Prediction of groundwater nitrate concentration in a semiarid region using hybrid Bayesian artificial intelligence approaches

Al‐Kindi, Khalifa M.; Mukherjee, Kaustuv; Pandey, Manish; Arora, Aman; Janizadeh, Saeid; Pham, Quoc Bao; Anh, Duong Tran; Ahmadi, Kourosh

doi:10.1007/s11356-021-17224-9

Cited by 16 publications

(4 citation statements)

References 62 publications

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“…Optimising falaj channel alignment with plan curvature promotes efficient flow, prevents erosion and ensures uniform irrigation. Considerations such as slope, soil type and crop requirements complement plan curvature in Aini design and promote water efficiency and agricultural productivity [ 31 ]. Fig 2I displays the plan curvature distribution in the study area.…”

Section: Methodsmentioning

confidence: 99%

Assessing the environmental factors affecting the sustainability of Aini Falaj system

Al-Kindi

2024

PLoS ONE

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This study investigates the spatial distribution patterns and environmental factors influencing the Aini Falaj system in a specific study area. The research findings are presented through the lens of the following four categories: collinearity diagnostics, spatial autocorrelation analysis, kernel density (KD) findings, and multivariate geographically weighted regression (MGWR) analysis. The collinearity diagnostics were applied to examine the interrelationships among 18 independent environmental variables. The results indicate the absence of significant multicollinearity concerns, with most variables showing values below the critical threshold of five for variance inflation factors (VIFs). The selected variables indicate minimal intercorrelation, suggesting that researchers should be confident utilizing them in subsequent modelling or regression analyses. A spatial autocorrelation analysis using Moran’s Index revealed positive spatial autocorrelation and significant clustering patterns in the distribution of live and non-functional Aini Falajs. High concentrations of live or dead Falajs tended to be surrounded by neighbouring areas with similar characteristics. These findings provide insights into the ecological preferences and habitat associations of Aini Falajs, thereby aiding conservation strategies and targeted studies. The kernel density (KD) analysis depicted distribution patterns of live and dry Aini Falajs through hotspots and cold spots. Specific regions exhibited high-density areas of live Falajs, indicating favourable environmental conditions or historical factors contributing to their concentrated distribution. Identifying these high-density zones can enhance our understanding of the spatial patterns and potential factors influencing the prevalence and sustainability of Aini Falajs. The multivariate geographically weighted regression (MGWR) models revealed strong associations between the live or dead status of Aini Falajs and environmental factors. The precipitation, topographic wetness index (TWI), aspect and slope exerted positive impacts on the live status, while evaporation, solar radiation, distance to drains and drain density exerted negative influences. Similar associations were observed for the dead status, emphasising the importance of controlling evaporation, shading mechanisms, proper drainage planning and sustainable land-use practices. This study provides valuable insights into the spatial distributions and factors influencing the live and dead status of Aini Falajs, thereby contributing to our understanding of their ecological dynamics and guiding conservation efforts and management strategies.

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Section: Methodsmentioning

confidence: 99%

Assessing the environmental factors affecting the sustainability of Aini Falaj system

Al-Kindi

2024

PLoS ONE

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“…2). The higher the population density, the higher the probability groundwater is exposed to the pollutant (Alkindi et al 2022).…”

Section: Groundwater Quality Predictorsmentioning

confidence: 99%

“…Climate variabilities, including precipitation and evaporation, can indirectly affect groundwater quality through the recharge and discharge of aquifers (Band et al 2020;Alkindi et al 2022). Precipitation is the region's primary source of groundwater recharge, and high precipita-tion can introduce nitrate and organic materials into the aquifer system in agricultural areas.…”

Section: Groundwater Quality Predictorsmentioning

confidence: 99%

Evaluation of machine learning algorithms for groundwater quality modeling

Sahour¹,

Khanbeyki

Gholami

et al. 2023

Environ Sci Pollut Res

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Groundwater quality is measured through water sampling, and lab analysis. The eld-based measurements are costly and time-consuming when applied over a large domain. In this study, we developed a machine learning-based framework to map groundwater quality in an uncon ned aquifer in the north of Iran. Groundwater samples were provided from 248 monitoring wells across the region. The groundwater quality index (GWQI) in each well was measured and classi ed into four classes of Very poor, Poor, Good, and Excellent according to their cut-off values. Factors affecting groundwater quality, including distance to industrial centers, distance to residential areas, population density, aquifer transmissivity, precipitation, evaporation, geology, and elevation, were identi ed and prepared in the GIS environment. Six machine learning classi ers, including extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), arti cial neural networks (ANN), k-nearest neighbor (KNN), and Gaussian classi er model (GCM), were used to establish relationships between GWQI and its controlling factors. The algorithms were evaluated using the receiver operating characteristic curve (ROC) and statistical e ciencies (overall accuracy, precision, recall, and f-1 score). Accuracy assessment showed that ML algorithms provided high accuracy in predicting groundwater quality. However, RF was selected as the optimum model given its higher accuracy (overall accuracy, precision, and recall = 0.92; ROC = 0.95). The trained RF model was used to map GWQI classes across the entire region. Results showed that the Poor GWQI class is dominant in the study area and Good GWQI can be found in southwest. An area of Very Poor GWQI was observed in the north. Findings indicated that the distance to industrial locations is the main factor affecting groundwater quality in the area. The study provides a cost-effective methodology in groundwater quality modeling that can be duplicated in other regions with similar hydrological and geo-logical settings.

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“…By applying hybrid AI models, hydrogeology scientists can better understand the complexities of groundwater systems and develop effective strategies to conserve and manage this vital resource [36][37][38]. In recent years, more attention has been paid to the successful use of Hybrid AI in different hydrogeological fields, including Assessing groundwater potential [39,40], Estimating groundwater recharge [41,42], Managing and predicting groundwater levels [43,44], Simulating groundwater flow [45], Assessing groundwater quality [27,[46][47][48], Estimating aquifer parameters [49], Identifying sources of pollution in groundwater [48,50], Managing and planning groundwater resources [51], Designing groundwater remediation strategies [52], Managing water allocation [53,54], Assessing vulnerability to groundwater depletion or contamination [55,56], Predicting future groundwater conditions [57,58], including seawater intrusion in coastal areas [59,60].…”

Section: Introductionmentioning

confidence: 99%

Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review

Zaresefat

Derakhshani

2023

Water

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Developing precise soft computing methods for groundwater management, which includes quality and quantity, is crucial for improving water resources planning and management. In the past 20 years, significant progress has been made in groundwater management using hybrid machine learning (ML) models as artificial intelligence (AI). Although various review articles have reported advances in this field, existing literature must cover groundwater management using hybrid ML. This review article aims to understand the current state-of-the-art hybrid ML models used for groundwater management and the achievements made in this domain. It includes the most cited hybrid ML models employed for groundwater management from 2009 to 2022. It summarises the reviewed papers, highlighting their strengths and weaknesses, the performance criteria employed, and the most highly cited models identified. It is worth noting that the accuracy was significantly enhanced, resulting in a substantial improvement and demonstrating a robust outcome. Additionally, this article outlines recommendations for future research directions to enhance the accuracy of groundwater management, including prediction models and enhance related knowledge.

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Prediction of groundwater nitrate concentration in a semiarid region using hybrid Bayesian artificial intelligence approaches

Cited by 16 publications

References 62 publications

Assessing the environmental factors affecting the sustainability of Aini Falaj system

Assessing the environmental factors affecting the sustainability of Aini Falaj system

Evaluation of machine learning algorithms for groundwater quality modeling

Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review

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