Application of Machine Learning in Water Resources Management: A Systematic Literature Review

Water, Год журнала: 2023, Номер 15(4), С. 620 - 620

Опубликована: Фев. 5, 2023

In accordance with the rapid proliferation of machine learning (ML) and data management, ML applications have evolved to encompass all engineering disciplines. Owing importance world’s water supply throughout rest this century, much research has been concentrated on application strategies integrated resources management (WRM). Thus, a thorough well-organized review that is required. To accommodate underlying knowledge interests both artificial intelligence (AI) unresolved issues in WRM, overview divides core fundamentals, major applications, ongoing into two sections. First, basic are categorized three main groups, prediction, clustering, reinforcement learning. Moreover, literature organized each field according new perspectives, patterns indicated so attention can be directed toward where headed. second part, less investigated WRM addressed provide grounds for future studies. The widespread tools projected accelerate formation sustainable plans over next decade.

Язык: Английский

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Synergistic assessment of multi-scenario urban waterlogging through data-driven decoupling analysis in high-density urban areas: A case study in Shenzhen, China

Journal of Environmental Management, Год журнала: 2024, Номер 369, С. 122330 - 122330

Опубликована: Сен. 3, 2024

Язык: Английский

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Enhancing Flood Risk Mitigation by Advanced Data-Driven Approach

Heliyon, Год журнала: 2024, Номер 10(18), С. e37758 - e37758

Опубликована: Сен. 1, 2024

Язык: Английский

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Review and Intercomparison of Machine Learning Applications for Short-term Flood Forecasting

Water Resources Management, Год журнала: 2025, Номер unknown

Опубликована: Янв. 14, 2025

Abstract Among natural hazards, floods pose the greatest threat to lives and livelihoods. To reduce flood impacts, short-term forecasting can contribute early warnings that provide communities with time react. This manuscript explores how machine learning (ML) support forecasting. Using two methods [strengths, weaknesses, opportunities, threats (SWOT) comparative performance analysis] for different forecast lead times (1–6, 6–12, 12–24, 24–48 h), we evaluate of models in 94 journal papers from 2001 2023. SWOT reveals best was produced by hybrid, random forest (RF), long memory (LSTM), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) approaches. The analysis, meanwhile, favors convolutional network, ANFIS, multilayer perceptron, k-nearest neighbors algorithm (KNN), LSTM, ANN, vector (SVM) at 1–6 h; LSTM 6–12 SVM, RF 12–24 hybrid h. In general, approaches consistently perform well across all times. Trends such as hybridization, model selection, input data decomposition seem improve accuracy models. Furthermore, effective stand-alone ML RF, genetic algorithm, KNN, better outcomes through hybridization other By including parameters environmental, socio-economical, climatic parameters, produce more accurate forecasting, making it warning operational purposes.

Язык: Английский

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A smart sustainable system for flood damage management with the application of artificial intelligence and multi-criteria decision-making computations

International Journal of Disaster Risk Reduction, Год журнала: 2022, Номер 84, С. 103470 - 103470

Опубликована: Дек. 5, 2022

Язык: Английский

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Impacts of logistics service quality and energy service of Business to Consumer (B2C) online retailing on customer loyalty in a circular economy

Sustainable Energy Technologies and Assessments, Год журнала: 2022, Номер 52, С. 102333 - 102333

Опубликована: Июнь 7, 2022

Язык: Английский

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Low-energy residential building optimisation for energy and comfort enhancement in semi-arid climate conditions

Energy Conversion and Management, Год журнала: 2023, Номер 291, С. 117264 - 117264

Опубликована: Июнь 17, 2023

The application of energy-efficient strategies in buildings, such as the Green Building Concept, can significantly impact human comfort and resource consumption. However, due to complexity decision-making factors variety available materials, computational models are necessary identify most effective solutions optimise building energy performance. This study presents an integrated framework that uses machine learning algorithms a Petri Net control system thermal, comfort, efficiency both vertical horizontal envelopes semi-arid climate zones. incorporates several passive techniques for parameters, including material thickness melting point, window types, wall insulation thermal emissivity, solar absorbance, ratio, fenestration position, air tightness, roof reflectance, conductivity (W/(m·°C)), floor thickness. An experiment design was developed using Box-Behnken Design-Response Surface Methodology (BBD-RSM) statistical optimisation, which coupled with Design Builder simulation model. methodology demonstrated by applying it residential Mexico. Meta Additive Regression used analyse output factors, showed higher confidence compared REP Tree M5P green buildings. results demonstrate annual reduction 50 kW/m2 per household be achieved optimised envelope.

Язык: Английский

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Elderly people evacuation planning in response to extreme flood events using optimisation-based decision-making systems: A case study in western Sydney, Australia

Knowledge-Based Systems, Год журнала: 2023, Номер 274, С. 110629 - 110629

Опубликована: Май 13, 2023

Язык: Английский

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Enhancing community resilience in arid regions: A smart framework for flash flood risk assessment

Ecological Indicators, Год журнала: 2023, Номер 153, С. 110457 - 110457

Опубликована: Июнь 15, 2023

This paper presents a novel framework for smart integrated risk management in arid regions. The combines flash flood modelling, statistical methods, artificial intelligence (AI), geographic evaluations, analysis, and decision-making modules to enhance community resilience. Flash is simulated by using Watershed Modelling System (WMS). Statistical methods are also used trim outlier data from physical systems climatic data. Furthermore, three AI including Support Vector Machine (SVM), Artificial Neural Network (ANN), Nearest Neighbours Classification (NNC), predict classify occurrences. Geographic Information (GIS) utilised assess potential risks vulnerable regions, together with Failure Mode Effects Analysis (FMEA) Hazard Operability Study (HAZOP) methods. module employs the Classic Delphi technique appropriate solutions control. methodology demonstrated its application real case study of Khosf region Iran, which suffers both drought severe floods simultaneously, exacerbated recent climate changes. results show high Coefficient determination (R2) scores SVM at 0.88, ANN 0.79, NNC 0.89. FMEA indicate that over 50% scenarios risk, while HAZOP indicates 30% same rate. Additionally, peak flows 24 m3/s considered occurrences can cause financial damage all techniques study. Finally, our research findings practical decision support system compatible sustainable development concepts resilience

Язык: Английский

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Sustainable Water Infrastructure: Visions and Options for Sub-Saharan Africa

Sustainability, Год журнала: 2024, Номер 16(4), С. 1592 - 1592

Опубликована: Фев. 14, 2024

Developing a sustainable water infrastructure entails the planning and management of systems to ensure availability, access, quality, affordability resources in face social, environmental, economic challenges. Sub-Saharan Africa (SSA) is currently an era where it must make significant changes improve sustainability its infrastructure. This paper reviews factors affecting interventions taken globally address these In parallel, reflects on relevance context through lens STEEP (societal, technological, economic, political) framework. The goes recommend extended analysis that captures additional critical dimensions when applying concept sustainability. Furthermore, this sheds light practice development fosters deeper understanding issues, thereby forming basis for further research resilient solutions asset more generally.

Язык: Английский

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