  Common models include decision trees, random forests, and neural networks.  Feature selection is crucial for accurate predictions. Regularly update the model with new data for improved accuracy over time.
  • 6. PARAMETERS OF WATER QUALITY  Turbidity  Temperature  Solids  pH (Hydrogen Ion Concentration)  Dissolved Oxygen (DO)  Nitrate and Nitrite  Dissolved sodium  Dissolved chlorine  Dissolved Sulphate  Dissolved calcium
  • 7. FINDINGS AND PROPOSALS  It gives a brief methodology to predict unknown parameters such Alkalinity, Chloride, Sulphate values using known parameters such as pH, Electrical Conductivity, TDS etc. using Decision Tree algorithm, which helps in further classification of water bodies for different application.  Results gave accuracy of 83.94%, 87.9%, 81.736%, 79.48% in predicting chloride, total- hardness, sulphate, total alkalinity respectively.  It gives a conclusion that Decision Tree method gives the best accuracy when it comes to water quality prediction against various other machine learning methods.
  • 8. DATA SET Feature lists are:  pH  Hardness  Solids  Chloramines  Sulfate  Conductivity  Organic Carbon  Trihalomethanes  Turbidity
  • 10. WORKING OF ALGORITHM Step 1: Select random samples from a given data or training set. Step 2: This algorithm will construct a decision tree for every training data. Step 3: Voting will take place by averaging the decision tree. Step 4: Finally, select the most voted prediction result as the final prediction result. This combination of multiple models is called Ensemble. The following steps explain the working Decision Tree Algorithm:
  • 12.  Data collection: A dataset with appropriate parameters like pH, Hardness, Solids, Chloramines, Sulfate, Conductivity, Organic Carbon, Trihalomethanes and class variable Potability is used.  Data Pre-processing: Make the acquired data set in an organized format. Data Cleaning is the data pre-processing method we choose. Missing values are filled in this phase.  Split Data: In this phase we split the data that is preprocessed into training and test data. 80% data is taken for training and the remaining 20% data is taken for testing.  Load Train Data: The training data is loaded for training the model using the Random Forest algorithm.
  • 13. Train Model: The loaded data is provided for training and a model is created using the Random Forest algorithm and it is saved for further use. Confusion Matrix: Confusion matrix is plotted using the algorithm to determine True Positive, True Negative, False Positive, False Negative metrics. Export trained model: The trained model is now exported for the testing purposes.  Load trained model: The trained model is exported and then loaded for testing. Load test data: Finally test data(input) is provided to predict whether the water sample is contaminated or not by analyzing the provided parameters.
  • 15. OUTPUT parameter Mean count std minimum Dissolved sulfate 7.1222 3276 1.5830 0.352 Dissolved chloramines 22014.097 3276 8768.57 320.9426 Gran alkalinity 196 3276 32.844 47.43 conductivity 426.205 3276 80.823 181 turbidity 3.9667 3276 0.7803 1.4534 Dissolved carbon 14.26 3276 3.325 2.2000
  • 16. CONCLUSION  In conclusion, employing supervised machine learning for water quality prediction offers a promising and effective approach to address contemporary challenges in water management.  The use of advanced algorithms allows for the analysis of complex datasets, providing valuable insights into the factors influencing water quality.  This predictive modeling not only enhances our understanding of the dynamics of water systems but also facilitates proactive decision-making and resource allocation.  The ability to forecast water quality conditions empowers stakeholders to implement preventive measures, mitigate potential risks, and optimize water treatment processes.
  • 17. FUTURE SCOPE  The future scope for a water quality prediction project is promising.  Enhancements could include integrating real-time sensor data, expanding predictive models for broader geographical coverage, and incorporating machine learning for adaptive and accurate predictions.  Collaborations with environmental agencies could lead to the development of comprehensive water management systems, contributing to sustainable resource utilization.  Additionally, exploring mobile applications or online platforms to disseminate water quality information can empower communities and authorities to make informed decisions about water usage and conservation.