Groundwater Arsenic and Health Risk Prediction Model using Machine Learning for T.M Khan Sindh, Pakistan

Iftikhar, Sobia; Bhatti, Sania; Memon, Mohsin Ali; Bhatti, Zulfiqar Ali

doi:10.5815/ijitcs.2020.02.03

Cited by 3 publications

(1 citation statement)

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“…Due to its accuracy and low cost perspectives the use of ML alongside geospatial tools have become popular over the years in many developing countries especially in the South Asian countries. Ensemble Modelling Framework for groundwater level 2 prediction in Bihar 19 , groundwater Arsenic and health risk prediction model using ML in Pakistan 28 , mapping of groundwater productivity potential with ML algorithms in the provincial capital of Baluchistan, Pakistan 29 , water quality analysis with the help of ML algorithms in Sri Lanka by 30 shows the growing interest of ML algorithms in these countries to detect the groundwater modeling. Prediction of groundwater level changes has been done in several circumstances 31 , 32 ,.…”

Section: Introductionmentioning

confidence: 99%

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh

Sarkar,

Rudra,

Talukdar

et al. 2024

Sci Rep

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The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study’s results to inform evidence-based choices on water management and planning at the national level.

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