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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/13/9/1172 |
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| author | Purushottam Agrawal Alok Sinha Satish Kumar Ankit Agarwal Ashes Banerjee Vasanta Govind Kumar Villuri Chandra Sekhara Rao Annavarapu Rajesh Dwivedi Vijaya Vardhan Reddy Dera Jitendra Sinha Srinivas Pasupuleti |
| author_facet | Purushottam Agrawal Alok Sinha Satish Kumar Ankit Agarwal Ashes Banerjee Vasanta Govind Kumar Villuri Chandra Sekhara Rao Annavarapu Rajesh Dwivedi Vijaya Vardhan Reddy Dera Jitendra Sinha Srinivas Pasupuleti |
| author_sort | Purushottam Agrawal |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T12:00:52Z |
| format | Article |
| id | doaj.art-642542441a76472fbfc9eaa1b28181ea |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T12:00:52Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-642542441a76472fbfc9eaa1b28181ea2023-11-21T16:55:51ZengMDPI AGWater2073-44412021-04-01139117210.3390/w13091172Exploring Artificial Intelligence Techniques for Groundwater Quality AssessmentPurushottam Agrawal0Alok Sinha1Satish Kumar2Ankit Agarwal3Ashes Banerjee4Vasanta Govind Kumar Villuri5Chandra Sekhara Rao Annavarapu6Rajesh Dwivedi7Vijaya Vardhan Reddy Dera8Jitendra Sinha9Srinivas Pasupuleti10Department of Environmental Science and Engineering, Indian Institute of Technology (Indian School of Mines) Dhanbad, Dhanbad 826004, Jharkhand, IndiaDepartment of Environmental Science and Engineering, Indian Institute of Technology (Indian School of Mines) Dhanbad, Dhanbad 826004, Jharkhand, IndiaDepartment of Mining Engineering, Indian Institute of Technology (Indian School of Mines) Dhanbad, Dhanbad 826004, Jharkhand, IndiaDepartment of Hydrology, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, IndiaDepartment of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, IndiaDepartment of Mining Engineering, Indian Institute of Technology (Indian School of Mines) Dhanbad, Dhanbad 826004, Jharkhand, IndiaDepartment of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines) Dhanbad, Dhanbad 826004, Jharkhand, IndiaKIET Group of Institutions, Department of Computer Science and Engineering, Ghaziabad 201206, Delhi-NCR, IndiaSMS India Ltd., Khetri, Rajasthan 333503, IndiaSoil and Water Engineering, SVCAETRS, Indira Gandhi Krishi Vishwavidyalaya, Raipur 492012, Chhattisgarh, IndiaDepartment of Civil Engineering, Indian Institute of Technology (Indian School of Mines) Dhanbad, Dhanbad 826004, Jharkhand, IndiaFreshwater 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.https://www.mdpi.com/2073-4441/13/9/1172WQIPindrawan tank areadrinking water qualityartificial intelligenceparticle swarm optimizationsupport vector machine |
| spellingShingle | Purushottam Agrawal Alok Sinha Satish Kumar Ankit Agarwal Ashes Banerjee Vasanta Govind Kumar Villuri Chandra Sekhara Rao Annavarapu Rajesh Dwivedi Vijaya Vardhan Reddy Dera Jitendra Sinha Srinivas Pasupuleti Exploring Artificial Intelligence Techniques for Groundwater Quality Assessment Water WQI Pindrawan tank area drinking water quality artificial intelligence particle swarm optimization support vector machine |
| title | Exploring Artificial Intelligence Techniques for Groundwater Quality Assessment |
| title_full | Exploring Artificial Intelligence Techniques for Groundwater Quality Assessment |
| title_fullStr | Exploring Artificial Intelligence Techniques for Groundwater Quality Assessment |
| title_full_unstemmed | Exploring Artificial Intelligence Techniques for Groundwater Quality Assessment |
| title_short | Exploring Artificial Intelligence Techniques for Groundwater Quality Assessment |
| title_sort | exploring artificial intelligence techniques for groundwater quality assessment |
| topic | WQI Pindrawan tank area drinking water quality artificial intelligence particle swarm optimization support vector machine |
| url | https://www.mdpi.com/2073-4441/13/9/1172 |
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