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Mallick J, Alqadhi S, Hang HT, Alsubih M. Interpreting optimised data-driven solution with explainable artificial intelligence (XAI) for water quality assessment for better decision-making in pollution management. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:42948-42969. [PMID: 38884936 DOI: 10.1007/s11356-024-33921-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 06/03/2024] [Indexed: 06/18/2024]
Abstract
In Saudi Arabia, water pollution and drinking water scarcity pose a major challenge and jeopardise the achievement of sustainable development goals. The urgent need for rapid and accurate monitoring and assessment of water quality requires sophisticated, data-driven solutions for better decision-making in water management. This study aims to develop optimised data-driven models for comprehensive water quality assessment to enable informed decisions that are critical for sustainable water resources management. We used an entropy-weighted arithmetic technique to calculate the Water Quality Index (WQI), which integrates the World Health Organization (WHO) standards for various water quality parameters. Our methodology incorporated advanced machine learning (ML) models, including decision trees, random forests (RF) and correlation analyses to select features essential for identifying critical water quality parameters. We developed and optimised data-driven models such as gradient boosting machines (GBM), deep neural networks (DNN) and RF within the H2O API framework to ensure efficient data processing and handling. Interpretation of these models was achieved through a three-pronged explainable artificial intelligence (XAI) approach: model diagnosis with residual analysis, model parts with permutation-based feature importance and model profiling with partial dependence plots (PDP), accumulated local effects (ALE) plots and individual conditional expectation (ICE) plots. The quantitative results revealed insightful findings: fluoride and residual chlorine had the highest and lowest entropy weights, respectively, indicating their differential effects on water quality. Over 35% of the water samples were categorised as 'unsuitable' for consumption, highlighting the urgency of taking action to improve water quality. Amongst the optimised models, the Random Forest (model 79) and the Deep Neural Network (model 81) proved to be the most effective and showed robust predictive abilities with R2 values of 0.96 and 0.97 respectively for testing dataset. Model profiling as XAI highlighted the significant influence of key parameters such as nitrate, total hardness and pH on WQI predictions. These findings enable targeted water quality improvement measures that are in line with sustainable water management goals. Therefore, our study demonstrates the potential of advanced, data-driven methods to revolutionise water quality assessment in Saudi Arabia. By providing a more nuanced understanding of water quality dynamics and enabling effective decision-making, these models contribute significantly to the sustainable management of valuable water resources.
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Affiliation(s)
- Javed Mallick
- Department of Civil Engineering, College of Engineering, King Khalid University, P.O. Box: 394, Abha, 61411, Kingdom of Saudi Arabia.
| | - Saeed Alqadhi
- Department of Civil Engineering, College of Engineering, King Khalid University, P.O. Box: 394, Abha, 61411, Kingdom of Saudi Arabia
| | - Hoang Thi Hang
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India
| | - Majed Alsubih
- Department of Civil Engineering, College of Engineering, King Khalid University, P.O. Box: 394, Abha, 61411, Kingdom of Saudi Arabia
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Talukdar S, Shahfahad, Bera S, Naikoo MW, Ramana GV, Mallik S, Kumar PA, Rahman A. Optimisation and interpretation of machine and deep learning models for improved water quality management in Lake Loktak. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 351:119866. [PMID: 38147770 DOI: 10.1016/j.jenvman.2023.119866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 11/28/2023] [Accepted: 12/13/2023] [Indexed: 12/28/2023]
Abstract
Loktak Lake, one of the largest freshwater lakes in Manipur, India, is critical for the eco-hydrology and economy of the region, but faces deteriorating water quality due to urbanisation, anthropogenic activities, and domestic sewage. Addressing the urgent need for effective pollution management, this study aims to assess the lake's water quality status using the water quality index (WQI) and develop advanced machine learning (ML) tools for WQI assessment and ML model interpretation to improve pollution management decision making. The WQI was assessed using entropy-based weighting arithmetic and three ML models - Gradient Boosting Machine (GBM), Random Forest (RF) and Deep Neural Network (DNN) - were optimised using a grid search algorithm in the H2O Application Programming Interface (API). These models were validated by various metrics and interpreted globally and locally via Partial Dependency Plot (PDP), Accumulated Local Effect (ALE) and SHapley Additive exPlanations (SHAP). The results show a WQI range of 72.38-100, with 52.7% of samples categorised as very poor. The RF model outperformed GBM and DNN and showed the highest accuracy and generalisation ability, which is reflected in the superior R2 values (0.97 in training, 0.9 in test) and the lower root mean square error (RMSE). RF's minimal margin of error and reliable feature interpretation contrasted with DNN's larger margin of error and inconsistency, which affected its usefulness for decision making. Turbidity was found to be a critical predictive feature in all models, significantly influencing WQI, with other variables such as pH and temperature also playing an important role. SHAP dependency plots illustrated the direct relationship between key water quality parameters such as turbidity and WQI predictions. The novelty of this study lies in its comprehensive approach to the evaluation and interpretation of ML models for WQI estimation, which provides a nuanced understanding of water quality dynamics in Loktak Lake. By identifying the most effective ML models and key predictive functions, this study provides invaluable insights for water quality management and paves the way for targeted strategies to monitor and improve water quality in this vital freshwater ecosystem.
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Affiliation(s)
- Swapan Talukdar
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India.
| | - Shahfahad
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India.
| | - Somnath Bera
- Department of Geography, Central University of South Bihar, Gaya, Bihar, 823001, India.
| | - Mohd Waseem Naikoo
- Department of Geography & Disaster Management, University of Kashmir, Srinagar, Jammu & Kashmir, 190006, India.
| | - G V Ramana
- Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.
| | - Santanu Mallik
- Department of Civil Engineering, National Institution of Technology, Agaratala, Tripura, 799046, India.
| | - Potsangbam Albino Kumar
- Department of Civil Engineering, National Institution of Technology, Imphal, Manipur, 795004, India.
| | - Atiqur Rahman
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India.
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Mia MY, Haque ME, Islam ARMT, Jannat JN, Jion MMMF, Islam MS, Siddique MAB, Idris AM, Senapathi V, Talukdar S, Rahman A. Analysis of self-organizing maps and explainable artificial intelligence to identify hydrochemical factors that drive drinking water quality in Haor region. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 904:166927. [PMID: 37704149 DOI: 10.1016/j.scitotenv.2023.166927] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 06/28/2023] [Accepted: 09/06/2023] [Indexed: 09/15/2023]
Abstract
Water contamination undermines human survival and economic growth. Water resource protection and management require knowledge of water hydrochemistry and drinking water quality characteristics, mechanisms, and factors. Self-organizing maps (SOM) have been developed using quantization and topographic error approaches to cluster hydrochemistry datasets. The Piper diagram, saturation index (SI), and cation exchange method were used to determine the driving mechanism of hydrochemistry in both surface and groundwater, while the Gibbs diagram was used for surface water. In addition, redundancy analysis (RDA) and a generalized linear model (GLM) were used to determine the key drinking water quality parameters in the study area. Additionally, the study aimed to utilize Explainable Artificial Intelligence (XAI) techniques to gain insights into the relative importance and impact of different parameters on the entropy water quality index (EWQI). The SOM results showed that thirty neurons generated the hydrochemical properties of water and were organized into four clusters. The Piper diagram showed that the primary hydrochemical facies were HCO3--Ca2+ (cluster 4), Cl---Na+ (all clusters), and mixed (clusters 1 and 4). Results from SI and cation exchange show that demineralization and ion exchange are the driving mechanisms of water hydrochemistry. About 45 % of the studied samples are classified as "medium quality"," that could be suitable as drinking water with further refinement. Cl- may pose increased non-carcinogenic risk to adults, with children at double risk. Cluster 4 water is low-risk, supporting EWQI findings. The RDA and GLM observations agree in that Ca2+, Mg2+, Na+, Cl- and HCO3- all have a positive and significant effect on EWQI, with the exception of K+. TDS, EC, Na+, and Ca2+ have been identified as influencing factors based on bagging-based XAI analysis at global and local levels. The analysis also addressed the importance of SO4, HCO3, Cl, Mg2+, K+, and pH at specific locations.
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Affiliation(s)
- Md Yousuf Mia
- Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh
| | - Md Emdadul Haque
- Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh.
| | - Abu Reza Md Towfiqul Islam
- Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh; Department of Development Studies, Daffodil International University, Dhaka 1216, Bangladesh.
| | - Jannatun Nahar Jannat
- Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh
| | | | - Md Saiful Islam
- Department of Soil Science, Patuakhali Science and Technology University, Dumki, Patuakhali 8602, Bangladesh
| | - Md Abu Bakar Siddique
- Institute of National Analytical Research and Service (INARS), Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhanmondi, Dhaka 1205, Bangladesh
| | - Abubakr M Idris
- Department of Chemistry, College of Science, King Khalid University, Abha 62529, Saudi Arabia; Research Center for Advanced Materials Science (RCAMS), King Khalid University, Abha, Saudi Arabia
| | | | - Swapan Talukdar
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Atiqur Rahman
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India.
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Zihad SMRA, Islam ARMT, Siddique MAB, Mia MY, Islam MS, Islam MA, Bari ABMM, Bodrud-Doza M, Yakout SM, Senapathi V, Chatterjee S. Fuzzy logic, geostatistics, and multiple linear models to evaluate irrigation metrics and their influencing factors in a drought-prone agricultural region. ENVIRONMENTAL RESEARCH 2023; 234:116509. [PMID: 37399988 DOI: 10.1016/j.envres.2023.116509] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 06/05/2023] [Accepted: 06/23/2023] [Indexed: 07/05/2023]
Abstract
The quality of water used for irrigation is one of the major threats to maintaining the long-term sustainability of agricultural practices. Although some studies have addressed the suitability of irrigation water in different parts of Bangladesh, the irrigation water quality in the drought-prone region has yet to be thoroughly studied using integrated novel approaches. This study aims to assess the suitability of irrigation water in the drought-prone agricultural region of Bangladesh using traditional irrigation metrics such as sodium percentage (NA%), magnesium adsorption ratio (MAR), Kelley's ratio (KR), sodium adsorption ratio (SAR), total hardness (TH), permeability index (PI), and soluble sodium percentage (SSP), along with novel irrigation indices such as irrigation water quality index (IWQI) and fuzzy irrigation water quality index (FIWQI). Thirty-eight water samples were taken from tube wells, river systems, streamlets, and canals in agricultural areas, then analyzed for cations and anions. The multiple linear regression model predicted that SAR (0.66), KR (0.74), and PI (0.84) were the primary important elements influencing electrical conductivity (EC). Based on the IWQI, all water samples fall into the "suitable" category for irrigation. The FIWQI suggests that 75% of the groundwater and 100% of the surface water samples are excellent for irrigation. The semivariogram model indicates that most irrigation metrics have moderate to low spatial dependence, suggesting strong agricultural and rural influence. Redundancy analysis shows that Na+, Ca2+, Cl-, K+, and HCO3- in water increase with decreasing temperature. Surface water and some groundwater in the southwestern and southeastern parts are suitable for irrigation. The northern and central parts are less suitable for agriculture because of elevated K+ and Mg2+ levels. This study determines irrigation metrics for regional water management and pinpoints suitable areas in the drought-prone region, which provides a comprehensive understanding of sustainable water management and actionable steps for stakeholders and decision-makers.
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Affiliation(s)
- S M Rabbi Al Zihad
- Department of Disaster Management, Begum Bekeya University, Rangpur, 5400, Bangladesh.
| | - Abu Reza Md Towfiqul Islam
- Department of Disaster Management, Begum Bekeya University, Rangpur, 5400, Bangladesh; Department of Development Studies, Daffodil International University, Dhaka, 1216, Bangladesh.
| | - Md Abu Bakar Siddique
- Institute of National Analytical Research and Service (INARS), Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhanmondi, Dhaka 1205, Bangladesh.
| | - Md Yousuf Mia
- Department of Disaster Management, Begum Bekeya University, Rangpur, 5400, Bangladesh.
| | - Md Saiful Islam
- Department of Soil Science, Patuakhali Science and Technology University, Dumki, Patuakhali 8602, Bangladesh.
| | - Md Aminul Islam
- Department of Disaster Management, Begum Bekeya University, Rangpur, 5400, Bangladesh.
| | - A B M Mainul Bari
- Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh.
| | - Md Bodrud-Doza
- Department of Geography, Environment & Geomatics, University of Guelph, ON | N1G 2W1, Canada.
| | - Sobhy M Yakout
- Department of Biochemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia.
| | | | - Sumanta Chatterjee
- USDA-ARS Hydrology and Remote Sensing Laboratory, BARC-West, Beltsville, MD 20705, USA; ICAR-National Rice Research Institute, Cuttack, Odisha 753006, India.
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Mia MY, Islam ARMT, Jannat JN, Jion MMMF, Sarker A, Tokatli C, Siddique MAB, Ibrahim SM, Senapathi V. Identifying factors affecting irrigation metrics in the Haor basin using integrated Shannon's entropy, fuzzy logic and automatic linear model. ENVIRONMENTAL RESEARCH 2023; 226:115688. [PMID: 36931377 DOI: 10.1016/j.envres.2023.115688] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/10/2023] [Accepted: 03/13/2023] [Indexed: 06/18/2023]
Abstract
The sustainability of agricultural practices is seriously threatened by the quality of water used for irrigation. This paper aims to evaluate the suitability of irrigation water and identify the region suitable for agricultural use in the Haor basin of Bangladesh using conventional irrigation indices such as sodium adsorption ratio (SAR), percent sodium (Na%), magnesium hazard ratio (MHR), permeability index (PI), and Kelly's ratio (KR), as well as novel irrigation indices such as, Shannon's entropy index for irrigation water quality (EWQ) and fuzzy logic index for irrigation water quality (FIWQI). The main influences of groundwater and surface water parameters on irrigation indices were predicted using automatic linear modeling (ALM). Forty water samples were collected from shallow tube wells, rivers, canals, ponds, and drainage systems within agricultural land sampled and analyzed for cations and anions. SAR and KR show that 52.5% and 60% of the samples exceeded the allowable level, respectively, indicating that they were unsuitable for irrigation. According to EWQI, about 55% of the analyzed samples were of good quality, while 45% were of medium quality. ALM predicted that KR (0.98), Na% (0.87), and MHR (0.14) were the main significant factors affecting SAR and KR. ALM shows that elevated sodium, magnesium, and calcium are the most important factors affecting irrigation water suitability. The EWQI and FIWQI integrated models showed that water from nearly 30% of the sampling sites would need treatment before use. A new suitability map created by overlaying all parameters showed that surface water and some groundwater in the western and southwestern portions are suitable for agriculture. The north-central part is unsuitable for irrigation due to excessive sodium and magnesium levels. This paper will highlight the irrigation pattern for regional water resource use, identify new suitable regions, and improve sustainable agricultural practices in the Haor basin.
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Affiliation(s)
- Md Yousuf Mia
- Department of Disaster Management, Begum Bekeya University, Rangpur, 5400, Bangladesh.
| | - Abu Reza Md Towfiqul Islam
- Department of Disaster Management, Begum Bekeya University, Rangpur, 5400, Bangladesh; Department of Development Studies, Daffodil International University, Dhaka 1216, Bangladesh.
| | - Jannatun Nahar Jannat
- Department of Disaster Management, Begum Bekeya University, Rangpur, 5400, Bangladesh.
| | | | - Aniruddha Sarker
- Department of Agro-food Safety and Crop Protection, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju, Republic of Korea.
| | - Cem Tokatli
- Trakya University, Laboratory Technology Program, Ipsala, Edirne, Turkiye.
| | - Md Abu Bakar Siddique
- Institute of National Analytical Research and Service (INARS), Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhanmondi, Dhaka, 1205, Bangladesh.
| | - Sobhy M Ibrahim
- Department of Biochemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia.
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