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1. Base paper Title: Detection of Wastewater Pollution Through Natural Language Generation
With a Low-Cost Sensing Platform
Modified Title: Using a Low-Cost Sensing Platform to Detect Wastewater Pollution Through
Natural Language Generation
Abstract
The detection of contaminants in several environments (e.g., air, water, sewage
systems) is of paramount importance to protect people and predict possible dangerous
circumstances. Most works do this using classical Machine Learning tools that act on the
acquired measurement data. This paper introduces two main elements: a low-cost platform to
acquire, pre-process, and transmit data to classify contaminants in wastewater; and a novel
classification approach to classify contaminants in wastewater, based on deep learning and the
transformation of raw sensor data into natural language metadata. The proposed solution
presents clear advantages against state-of-the-art systems in terms of higher effectiveness and
reasonable efficiency. The main disadvantage of the proposed approach is that it relies on
knowing the injection time, i.e., the instant in time when the contaminant is injected into the
wastewater. For this reason, the developed system also includes a finite state machine tool able
to infer the exact time instant when the substance is injected. The entire system is presented
and discussed in detail. Furthermore, several variants of the proposed processing technique are
also presented to assess the sensitivity to the number of used samples and the corresponding
promptness/computational burden of the system. The lowest accuracy obtained by our
technique is 91.4%, which is significantly higher than the 81.0% accuracy reached by the best
baseline method.
Existing System
The task of accurate environmental monitoring is a pressing worldwide issue which is
bound to become increasingly more important in the near future. There are many aspects that
should be kept under control and concern the quality of the air, soil, and water [1], [2]. In fact,
their continuous monitoring would allow targeted and timely actions aimed at restoring optimal
conditions following dangerous events such as the appearance of pollutants. In this context,
monitoringmore important in the near future. There are many aspects that should be kept under
control and concern the quality of the air, soil, and water [1], [2]. In fact, their continuous

2. monitoring would allow targeted and timely actions aimed at restoring optimal conditions
following dangerous events such as the appearance of pollutants. In this context, monitoring
wastewater (WW) is particularly important [3]. WW is the water that has already been used for
some purpose (civil or industrial uses) and must be subjected to purification before being
returned to the natural cycle. To function at their best and effectively, the purification systems
must know a priori the type of substances mixed with the water. It follows that a purification
system for water for industrial use will be different from a purification plant for water for civil
use. Hence, there is a strong need for protocols to promptly detect incompatible substances, to
guarantee the correct and effective operation of purification plants [4]. Currently, this is solved
by organizing periodic monitoring activities at particular points of the water path, which are
carried out by the control institutes in charge using specialized laboratory instruments.
Although this is an effective method, the quality of the water between two consecutive checks
is unknown, and the checks may be not frequent enough to promptly identify problems. The
ideal solution would combine automated continuous and distributed early warning monitoring,
alongside periodic manual checks carried out by the control institutes. To solve the problems
of cost and installation of a distributed and continuous monitoring system, it is necessary to
resort to low-cost and IoT-ready systems [5], which are able not only to collect environmental
data but also to process them relying on centralized data collection and elaboration points.
Drawback in Existing System
 Data Quality and Variability:
Data Variability: Wastewater composition can vary significantly, and low-cost
sensors might not capture this variability accurately. This can result in incomplete or
inconsistent data for NLG to generate meaningful reports.
Interference: Other environmental factors or contaminants may interfere with sensor
readings, leading to incorrect interpretations of pollution levels.
 Limited Sensor Capabilities:
Parameter Coverage: Low-cost sensors may not cover all relevant parameters
necessary for a comprehensive assessment of wastewater pollution. Some critical
pollutants may not be detectable by these sensors, limiting the scope of pollution
monitoring.

3.  Public Perception:
Trust and Perception: Public trust in the accuracy of low-cost sensor data and the
generated reports may be lower compared to data obtained from more sophisticated,
expensive monitoring systems. This can impact the acceptance and effectiveness of the
pollution monitoring program.
 Scalability and Coverage:
Limited Coverage: Deploying low-cost sensors on a large scale for comprehensive
coverage may be economically challenging. Gaps in coverage may lead to incomplete
pollution detection across different areas.
Proposed System
 The proposed solution presents clear advantages against state-of-the-art systems in
terms of higher effectiveness and reasonable efficiency.
 Proposed methodology outperforms the baseline methods and its effectiveness allows
for practical usage of the developed methodology.
 The proposed methodology testing its effectiveness against a set of state-of-the-art
baselines, and we measured its efficiency. Experimental results show that the proposed
methodology outperforms the baseline methods, and its efficiency and effectiveness
allow for its deployment and for practical use.
 Proposed approach is related to the availability of data, since a certain amount of labeled
data is needed to train the deep learning model. Obtaining such data requires access to
polluting substances or contaminated wastewater, and this can be difficult in practical
situations.
Algorithm
 Sensor Data Acquisition:
Signal Processing Algorithms: Algorithms for reading and processing signals from
sensors are crucial. This can include noise reduction, filtering, and signal enhancement
techniques to improve the quality of raw sensor data.

4.  Feature Extraction:
Identifying Relevant Features: Feature extraction algorithms help in identifying and
selecting relevant parameters from the sensor data. This step is essential for reducing
dimensionality and focusing on key indicators of pollution.
 NLG Integration:
Text Generation Algorithms: NLG algorithms take the analyzed sensor data and
convert it into human-readable reports. These algorithms may use templates, rule-based
systems, or more advanced natural language processing (NLP) approaches.
Advantages
 Cost-Effectiveness:
Affordable Sensor Technology: Low-cost sensing platforms are more economically
feasible compared to traditional, high-end monitoring systems. This allows for wider
deployment of sensors, covering a larger geographic area for monitoring.
 Real-Time Monitoring:
Immediate Detection: Low-cost sensors, when coupled with real-time data
processing algorithms, enable the immediate detection of pollution events. This rapid
response is crucial for addressing environmental concerns promptly.
 Integration with NLG:
Human-Readable Reports: NLG technology translates complex sensor data into
easily understandable and actionable reports. This facilitates communication between
technical experts, decision-makers, and the general public, fostering a broader
understanding of pollution issues.
 Compliance Monitoring:
Regulatory Compliance: These systems can help industries and municipalities ensure
compliance with environmental regulations by providing real-time data on pollutant
levels.

5. Software Specification
 Processor : I3 core processor
 Ram : 4 GB
 Hard disk : 500 GB
Software Specification
 Operating System : Windows 10 /11
 Frond End : Python
 Back End : Mysql Server
 IDE Tools : Pycharm