Critical role of climate factors for groundwater potential mapping in arid regions: Insights from random forest, XGBoost, and LightGBM algorithms

Journal of Hydrology, Год журнала: 2023, Номер 621, С. 129599 - 129599

Опубликована: Май 1, 2023

Язык: Английский

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Critical review on water quality analysis using IoT and machine learning models

International Journal of Information Management Data Insights, Год журнала: 2024, Номер 4(1), С. 100210 - 100210

Опубликована: Янв. 4, 2024

Water quality and its management are the most precise concerns confronting humanity globally. This article evaluates various sensors used for water monitoring focuses on index considering multiple physical, chemical, biological parameters. A Review of Internet Things (IoT) research analysis, can help remote parameters using IoT-based that convey assembled estimations utilizing Low-Power Wide Area Network innovations. Overall, IoT system was 95 % accurate in measuring pH, Turbidity, TDS, Temperature, while traditional method only 85 accurate. Also, this study reviewed different A.I. techniques to assess quality, including conventional machine learning techniques, Support Vector Machines, Deep Neural Networks, K-nearest neighbors. Compared methods, deep significantly increase accuracy measurements groundwater quality. However, variables, such as caliber training data, metrics' complexity, frequency, will affect accuracy. The geographical information (GIS) is spatial data analysis managing resources. also paper. Based these analyses, has forecasted future sensors, Geospatial Technology, analysis.

Язык: Английский

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Evaluation of groundwater potential using ANN-based mountain gazelle optimization: A framework to achieve SDGs in East El Oweinat, Egypt

Journal of Hydrology Regional Studies, Год журнала: 2024, Номер 52, С. 101703 - 101703

Опубликована: Фев. 12, 2024

A pilot case study in East El Oweinat (PCSEO), Egypt. An artificial neural network (ANN)-based mountain gazelle optimization (MGO) model was applied to map groundwater potential zones (GWPZs). For this purpose, ten layers affecting occurrence were prepared and normalized against the drawdown (DD) map. All data divided into 70:30 for training testing. After that, sensitivity analysis adopted verify relative importance (RI) of layers. The accuracy GWPZs checked using receiver operating characteristic (ROC) curve other statistical indicators. finally propose a sustainable strategy exploration by implementing integrated MODFLOW-USG MGO framework. Over 40% PCSEO revealed high very degrees situated mostly on southwestern side. Sensitivity that significantly affected table (GWT), well density (WD), land use (LU). results also indicated ANN-based performed with an area under (AUC) ∼ 90% compared conventional models. Additionally, MODFLOW-USG-based gave spatial distribution optimal discharge well-depth zones. This finding could match SDGs relevant ending poverty, affordable groundwater, life land.

Язык: Английский

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Improving groundwater quality predictions in semi-arid regions using ensemble learning models

Environmental Science and Pollution Research, Год журнала: 2025, Номер unknown

Опубликована: Янв. 4, 2025

Язык: Английский

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Convolutional neural network and long short-term memory algorithms for groundwater potential mapping in Anseong, South Korea

Journal of Hydrology Regional Studies, Год журнала: 2022, Номер 39, С. 100990 - 100990

Опубликована: Янв. 12, 2022

The study area was the Anseong-si that located in southernmost part of Gyeonggi-do Province at 127°19′ E, 36°82′ N. Anseong has a transitional climate between north and south regions. Its is characterized by geographical conditions forming expansive plains stretch from Charyeong Range. entire city surrounded many high low mountains to west, late-middle age old hills are spread, while there due development rivers. In this study, machine learning algorithms were used based on convolutional neural network (CNN) long short-term memory (LSTM) generate groundwater potential map Anseong, South Korea. A total 295 wells locations divided median value transmissivity data (T) produced "1" point with productivity "0" as into 70:30 for training validation model. 14 related factors such topo-hydrological geo-environmental define spatial correlation data. model evaluated using receiver operating characteristics (ROC) curve analysis method. under ROC (AUC) calculated test results shows good accuracy, AUC values all higher than 0.8. Finally, maps generated CNN LSTM can be analyze areas potentially provided This could help local environment manage resources assist planners decision-makers sustainability planning.

Язык: Английский

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Spatial prediction of groundwater potential and driving factor analysis based on deep learning and geographical detector in an arid endorheic basin

Ecological Indicators, Год журнала: 2022, Номер 142, С. 109256 - 109256

Опубликована: Авг. 9, 2022

Язык: Английский

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Novel Ensemble Machine Learning Modeling Approach for Groundwater Potential Mapping in Parbhani District of Maharashtra, India

Water, Год журнала: 2023, Номер 15(3), С. 419 - 419

Опубликована: Янв. 19, 2023

Groundwater is an essential source of water especially in arid and semi-arid regions the world. The demand for due to exponential increase population has created stresses on available groundwater resources. Further, climate change affected quantity globally. Many parts Indian cities are experiencing scarcity. Thus, assessment potential necessary sustainable utilization management We utilized a novel ensemble approach using artificial neural network multi-layer perceptron (ANN-MLP), random forest (RF), M5 prime (M5P) support vector machine regression (SMOReg) models assessing Parbhani district Maharashtra India. Ten site-specific influencing factors, elevation, slope, aspect, drainage density, rainfall, table depth, lineament land use cover, geomorphology, soil types, were integrated preparation zones. results revealed that largest area was found under moderate category GWP zone followed by poor, good, very good poor. Spatial distribution zones showed Poor GWPZs spread over north, central southern district. Very poor mostly north-western study calls policy implications conserve manage these parts. ensembled model proved be effective outcome may help stakeholders efficiently utilize devise suitable strategies its management. Other geographical find methodology adopted this assessment.

Язык: Английский

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Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr Basin

Water, Год журнала: 2023, Номер 15(6), С. 1182 - 1182

Опубликована: Март 18, 2023

A water supply is vital for preserving usual human living standards, industrial development, and agricultural growth. Scarce supplies unplanned urbanization are the primary impediments to results in dry environments. Locating suitable sites artificial groundwater recharge (AGR) could be a strategic priority countries groundwater. Recent advances machine learning (ML) techniques provide valuable tools producing an AGR site suitability map (AGRSSM). This research developed ML algorithm identify most appropriate location Iranshahr, one of major districts East Iran characterized by severe drought excessive consumption. The area’s undue reliance on resources has resulted aquifer depletion socioeconomic problems. Nine digitized georeferenced data layers have been considered preparing AGRSSM, including precipitation, slope, geology, unsaturated zone thickness, land use, distance from main rivers, quality, transmissivity soil. AGRSSM was trained validated using 1000 randomly selected points across study area with accuracy 97%. By comparing proposed those other methods, it discovered that intelligence method accurately determine sites. In summary, this uses novel approach optimal algorithms. Our findings practical implications policymakers resource managers looking address problem Iranshahr regions facing similar challenges. Future explore applicability our examine potential economic benefits

Язык: Английский

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Assessment of groundwater potential and determination of influencing factors using remote sensing and machine learning algorithms: A study of Nainital district of Uttarakhand state, India

Groundwater for Sustainable Development, Год журнала: 2024, Номер 25, С. 101094 - 101094

Опубликована: Янв. 24, 2024

Язык: Английский

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Harnessing Machine Learning for Assessing Climate Change Influences on Groundwater Resources: A Comprehensive Review

Heliyon, Год журнала: 2024, Номер 10(17), С. e37073 - e37073

Опубликована: Авг. 28, 2024

Язык: Английский

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