Comparative Assessment of Machine Learning Models for Groundwater Quality Prediction Using Various Parameters

Environmental Processes, Journal Year: 2025, Volume and Issue: 12(1)

Published: Feb. 11, 2025

Language: Английский

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Artificial Intelligence in Agricultural Mapping: A Review

Agriculture, Journal Year: 2024, Volume and Issue: 14(7), P. 1071 - 1071

Published: July 3, 2024

Artificial intelligence (AI) plays an essential role in agricultural mapping. It reduces costs and time increases efficiency management activities, which improves the food industry. Agricultural mapping is necessary for resource requires technologies farming challenges. The AI applications gives its subsequent use decision-making. This study analyses AI’s current state through bibliometric indicators a literature review to identify methods, resources, geomatic tools, types, their management. methodology begins with bibliographic search Scopus Web of Science (WoS). Subsequently, data analysis establish scientific contribution, collaboration, trends. United States (USA), Spain, Italy are countries that produce collaborate more this area knowledge. Of studies, 76% machine learning (ML) 24% deep (DL) applications. Prevailing algorithms such as Random Forest (RF), Neural Networks (ANNs), Support Vector Machines (SVMs) correlate activities In addition, contributes associated production, disease detection, crop classification, rural planning, forest dynamics, irrigation system improvements.

Language: Английский

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Tracing spatial patterns of lacustrine groundwater discharge in a closed inland lake using stable isotopes

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 354, P. 120305 - 120305

Published: Feb. 14, 2024

Language: Английский

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Spatiotemporal drivers of urban water pollution: Assessment of 102 cities across the Yangtze River Basin

Environmental Science and Ecotechnology, Journal Year: 2024, Volume and Issue: 20, P. 100412 - 100412

Published: March 11, 2024

Effective management of large basins necessitates pinpointing the spatial and temporal drivers primary index exceedances urban risk factors, offering crucial insights for basin administrators. Yet, comprehensive examinations multiple pollutants within Yangtze River Basin remain scarce. Here we introduce a pollution inventory clusters surrounding Basin, analyzing water quality data from 102 cities during 2018–2019. We assessed exceedance rates six pivotal indicators: dissolved oxygen (DO), ammonia nitrogen (NH3–N), chemical demand (COD), biochemical (BOD), total phosphorus (TP), permanganate (CODMn) each city. Employing random forest regression SHapley Additive exPlanations (SHAP) analyses, identified spatiotemporal factors influencing these key indicators. Our results highlight agricultural activities as contributors to all indicators, thus them leading source in basin. Additionally, coverage, livestock farming, pharmaceutical sectors, along with meteorological elements like precipitation temperature, significantly impacted various indicators' exceedances. Furthermore, delineate five core components through principal component analysis, which are (1) anthropogenic industrial activities, (2) practices extent, (3) climatic variables, (4) rearing, (5) polluting sectors. The were subsequently evaluated categorized based on components, incorporating policy interventions administrative performance region. analysis advocates customized strategy addressing discerned especially presenting elevated levels.

Language: Английский

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Using game theory algorithm to identify critical watersheds based on environmental flow components and hydrological indicators

Environment Development and Sustainability, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 9, 2024

Language: Английский

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Integration of Watershed eco-physical health through Algorithmic game theory and supervised machine learning

Groundwater for Sustainable Development, Journal Year: 2024, Volume and Issue: 26, P. 101216 - 101216

Published: May 31, 2024

The eco-physical health assessment of watersheds is crucial for sustainable water resource management and ecosystem services. This study quantifies the Talar watershed in Iran using geometric mean method (GMM), game-theoretic algorithm (GTA), machine learning algorithms including Random Forest (RF), Support Vector Machine (SVM), Simple Linear Regression (SLR), K-Nearest Neighbor (KNN) distributed semi-distributed monitoring. results show that RF performed better than other models, as indicated by MAE, MSE, RMSE, AUC statistics with values 0.032, 0.003, 0.058, 0.940, respectively. index prioritization different approaches showed pattern changes positively from upstream to downstream. Based on GMM, it can be said sub-watersheds Int6 Int5 are healthiest studied watershed, 0.93 0.90, GTA approach, also Int6, Int5, Int01 ones. In case algorithm, average pixels each sub-watershed were recognized 0.91 0.88, consistently emerged across all methods, attributed high TWI NDVI low slope, DEM, erosion, CN values. general, catchment fully followed factors affecting catchment's spatial patterns change this consistent physiographic hydroclimatic conditions three approaches. study's implications underline importance multi-criteria multi-algorithm accurately assessing managing development.

Language: Английский

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Groundwater quality modeling and determining critical points: a comparison of machine learning to Best–Worst Method

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(54), P. 115758 - 115775

Published: Oct. 27, 2023

Language: Английский

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Machine learning modeling of base flow generation potential: A case study of the combined application of BWM and Fallback bargaining algorithm

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 636, P. 131220 - 131220

Published: April 19, 2024

Language: Английский

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Conjunct applicability of MCDM-based machine learning algorithms in mapping the sediment formation potential

Environment Development and Sustainability, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 13, 2024

Abstract This study evaluates the applicability of multicriteria decision-making (MCDM) methods, including SAW, VIKOR, TOPSIS, and Condorcet algorithm based on game theory machine learning algorithms (MLAs) K-nearest neighbor, Naïve Bayes, Random Forest (RF), simple linear regression support vector in spatial mapping sediment formation potential Talar watershed, Iran. In first approach, MCDM was used, Condorcet’s theory. To this end, a decision matrix for created factors affecting potential. next step, various MLAs were used to construct distribution map Finally, constructed very low high classes. The summary results prioritizing sub-basins using multi-criteria methods showed that sub-basin SW12 had highest methods. modeling different values error statistics, RF with MAE = 0.032, MSE 0.024, RMSE 0.155, AUC 0.930 selected as most optimal algorithm. On other side, correlation Taylor diagram (Figs. 10 11) also slope factor value 0.84. Also, LS coefficient 0.65 after model modeling. shows amount increases from downstream upstream side watershed.

Language: Английский

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Changes in the characteristics of water quality parameters under the influence of dam construction

Environment Development and Sustainability, Journal Year: 2024, Volume and Issue: unknown

Published: May 8, 2024

Language: Английский

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