Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions

Journal of Hydrology, Год журнала: 2023, Номер 628, С. 130458 - 130458

Опубликована: Ноя. 15, 2023

Язык: Английский

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A sophisticated model for rating water quality

The Science of The Total Environment, Год журнала: 2023, Номер 868, С. 161614 - 161614

Опубликована: Янв. 18, 2023

Here, we present the Irish Water Quality Index (IEWQI) model for assessing transitional and coastal water quality in an effort to improve method develop a tool that can be used by environmental regulators abate pollution Ireland. The developed has been associated with adoption of standards formulated waterbodies according framework directive legislation regulator water. consists five identical components, including (i) indicator selection technique is select crucial indicator; (ii) sub-index (SI) function rescaling various indicators' information into uniform scale; (iii) weight estimating values based on relative significance real-time quality; aggregation computing index (WQI) score; (v) score interpretation scheme state quality. IEWQI was Cork Harbour, applied four Ireland, using 2021 data summer winter seasons order evaluate sensitivity terms spatio-temporal resolution waterbodies. efficiency uncertainty were also analysed this research. In different magnitudes domains, shows higher application domains during winter. addition, results reveal architecture may effective reducing avoid eclipsing ambiguity problems. findings study could efficient reliable assessment more accurately any geospatial domain.

Язык: Английский

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Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 129, С. 107559 - 107559

Опубликована: Дек. 3, 2023

Язык: Английский

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Hybrid WT–CNN–GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features

Journal of Environmental Management, Год журнала: 2024, Номер 358, С. 120756 - 120756

Опубликована: Апрель 9, 2024

Язык: Английский

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A stacking ANN ensemble model of ML models for stream water quality prediction of Godavari River Basin, India

Ecological Informatics, Год журнала: 2024, Номер 80, С. 102500 - 102500

Опубликована: Янв. 28, 2024

The importance of water quality models has increased as their inputs are critical to the development risk assessment framework for environmental management and monitoring rivers. However, with advent a plethora recent advances in ML algorithms better predictions possible. This study proposes causal effect model by considering climatological such temperature precipitation along geospatial information related agricultural land use factor (ALUF), forest (FLUF), grassland usage (GLUF), shrub (SLUF), urban (ULUF). All these factors included input data, whereas four Stream Water Quality parameters (SWQPs) Electrical Conductivity (EC), Biochemical Oxygen Demand (BOD), Nitrate, Dissolved (DO) from 2019 2021 taken outputs predict Godavari River Basin quality. In preliminary investigation, out SWQPs, nitrate's coefficient variation (CV) is high, revealing close association climate practices across sampling stations. authors' earlier study, using single-layer Feed-Forward Neural Network (FFNN) showed improved performance predicting cause linked metrics. To achieve prediction, stacked ANN meta-model nine conventional machine learning (ML) models, including Extreme Gradient Boosting (XGB), Extra Trees (ET), Bagging (BG), Random Forest (RF), AdaBoost or Adaptive (ADB), Decision Tree (DT), Highest (HGB), Light Method (LGBM), (GB), were compared this study. According study's findings, outperformed stand-alone FFNN same dataset superior predictive capabilities terms accuracy forecasting variable interest. For instance, during testing, determination (R2) (BOD) 0.72 0.87. Furthermore, Artificial (ANN) meta that was reinforced (ET) base performed than individual (from R2 = 0.87 0.91 BOD testing). By new framework, effort hyperparameter tuning can be minimized.

Язык: Английский

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Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolis

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 58, С. 102227 - 102227

Опубликована: Фев. 17, 2025

Язык: Английский

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Prediction model of drinking water source quality with potential industrial-agricultural pollution based on CNN-GRU-Attention

Journal of Hydrology, Год журнала: 2022, Номер 610, С. 127934 - 127934

Опубликована: Май 17, 2022

Язык: Английский

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Prediction of Streamflow Drought Index for Short-Term Hydrological Drought in the Semi-Arid Yesilirmak Basin Using Wavelet Transform and Artificial Intelligence Techniques

Sustainability, Год журнала: 2023, Номер 15(2), С. 1109 - 1109

Опубликована: Янв. 6, 2023

The prediction of hydrological droughts is vital for surface and ground waters, reservoir levels, hydroelectric power generation, agricultural production, forest fires, climate change, the survival living things. This study aimed to forecast 1-month lead-time in Yesilirmak basin. For this purpose, support vector regression, Gaussian process regression tree, ensemble tree models were used alone combination with a discrete wavelet transform. Streamflow drought index values determine droughts. data divided into 70% training (1969–1998) 30% (1999–2011) testing. performance was evaluated according various statistical criteria such as mean square error, root means absolute determination coefficient. As result, it determined that obtained by decomposing subcomponents transform optimal. In addition, most effective drought-predicting model using db10 MGPR algorithm squared error 0.007, 0.08, 0.04, coefficient (R2) 0.99 at station 1413. weakest stand-alone FGSV (RMSE 0.88, RMSE 0.94, MAE 0.76, R2 0.14). Moreover, revealed main more accurate predicting short-term than other wavelets. These results provide essential information decision-makers planners manage

Язык: Английский

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Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation

Land, Год журнала: 2023, Номер 12(5), С. 1018 - 1018

Опубликована: Май 5, 2023

(1) Background: The aim of this paper was to study landslide susceptibility mapping based on interpretable machine learning from the perspective topography differentiation. (2) Methods: This selects three counties (Chengkou, Wushan and Wuxi counties) in northeastern Chongqing, delineated as corrosion layered high middle mountain region (Zone I), (Wulong, Pengshui Shizhu southeastern mountainous strong karst gorges II), area. used a Bayesian optimization algorithm optimize parameters LightGBM XGBoost models construct evaluation for each two regions. model with accuracy selected according indicators order establish mapping. SHAP then explore formation mechanisms different landforms both global local perspective. (3) Results: AUC values test set mode Zones I II are 0.8525 0.8859, respectively, those 0.8214 0.8375, respectively. shows that has prediction regard landforms. Under landform types, elevation, land use, incision depth, distance road average annual rainfall were common dominant factors contributing most decision making at sites; fault river have degrees influence under types. (4) Conclusions: optimized LightGBM-SHAP is suitable analysis types landscapes, namely region, gorges, can be internal decision-making mechanism levels, which makes results more realistic transparent. beneficial selection index system early prevention control hazards, provide reference potential hazard-prone areas research.

Язык: Английский

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New-generation machine learning models as prediction tools for modeling interfacial tension of hydrogen-brine system

International Journal of Hydrogen Energy, Год журнала: 2023, Номер 50, С. 1326 - 1337

Опубликована: Окт. 4, 2023

Язык: Английский

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