Integration of hydrogeological data, GIS and AHP techniques applied to delineate groundwater potential zones in sandstone, limestone and shales rocks of the Damoh district, (MP) central India

Environmental Research, Год журнала: 2023, Номер 228, С. 115832 - 115832

Опубликована: Апрель 11, 2023

Язык: Английский

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Delineation of Groundwater Potential Zones (GWPZs) in a Semi-Arid Basin through Remote Sensing, GIS, and AHP Approaches

Water, Год журнала: 2022, Номер 14(13), С. 2138 - 2138

Опубликована: Июль 5, 2022

Groundwater occurrence in semi-arid regions is variable space and time due to climate patterns, terrain features, aquifer properties. Thus, accurate delineation of Potential Zones (GWPZs) essential for sustainable water resources management these environments. The present research aims delineate assess GWPZs a basin San Luis Potosi (SLP), Mexico, through the integration Remote Sensing (RS), Geographic Information System (GIS), Analytic Hierarchy Process (AHP). Seven thematic layers (geology, lineament density, land use cover, topographic wetness index (TWI), rainfall, drainage slope) were generated raster format. After AHP procedure rank assignment, integrated using calculator obtain map. results indicated that 68.21% area classified as low groundwater potential, whereas 26.30% moderate. Validation was done by assessing residence data from 15 wells distributed study area. Furthermore, Receiver Operating Characteristics (ROC) curve obtained, indicating satisfactory accuracy prediction (AUC = 0.677). This provides valuable information decision-makers regarding conservation resources.

Язык: Английский

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Applications of machine learning to water resources management: A review of present status and future opportunities

Journal of Cleaner Production, Год журнала: 2024, Номер 441, С. 140715 - 140715

Опубликована: Янв. 11, 2024

Water is the most valuable natural resource on earth that plays a critical role in socio-economic development of humans worldwide. used for various purposes, including, but not limited to, drinking, recreation, irrigation, and hydropower production. The expected population growth at global scale, coupled with predicted climate change-induced impacts, warrants need proactive effective management water resources. Over recent decades, machine learning tools have been widely applied to resources management-related fields often shown promising results. Despite publication several review articles applications water-related fields, this paper presents first time comprehensive techniques management, focusing achievements. study examines potential advanced improve decision support systems sectors within realm which includes groundwater streamflow forecasting, distribution systems, quality wastewater treatment, demand consumption, marine energy, drainage flood defence. This provides an overview state-of-the-art approaches industry how they can be ensure supply sustainability, quality, drought mitigation. covers related studies provide snapshot industry. Overall, LSTM networks proven exhibit reliable performance, outperforming ANN models, traditional established physics-based models. Hybrid ML exhibited great forecasting accuracy across all showing superior computational power over ANNs architectures. In addition purely data-driven physical-based hybrid models also developed prediction performance. These efforts further demonstrate Machine powerful practical tool management. It insights, predictions, optimisation capabilities help enhance sustainable use development, healthy ecosystems human existence.

Язык: Английский

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Developing a novel tool for assessing the groundwater incorporating water quality index and machine learning approach

Groundwater for Sustainable Development, Год журнала: 2023, Номер 23, С. 101049 - 101049

Опубликована: Ноя. 1, 2023

Groundwater plays a pivotal role as global source of drinking water. To meet sustainable development goals, it is crucial to consistently monitor and manage groundwater quality. Despite its significance, there are currently no specific tools available for assessing trace/heavy metal contamination in groundwater. Addressing this gap, our research introduces an innovative approach: the Quality Index (GWQI) model, developed tested Savar sub-district Bangladesh. The GWQI model integrates ten water quality indicators, including six heavy metals, collected from 38 sampling sites study area. enhance precision assessment, employed established machine learning (ML) techniques, evaluating model's performance based on factors such uncertainty, sensitivity, reliability. A major advancement incorporation metals into framework index model. best authors knowledge, marks first initiative develop encompassing heavy/trace elements. Findings assessment revealed that area ranged 'good' 'fair,' indicating most indicators met standard limits set by Bangladesh government World Health Organization. In predicting scores, artificial neural networks (ANN) outperformed other ML models. Performance metrics, root mean square error (RMSE), (MSE), absolute (MAE) training (RMSE = 0.361; MSE 0.131; MAE 0.262), testing 0.001; 0.00; 0.001), prediction evaluation statistics (PBIAS 0.000), demonstrated superior effectiveness ANN. Moreover, exhibited high sensitivity (R2 1.0) low uncertainty (less than 2%) rating These results affirm reliability novel monitoring management, especially regarding metals.

Язык: Английский

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Hydrogeochemical assessment of groundwater quality for drinking and irrigation applying groundwater quality index (GWQI) and irrigation water quality index (IWQI)

Groundwater for Sustainable Development, Год журнала: 2023, Номер 22, С. 100958 - 100958

Опубликована: Май 11, 2023

Язык: Английский

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Assessing groundwater artificial recharge suitability in the Mi River basin using GIS, RS, and FAHP: a comprehensive analysis with seasonal variations

Applied Water Science, Год журнала: 2025, Номер 15(2)

Опубликована: Янв. 29, 2025

Язык: Английский

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A systematic review and meta-analysis of groundwater level forecasting with machine learning techniques: Current status and future directions

Environmental Modelling & Software, Год журнала: 2023, Номер 168, С. 105788 - 105788

Опубликована: Авг. 2, 2023

Язык: Английский

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Landform classification and geomorphological mapping of the Chota Nagpur Plateau, India

Quaternary Science Advances, Год журнала: 2023, Номер 10, С. 100082 - 100082

Опубликована: Март 22, 2023

Geomorphological map plays a key role to illustrate landscape evolutionary history along with the guidelines of sustainable landuse planning. The Chota Nagpur Plateau is situated in eastern part Indian subcontinent and it storehouse valuable rocks minerals Precambrian origin. Classification geomorphological units highly required for planning natural hazards management. So, principal objective this scientific study classify region into micro by applying automated semi-automated methods. Digital elevation model (DEM) data satellite imageries (from United States Geological Survey) have been used improve precision map. mapping techniques such as terrain attributes classification, Topographical Position Index (TPI) Slope (SPI) applied extract major or features. TPI values show that maximum area comes under valley bottom stream (35.26%) followed high ridge (24.55%) whereas minimum coverage found open slope zone (0.02%). Local ridges mid-slope lie 8.77% 4.94% respectively. result has verified through field verification help GPS data. This high-accuracy should be regional These also give very good results classification well landforms evolution.

Язык: Английский

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A critical review on groundwater level depletion monitoring based on GIS and data-driven models: Global perspectives and future challenges

HydroResearch, Год журнала: 2024, Номер 7, С. 285 - 300

Опубликована: Янв. 1, 2024

The present study aims to thoroughly review GWL depletion monitoring studies completed between 2000 and 2023 based on data-driven models GIS approaches from a global perspective. summarizes the details of reviewed papers, including location, period, time scale, key objective, input parameter, applied model, performance metrics, research gaps, limitations, rate. mean rate varied worldwide 2.9 ± 1.56 1100 33.76 mm/yr using 7.6 2.98 2046 45.27 GIS-based approaches. This assesses strength relationships various keywords analyzed co-author networks Vos-viewer. It proposes groundwater development strategy evaluated papers provide long-term solution water scarcity problem. Overall, this highlights existing gaps suggests potential future paths boost associated new knowledge increase accuracy

Язык: Английский

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Application of bagging and boosting ensemble machine learning techniques for groundwater potential mapping in a drought-prone agriculture region of eastern India

Environmental Sciences Europe, Год журнала: 2024, Номер 36(1)

Опубликована: Сен. 2, 2024

Groundwater is a primary source of drinking water for billions worldwide. It plays crucial role in irrigation, domestic, and industrial uses, significantly contributes to drought resilience various regions. However, excessive groundwater discharge has left many areas vulnerable potable shortages. Therefore, assessing potential zones (GWPZ) essential implementing sustainable management practices ensure the availability present future generations. This study aims delineate with high Bankura district West Bengal using four machine learning methods: Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient (XGBoost), Voting Ensemble (VE). The models used 161 data points, comprising 70% training dataset, identify significant correlations between presence absence region. Among methods, (RF) (XGBoost) proved be most effective mapping potential, suggesting their applicability other regions similar hydrogeological conditions. performance metrics RF are very good precision 0.919, recall 0.971, F1-score 0.944, accuracy 0.943. indicates strong capability accurately predict minimal false positives negatives. (AdaBoost) demonstrated comparable across all (precision: recall: F1-score: accuracy: 0.943), highlighting its effectiveness predicting accurately; whereas, outperformed slightly, higher values metrics: (0.944), (0.971), (0.958), (0.957), more refined model performance. (VE) approach also showed enhanced performance, mirroring XGBoost's 0.958, 0.957). that combining strengths individual leads better predictions. potentiality zoning varied significantly, low accounting 41.81% at 24.35%. uncertainty predictions ranged from 0.0 0.75 area, reflecting variability need targeted strategies. In summary, this highlights critical managing resources effectively advanced techniques. findings provide foundation practices, ensuring use conservation beyond.

Язык: Английский

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