Research on Water Resource Modeling Based on Machine Learning Technologies

Water, Journal Year: 2024, Volume and Issue: 16(3), P. 472 - 472

Published: Jan. 31, 2024

Water resource modeling is an important means of studying the distribution, change, utilization, and management water resources. By establishing various models, resources can be quantitatively described predicted, providing a scientific basis for management, protection, planning. Traditional hydrological observation methods, often reliant on experience statistical are time-consuming labor-intensive, frequently resulting in predictions limited accuracy. However, machine learning technologies enhance efficiency sustainability by analyzing extensive hydrogeological data, thereby improving optimizing utilization allocation. This review investigates application predicting aspects, including precipitation, flood, runoff, soil moisture, evapotranspiration, groundwater level, quality. It provides detailed summary algorithms, examines their technical strengths weaknesses, discusses potential applications modeling. Finally, this paper anticipates future development trends to

Language: Английский

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Exploring forest fire susceptibility and management strategies in Western Himalaya: Integrating ensemble machine learning and explainable AI for accurate prediction and comprehensive analysis

Environmental Technology & Innovation, Journal Year: 2024, Volume and Issue: 35, P. 103655 - 103655

Published: May 5, 2024

Forest fires pose a significant threat to ecosystems and socio-economic activities, necessitating the development of accurate predictive models for effective management mitigation. In this study, we present novel machine learning approach combined with Explainable Artificial Intelligence (XAI) techniques predict forest fire susceptibility in Nainital district. Our innovative methodology integrates several robust — AdaBoost, Gradient Boosting Machine (GBM), XGBoost Random Deep Neural Network (DNN) as meta-model stacking framework. This not only utilises individual strengths these models, but also improves overall prediction performance reliability. By using XAI techniques, particular SHAP (SHapley Additive exPlanations) LIME (Local Interpretable Model-agnostic Explanations), improve interpretability provide insights into decision-making processes. results show effectiveness ensemble model categorising different zones: very low, moderate, high high. particular, identified extensive areas susceptibility, precision, recall F1 values underpinning their effectiveness. These achieved ROC AUC above 0.90, performing exceptionally well an 0.94. The are remarkably inclusion confidence intervals most important metrics all emphasises robustness reliability supports practical use management. Through summary plots, analyze global variable importance, revealing annual rainfall Evapotranspiration (ET) key factors influencing susceptibility. Local analysis consistently highlights importance rainfall, ET, distance from roads across models. study fills research gap by providing comprehensive interpretable modelling that our ability effectively manage risk is consistent environmental protection sustainable goals.

Language: Английский

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Assessing the impact of COVID-19 lockdown on surface water quality in Ireland using advanced Irish water quality index (IEWQI) model

Environmental Pollution, Journal Year: 2023, Volume and Issue: 336, P. 122456 - 122456

Published: Sept. 4, 2023

The COVID-19 pandemic has significantly impacted various aspects of life, including environmental conditions. Surface water quality (WQ) is one area affected by lockdowns imposed to control the virus's spread. Numerous recent studies have revealed considerable impact on surface WQ. In response, this research aimed assess in Ireland using an advanced WQ model. To achieve goal, six years monitoring data from 2017 2022 were collected for nine indicators Cork Harbour, Ireland, before, during, and after lockdowns. These include pH, temperature (TEMP), salinity (SAL), biological oxygen demand (BOD5), dissolved (DOX), transparency (TRAN), three nutrient enrichment indicators-dissolved inorganic nitrogen (DIN), molybdate reactive phosphorus (MRP), total oxidized (TON). results showed that lockdown had a significant indicators, particularly TEMP, TON, BOD5. Over study period, most within permissible limit except MRP, with exception during COVID-19. During pandemic, TON DIN decreased, while improved. contrast, COVID-19, at 7% sites deteriorated. Overall, Harbour was categorized as "good," "fair," "marginal" classes over period. Compared temporal variation, improved 17% period Harbour. However, no trend observed. Furthermore, analyzed model's performance assessing indicate model could be effective tool evaluating lockdowns' quality. can provide valuable information decision-making planning protect aquatic ecosystems.

Language: Английский

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Comparison between the WFD approaches and newly developed water quality model for monitoring transitional and coastal water quality in Northern Ireland

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 901, P. 165960 - 165960

Published: Aug. 3, 2023

This study aims to evaluate existing approaches for monitoring and assessing water quality in waterbodies the North of Ireland using newly developed methodologies. The results reveal significant differences between new technique "one-out, all-out" approach rating quality. found status be "good," "fair," "marginal," whereas classified as "moderate," respectively. outperformed different waterbody types, with high R2 = 1, NSE 0.99, MEF 0 values. Furthermore, final assessment methodologies had lowest uncertainty (<1 %), efficiency measures (NSE MEF) indicate that are bias-free assess at any geographic scale. this proposed effective states transitional coastal Ireland. also highlighted limitations importance updating resource management systems better protection these waterbodies. findings have implications planning other similar regions.

Language: Английский

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Coupling Machine and Deep Learning with Explainable Artificial Intelligence for Improving Prediction of Groundwater Quality and Decision-Making in Arid Region, Saudi Arabia

Water, Journal Year: 2023, Volume and Issue: 15(12), P. 2298 - 2298

Published: June 20, 2023

Recently, machine learning (ML) and deep (DL) models based on artificial intelligence (AI) have emerged as fast reliable tools for predicting water quality index (WQI) in various regions worldwide. In this study, we propose a novel stacking framework DL WQI prediction, employing convolutional neural network (CNN) model. Additionally, introduce explainable AI (XAI) through XGBoost-based SHAP (SHapley Additive exPlanations) values to gain valuable insights that can enhance decision-making strategies management. Our findings demonstrate the model achieves highest accuracy prediction (R2: 0.99, MAPE: 15.99%), outperforming CNN 0.90, 58.97%). Although shows relatively high R2 value, other statistical measures indicate it is actually worst-performing among five tested. This discrepancy may be attributed limited training data available Furthermore, application of techniques, specifically values, allows us into extract information management purposes. The interaction plot reveal elevated levels total dissolved solids (TDS), zinc, electrical conductivity (EC) are primary drivers poor quality. These parameters exhibit nonlinear relationship with index, implying even minor increases their concentrations significantly impact Overall, study presents comprehensive integrated approach management, emphasizing need collaborative efforts all stakeholders mitigate pollution uphold By leveraging XAI, our proposed not only provides powerful tool accurate but also offers models, enabling informed strategies.

Language: Английский

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Comparison of strategies for multistep-ahead lake water level forecasting using deep learning models

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 444, P. 141228 - 141228

Published: Feb. 13, 2024

Language: Английский

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Smarter water quality monitoring in reservoirs using interpretable deep learning models and feature importance analysis

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 60, P. 105187 - 105187

Published: April 1, 2024

Language: Английский

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Surface water quality prediction in the lower Thoubal river watershed, India: A hyper-tuned machine learning approach and DNN-based sensitivity analysis

Journal of environmental chemical engineering, Journal Year: 2024, Volume and Issue: 12(3), P. 112915 - 112915

Published: May 3, 2024

Language: Английский

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Sensitivity analysis-driven machine learning approach for groundwater quality prediction: Insights from integrating ENTROPY and CRITIC methods

Groundwater for Sustainable Development, Journal Year: 2024, Volume and Issue: 26, P. 101309 - 101309

Published: Aug. 1, 2024

Language: Английский

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Drivers analysis and future scenario-based predictions of nutrient loads in key lakes and reservoirs of the Yangtze River Catchment

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 374, P. 124078 - 124078

Published: Jan. 11, 2025

Language: Английский

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