Exploring Artificial Intelligence Techniques for Groundwater Quality Assessment

Freshwater quality and quantity are some of the fundamental requirements for sustaining human life and civilization. The Water Quality Index is the most extensively used parameter for determining water quality worldwide. However, the traditional approach for the calculation of the WQI is often compl...

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Bibliographic Details
Published in:Water (Basel) 2021-05, Vol.13 (9), p.1172
Main Authors: Agrawal, Purushottam, Sinha, Alok, Kumar, Satish, Agarwal, Ankit, Banerjee, Ashes, Villuri, Vasanta Govind Kumar, Annavarapu, Chandra Sekhara Rao, Dwivedi, Rajesh, Dera, Vijaya Vardhan Reddy, Sinha, Jitendra, Pasupuleti, Srinivas
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Alkalinity
Artificial intelligence
Bayesian analysis
Chemical oxygen demand
Data collection
Fluorides
Fresh water
Groundwater
Groundwater quality
Learning algorithms
Machine learning
Mathematical optimization
Methods
Optimization
Population growth
Potassium
Predictions
Quality assessment
Quality control
Quality management
Support vector machines
Topography
Water quality
Water shortages
Water, Underground
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Summary:Freshwater quality and quantity are some of the fundamental requirements for sustaining human life and civilization. The Water Quality Index is the most extensively used parameter for determining water quality worldwide. However, the traditional approach for the calculation of the WQI is often complex and time consuming since it requires handling large data sets and involves the calculation of several subindices. We investigated the performance of artificial intelligence techniques, including particle swarm optimization (PSO), a naive Bayes classifier (NBC), and a support vector machine (SVM), for predicting the water quality index. We used an SVM and NBC for prediction, in conjunction with PSO for optimization. To validate the obtained results, groundwater water quality parameters and their corresponding water quality indices were found for water collected from the Pindrawan tank area in Chhattisgarh, India. Our results show that PSO–NBC provided a 92.8% prediction accuracy of the WQI indices, whereas the PSO–SVM accuracy was 77.60%. The study’s outcomes further suggest that ensemble machine learning (ML) algorithms can be used to estimate and predict the Water Quality Index with significant accuracy. Thus, the proposed framework can be directly used for the prediction of the WQI using the measured field parameters while saving significant time and effort.
ISSN:2073-4441
2073-4441
DOI:10.3390/w13091172