Fuzzy logic, geostatistics, and multiple linear models to evaluate irrigation metrics and their influencing factors in a drought-prone agricultural region

Groundwater quality drivers in the drought-prone Thakurgaon District, Northwestern Bangladesh: An integrated fuzzy logic and statistical modeling approach

Groundwater quality in the drought-prone Thakurgaon District, Northwestern Bangladesh, is deteriorating due to a combination of natural and anthropogenic factors. This study evaluates the key drivers of groundwater quality degradation by employing ecotoxicological risk indices, such as the Heavy Metal Pollution Index (HPI), Heavy Metal Evaluation Index (HEI), and Nemerow's Pollution Index (NPI). An innovative fuzzy logic approach is used to integrate these indices and reduce uncertainty, while Automatic Linear Modeling (ALM) predicts the primary impacts on the Fuzzy Groundwater Quality Index (FGWQI). Additionally, Monte Carlo simulations assess probabilistic health risks and sensitivity. Groundwater samples from 40 wells were analyzed for physicochemical parameters and heavy metal concentrations. The results show that 25 % of the samples are unsuitable for drinking, and 17.5 % are unfit for household use, based on HPI and HEI values. Fuzzy analysis reveals that 22.5 %, 47.5 %, and 30 % of the samples exhibit excellent, good, and poor quality, respectively. The overlay of FGWQI with Land Use/Land Cover (LULC) maps identifies areas with excellent groundwater quality in the southern parts of the region, while the northern areas suffer from poor quality due to overexploitation. One-way ANOVA indicates that rainfall, water discharge, and LULC significantly affect FGWQI. The ALM results highlight HEI (0.62) and HPI (0.38) as the main factors influencing FGWQI. Health risk analysis reveals elevated non-carcinogenic risks due to arsenic and lead ingestion, particularly for children. These findings emphasize the need for targeted policies and interventions to mitigate health risks and ensure the well-being of the community.

Applying the water quality indices, geographical information system, and advanced decision-making techniques to assess the suitability of surface water for drinking purposes in Brahmani River Basin (BRB), Odisha

Surface water is used for a variety of purposes, including agriculture, drinking water, and other services. Therefore, its quality is crucial for irrigation, human welfare, and health. Thus, the main objective is to improve surface water quality assessment and geochemical analysis to evaluate anthropogenic activities' impact on surface water quality in the Brahmani Watershed, Odisha. In the present paper, emerging techniques such as CRITIC (Criteria Importance Through Inter-criteria Correlation), Additive Ratio Assessment (ARAS), Weighted Aggregated Sum-Product Assessment (WASPAS), SHAP (Shapley Additive Explanation), and Geographical Information System (GIS) were used to locate the origins of pollution in the surface water. The 5-year (2018-2023) database was created by analysing samples that varied geographically over seven sampling locations. The dataset was categorized according to its intended usage. The study employed Inverse Distance Weighting (IDW) tool, to forecast quantities and their geographical arrangement. The water temperature detected at several locations along the river revealed minor variations. The pH variations indicate that the surface water in the studied area is alkaline. Notably, the water's lowest temperature ever recorded was 25.72 °C, at Q-(1). In addition, sufficient DO concentrations are monitored to ensure optimal water quality. The major parts of the study area were found to be majorly affected with high concentrations of PO43-, EC, Ca2+, Mg2+, and SO42-. To determine the degree of contamination, a basic standard reference is necessary to interpret the values, which range from the anthropogenic to the natural contribution. The statistical results reveal the dominant decreasing order amongst the cations, such as: Ca2+  > Mg2+  > Na+  > K+ and in anions, namely, SO42- > Cl- > NO3- > F- > PO43-, respectively. It displays seasonal variations in dissolved and specific phase metal fractions that are not statistically significant at any of the seven sites. Proceeding further, the water quality index showed that the four samples fall in the poor water quality class, whereas the rest, 3 samples, were of good water quality. The surface water is contaminated and negatively affected due to percolation of ions from landfill leachate as per the data of C-WQI. Based on ARAS and WASPAS, Q-(1) and Q-(2) were mainly not fit for consumption. Meanwhile, the SHAP-WQI showed an increase in the number of samples (71.43%) with unsuitable quality for drinking. This emphasizes on the importance of weathering, dissolution, terrigenous, leaching, ion exchange, lithological and evaporation as the primary processes. Human influences were the secondary factors. Overall, the findings indicate that the study area's surface water is safe to drink, with the exception of a few locations including, Q-(1), (2), (3), (4), and (7), in the river water. Integrating GIS using WQ methods gives a new knowledge on the spatial variation in surface water characteristics for designated use. When enforcing regulations and carrying out pollution control operations, this will help determine the precise sampling sites or the sections of the river that show significant degradation. Thus, the integrated model provides insightful data on surface watershed management for urban planners and decision-makers. In overall, these findings underscore the importance of coordinated efforts across administrative boundaries within the basin to reduce water governance costs, providing valuable insights for fostering the coordinated development of regional economies and environmental sustainability. As a result, future studies should be conducted in the area to precisely state the quality of water used for drinking and domestic purposes.

Optimizing coastal groundwater quality predictions: A novel data mining framework with cross-validation, bootstrapping, and entropy analysis

Investigating the potential of novel data mining algorithms (DMAs) for modeling groundwater quality in coastal areas is an important requirement for groundwater resource management, especially in the coastal region of Bangladesh where groundwater is highly contaminated. In this work, the applicability of DMA, including Gaussian Process Regression (GPR), Bayesian Ridge Regression (BRR) and Artificial Neural Network (ANN), for predicting groundwater quality in coastal areas was investigated. The optuna-based optimized hyperparameter is proposed to improve the accuracy of the models, including optuna-GPR and optuna-BRR as benchmark models. Combined cross-validation (CV) and bootstrapping (B) methods were used to build six predictive models. The entropy-based coastal groundwater quality index (ECWQI) was converted into a normalized index (ECWQIn), which was divided into five classes from very poor to excellent. The self-organizing map (SOM), spatial autocorrelation and fuzzy logic model were used to identify spatial groundwater quality patterns based on 12 physicochemical variables collected from 67 groundwater wells. The SOM analysis identified four distinct spatial patterns, including EC-TDS-Cl-, MgpH, Ca2+K+NO₃-, and HCO₃-SO₄2-Na+F-. The results showed that both the ANN (CV) and ANN (B) models performed better than other optuna-based models during the test phase (RMSE = 0.041, MAE = 0.026, R2 = 0.971, RAE = 0.15 = 21 and CC = 0.986) and (RMSE = 0.041, MAE = 0.025, R2 = 0.969, RAE = 0.119 and CC = 0.975), respectively. SO42-, Cl- and F- played an important role in the prediction accuracy. F- and SO42- showed higher spatial autocorrelation, which affected groundwater quality degradation. In addition, the ANN (CV) and ANN (B) models showed a Gaussian distribution of model errors (small standard error, <1 %), indicating the stability of the model. These results indicate the efficiency of the ANN model in predicting groundwater quality in coastal areas, which would help regional water managers in real-time monitoring and management of sustainable groundwater resources.

Comprehensive evaluation of groundwater quality in population-dense and extensive agricultural regions and study on its relationship with agricultural production and human activities

Extensive agricultural regions commonly face issues of poor groundwater management, non-standard agricultural production practices, and unordered discharge of domestic pollution, leading to a continuous decline in groundwater quality and a sharp increase in risks. A comprehensive understanding of groundwater conditions and pollution is a crucial step in effectively addressing the water quality crisis. This study employs the Comprehensive Water Quality Index, Irrigation parameter, and Pollution Index to comprehensively investigate the groundwater quality in a typical agricultural area in Shandong, China, and assesses the suitability of groundwater for irrigation and the risks to human health. Furthermore, multivariate statistical analysis methods are utilized to analyze the relationship between groundwater quality and agricultural production and human activities. The results of the comprehensive quality evaluation indicate that the groundwater in the study area is primarily characterized as weakly alkaline hard freshwater and slightly brackish water, with a hydrochemical type of HCO3-Ca. 42% of the groundwater is unsuitable for drinking, with the main pollutants being TDS, TH, F-, and NO3-. The shallow groundwater level and high soil permeability provide favorable conditions for pollutant migration. Residual Sodium Carbonate (RSC) and Potential Salinity (PS) indicate that 37% of the water samples have excessive bicarbonate levels and 5% have excessive salinity, making them unsuitable for irrigation. Nitrate poses non-carcinogenic risks to all three age groups. Multivariate analysis results show that agricultural pollution dominates in the groundwater, with major pollutants including SO42-, NO3-, COD, NH4-N, F-, etc. Domestic pollution mainly increases the concentrations of ions such as Ca2+, Na+, Mg2+, and also contributes to Cl- and NO3-. The findings of this study contribute to enhancing the rational utilization of groundwater quality in agricultural areas, standardizing agricultural production activities, and promoting the sustainable development of green agriculture.

Hydrogeochemical characterization of shallow and deep groundwater for drinking and irrigation water quality index of Kathmandu Valley, Nepal

A comprehensive hydrogeochemical analysis of 156 groundwater samples (106 shallow and 50 deep) was conducted in the Kathmandu Valley, Nepal. This study addresses a significant research gap by focusing on the hydro-geochemical composition and contamination of groundwater in the Kathmandu Valley, an area with limited detailed assessments. The novelty of this work lies in its comprehensive analysis of both shallow and deep groundwater, particularly concerning the high concentration of contaminants like arsenic, microbial pathogens, and ammonium, which are critical for public health. The results indicate that the mean concentration of turbidity, iron (Fe), and total coliform (TC) was exceeded the permissible range by National Drinking Water Quality Standards (NDWQS). Hydro-geochemical analysis using the Piper and Chadha diagrams showed the Ca2⁺-Mg2⁺-HCO₃ dominance, suggesting carbonate rock weathering and ion exchange as primary processes. Gibbs and mixing diagrams further supported these findings. The Water Quality Index ranged from 3.93 to 442.11 (mean: 66.87) for shallow water while 8.07 to 252.87 (mean: 79.24) with turbidity, iron, and ammonia significantly contributing to the overall index. Salinity hazard assessment considering total dissolved solids, sodium adsorption ratio, sodium percentage, magnesium adsorption ratio, and Kelly ratios, indicated that shallow and deep groundwater samples are suitable for irrigation, as confirmed by Wilcox diagrams. This study provides valuable insights into the groundwater quality of Kathmandu Valley and highlights the need for effective management strategies to ensure sustainable use of this vital resource, providing a nuanced understanding of groundwater quality and its implications for water management in the region. The findings can inform water treatment practices, policy-making, and future research, ultimately aiding in the development of safer and more sustainable groundwater management practices for the region.

The technological assessment of green buildings using artificial neural networks

This study aims to construct a comprehensive evaluation model for efficiently assessing appropriate technologies within green buildings. Initially, an Internet of Things (IoT)-based environmental monitoring system is devised and implemented to collect real-time environmental parameters both inside and outside the building. To evaluate the technical suitability of green buildings, this study employs a multifaceted approach encompassing various criteria, including energy efficiency, environmental impact, economic benefits, user comfort, and sustainability. Specifically, it involves real-time monitoring of environmental parameters, analysis of energy consumption data, and indoor environmental quality indicators derived from user satisfaction surveys. Subsequently, a Multi-Layer Perceptron (MLP) is selected as a conventional artificial neural network (ANN) model, while a Long Short-Term Memory (LSTM) model is chosen as an advanced recurrent neural network model in the realm of deep learning. These models are utilized to process and explore the collected data and assess the technical suitability of green buildings. The dataset comprises physical quantities such as temperature, humidity, and light intensity, as well as economic indicators including energy efficiency and building operating costs. Furthermore, the assessment process considers the building's life cycle assessment and indoor environmental quality factors such as health, comfort, and safety. By incorporating these comprehensive criteria, a holistic evaluation of green building technologies is achieved, ensuring the selected technologies' suitability and effectiveness. The model prediction results demonstrate that the proposed hybrid evaluation model exhibits high accuracy and robust stability in predicting building environmental parameters. For instance, the Root Mean Square Error (RMSE) for temperature prediction is 1.2 °C, the Mean Absolute Error (MAE) is 0.9 °C, and the determination coefficient (R2) reaches 0.95. Similarly, for humidity prediction, the RMSE, MAE, and R2 are 3.5 %, 2.8 %, and 0.88. Compared to the traditional MLP and LSTM models alone, the proposed hybrid model shows significant improvements in predicting building energy consumption, with approximately 15 % and 12 % reductions in RMSE and MAE, respectively, and an increase in R2 values of approximately 7 percentage points. These findings indicate that by amalgamation of the IoT and ANNs, this study successfully establishes a comprehensive model for accurately assessing technologies suitable for green buildings. This approach offers a novel perspective and methodology for the design and evaluation of green buildings.

Ecological risk assessment, source identification and spatial distribution of organic contaminants in terms of mucilage threat in streams of Çanakkale Strait Basin (Türkiye)

In this study, the spatial distributions of organic contamination stressors in water of fluvial habitats in the Çanakkale Strait (ÇS) watershed were investigated and the data were assessed in terms of human health and mucilage threat. Seven significant riverine ecosystems flowing into the ÇS were defined in the basin. Water samples were taken in the spring season (2023), when the phytoplankton communities reach their highest densities. Then they were tested for a total of 8 limnological parameters. The Nutrient Pollution Index (NPI) and Water Quality Index (WQI) were applied to assess the comprehensive quality characteristics of waters. The Hazard Quotient (HQ) and Hazard Index (HI) were applied to indicate the prospective non-carcinogenic human health risks of organic stressors. Principal Component Analysis (PCA) and Cluster Analysis (CA) were applied to categorize the investigated habitats and define the sources of investigated contamination parameters. Also, Geographic Information System (GIS) was used to make an effective assessment through visualization. The determined spatial mean values of the measured variables in ÇS watershed as follows: 18.21 °C for temperature, 8.51 mg/L for DO, 4.57 NTU for turbidity, 3.95 mg/L for suspended solids, 1.11 mg/L for NO3-N, 0.012 mg/L for NO2-N; 0.173 mg/L for PO4-P and 2.32 mg/L for BOD. It has been determined that the organic pollution loads and water temperature values of the investigated sub-basins increase from the upstream to the downstream locations and Çanakkale Stream was recorded as the riskiest fluvial habitat for the ÇS watershed. According to the results of health risk assessment indices, non-carcinogenic risks of organic pollutants would not be expected for all age groups.

Using unsupervised machine learning models to drive groundwater chemistry and associated health risks in Indo-Bangla Sundarban region

Groundwater is an essential resource in the Sundarban regions of India and Bangladesh, but its quality is deteriorating due to anthropogenic impacts. However, the integrated factors affecting groundwater chemistry, source distribution, and health risk are poorly understood along the Indo-Bangla coastal border. The goal of this study is to assess groundwater chemistry, associated driving factors, source contributions, and potential non-carcinogenic health risks (PN-CHR) using unsupervised machine learning models such as a self-organizing map (SOM), positive matrix factorization (PMF), ion ratios, and Monte Carlo simulation. For the Sundarban part of Bangladesh, the SOM clustering approach yielded six clusters, while it yielded five for the Indian Sundarbans. The SOM results showed high correlations among Ca2+, Mg2+, and K+, indicating a common origin. In the Bangladesh Sundarbans, mixed water predominated in all clusters except for cluster 3, whereas in the Indian Sundarbans, Cl--Na+ and mixed water dominated in clusters 1 and 2, and both water types dominated the remaining clusters. Coupling of SOM, PMF, and ionic ratios identified rock weathering as a driving factor for groundwater chemistry. Clusters 1 and 3 were found to be influenced by mineral dissolution and geogenic inputs (overall contribution of 47.7%), while agricultural and industrial effluents dominated clusters 4 and 5 (contribution of 52.7%) in the Bangladesh Sundarbans. Industrial effluents and agricultural activities were associated with clusters 3, 4, and 5 (contributions of 29.5% and 25.4%, respectively) and geogenic sources (contributions of 23 and 22.1% in clusters 1 and 2) in Indian Sundarbans. The probabilistic health risk assessment showed that NO3- poses a higher PN-CHR risk to human health than F- and As, and that potential risk to children is more evident in the Bangladesh Sundarban area than in the Indian Sundarbans. Local authorities must take urgent action to control NO3- emissions in the Indo-Bangla Sundarbans region.