A Bibliometric Analysis of Trends in Rainfall-Runoff Modeling Techniques for Urban Flood Mitigation (2005-2024)

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104927 - 104927

Опубликована: Апрель 1, 2025

Язык: Английский

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Hybrid deep learning downscaling of GCMs for climate impact assessment and future projections in Oman

Journal of Environmental Management, Год журнала: 2025, Номер 376, С. 124522 - 124522

Опубликована: Фев. 15, 2025

Язык: Английский

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Coupling SWAT and Transformer Models for Enhanced Monthly Streamflow Prediction

Sustainability, Год журнала: 2024, Номер 16(19), С. 8699 - 8699

Опубликована: Окт. 9, 2024

The establishment of an accurate and reliable predictive model is essential for water resources planning management. Standalone models, such as physics-based hydrological models or data-driven have their specific applications, strengths, limitations. In this study, a hybrid (namely SWAT-Transformer) was developed by coupling the Soil Water Assessment Tool (SWAT) with Transformer to enhance monthly streamflow prediction accuracy. SWAT first constructed calibrated, then its outputs are used part inputs Transformer. By correcting errors using Transformer, two effectively coupled. Monthly runoff data at Yan’an Ganguyi stations on Yan River, first-order tributary Yellow River Basin, were evaluate proposed model’s performance. results indicated that performed well in predicting high flows but poorly low flows. contrast, able capture low-flow period information more accurately outperformed overall. SWAT-Transformer could correct predictions overcome limitations single model. integrating SWAT’s detailed physical process portrayal Transformer’s powerful time-series analysis, coupled significantly improved offer optimal resource management, which crucial sustainable economic societal development.

Язык: Английский

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Research on a Non-Stationary Groundwater Level Prediction Model Based on VMD-iTransformer and Its Application in Sustainable Water Resource Management of Ecological Reserves

Sustainability, Год журнала: 2024, Номер 16(21), С. 9185 - 9185

Опубликована: Окт. 23, 2024

The precise forecasting of groundwater levels significantly influences plant growth and the sustainable management ecosystems. Nonetheless, non-stationary characteristics level data often hinder current deep learning algorithms from precisely capturing variations in levels. We used Variational Mode Decomposition (VMD) an enhanced Transformer model to address this issue. Our objective was develop a called VMD-iTransformer, which aims forecast level. This research nine monitoring stations located Hangjinqi Ecological Reserve Kubuqi Desert, China, as case studies over four months. To enhance predictive performance we introduced novel approach fluctuations Desert region. technique achieve predictions conditions. Compared with classic model, our more effectively captured non-stationarity prediction accuracy by 70% test set. novelty lies its initial decomposition multimodal signals using adaptive approach, followed reconfiguration conventional model’s structure (via self-attention inversion feed-forward neural network (FNN)) challenge multivariate time prediction. Through evaluation results, determined that method had mean absolute error (MAE) 0.0251, root square (RMSE) 0.0262, percentage (MAPE) 1.2811%, coefficient determination (R2) 0.9287. study validated VMD iTransformer offering modeling for predicting context, thereby aiding water resource ecological reserves. VMD-iTransformer enhances projections level, facilitating reasonable distribution resources long-term preservation ecosystems, providing technical assistance ecosystems’ vitality regional development.

Язык: Английский

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Cascaded neural network surrogate modeling for real-time decision-making in long-distance water supply distribution

Engineering Applications of Computational Fluid Mechanics, Год журнала: 2025, Номер 19(1)

Опубликована: Янв. 16, 2025

Effective water distribution in long-distance supply systems requires precise control over pump station operations and flow-regulating elements, such as speeds valve openings, typically achieved through hydraulic models. However, traditional models are time-intensive to develop require frequent calibration, limiting their practicality for real-time applications. This paper presents a cascaded neural network (CNN) model that integrates classification regression components serve an efficient surrogate decision-making. In the proposed CNN model, component identifies number of pumps needed meet system flow demands, while predicts target values openings. Considering nonlinear relationship between rate regulating error was introduced evaluation metric via Orthogonal-Triangular (QR) decomposition. The model's performance robustness were validated using data from actual system, including analyses its sensitivity uncertainties reservoir level measurements. Results demonstrate achieves more accurate predictions compared pure networks. Furthermore, uncertainty analysis reveals is less affected by measurement errors, it sensitive underscoring importance monitoring practical

Язык: Английский

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Enhancing Long-Term Flood Forecasting with SageFormer: A Cascaded Dimensionality Reduction Approach Based on Satellite-Derived Data

Remote Sensing, Год журнала: 2025, Номер 17(3), С. 365 - 365

Опубликована: Янв. 22, 2025

Floods, increasingly exacerbated by climate change, are among the most destructive natural disasters globally, necessitating advancements in long-term forecasting to improve risk management. Traditional models struggle with complex dependencies of hydroclimatic variables and environmental conditions, thus limiting their reliability. This study introduces a novel framework for enhancing flood accuracy integrating geo-spatiotemporal analyses, cascading dimensionality reduction, SageFormer-based multi-step-ahead predictions. The efficiently processes satellite-derived data, addressing curse focusing on critical long-range spatiotemporal dependencies. SageFormer captures inter- intra-dependencies within compressed feature space, making it particularly effective forecasting. Performance evaluations against LSTM, Transformer, Informer across three data fusion scenarios reveal substantial improvements accuracy, especially data-scarce basins. integration hydroclimate attention-based networks reduction demonstrates significant over traditional approaches. proposed combines advanced deep learning, both interpretability precision capturing By offering straightforward reliable approach, this advances remote sensing applications hydrological modeling, providing robust tool mitigating impacts extremes.

Язык: Английский

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A spatiotemporal watershed-scale machine-learning model for hourly and daily flood-water level prediction: the case of the tidal Beigang River, Taiwan

Natural Hazards, Год журнала: 2025, Номер unknown

Опубликована: Март 3, 2025

Язык: Английский

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Middle‐ and Long‐Term Runoff Forecast Model for Water Resource and Climate Security Based on Self‐Attention Mechanism

Land Degradation and Development, Год журнала: 2025, Номер unknown

Опубликована: Фев. 28, 2025

ABSTRACT Reliable middle‐ and long‐term streamflow forecasts are critical for ensuring water resources management climate security. This study establishes a novel runoff forecasting model based on the self‐attention (SA) mechanism variational mode decomposition‐gated recurrent unit (VMD‐GRU) framework to improve monthly prediction accuracy. The maximal information coefficient (MIC) method is adopted screen key drivers of variability. proposed integrates VMD decompose sequence into intrinsic components applies GRU coupled with SA predict each component. whale optimization algorithm (WOA) VMD‐SA‐GRU hyperparameters, then forecast results obtained by superimposing Using 40 years data from South‐to‐North Water Diversion Project in China, evaluated against VMD‐GRU benchmarks. Results demonstrate that leverages strengths its constituent algorithms, significantly improving Compared model, enhances Nash‐Sutcliff efficiency (NSE) 6%–35%, reduces root mean square error (RMSE) 15%–30%, decreases absolute (MAE) 15%–33%. robust provides reliable tool sustainable resource addressing climate‐related challenges.

Язык: Английский

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Long Short-Term Memory (LSTM) Networks for Accurate River Flow Forecasting: A Case Study on the Morava River Basin (Serbia)

Water, Год журнала: 2025, Номер 17(6), С. 907 - 907

Опубликована: Март 20, 2025

Accurate forecasting of river flows is essential for effective water resource management, flood risk reduction and environmental protection. The ongoing effects climate change, in particular the shift precipitation patterns increasing frequency extreme weather events, necessitate development advanced models. This study investigates application long short-term memory (LSTM) neural networks predicting runoff Velika Morava catchment Serbia, representing a pioneering LSTM this region. uses daily runoff, temperature data from 1961 to 2020, interpolated using inverse distance weighting method. model, which was optimized trial-and-error approach, showed high prediction accuracy. For station, model mean square error (MSE) 2936.55 an R2 0.85 test phase. findings highlight effectiveness capturing nonlinear hydrological dynamics, temporal dependencies regional variations. underlines potential models improve management strategies Western Balkans.

Язык: Английский

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Analysis and Prediction of Flood Disasters Based on BP Neural Network and Convolutional Neural Network

Опубликована: Янв. 10, 2025

Язык: Английский

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