A cooperative search algorithm-based flood forecasting framework: application across diverse Chinese catchments

Frontiers in Earth Science, Год журнала: 2025, Номер 13

Опубликована: Фев. 11, 2025

Flood forecasting is crucial for disaster mitigation, particularly in regions prone to flash floods. This study introduces a novel flood framework by coupling the Geomorphological Instantaneous Unit Hydrograph (GIUH) with Xinanjiang model and optimizing parameters using Cooperation Search Algorithm (CSA). Applied across six diverse Chinese catchments, significantly improved computational efficiency accuracy. Key findings demonstrate that: 1) CSA achieved high Nash-Sutcliffe Efficiency (NSE >0.9) only 16 optimization trials on average, outperforming SCE-UA algorithms; 2) The performed exceptionally data-sparse regions, achieving NSE values >0.9 even minimal datasets; 3) Enhanced runoff routing via GIUH enabled accurate simulation of extreme rainfall events. These results highlight framework’s potential operational management globally. Future research will expand validation datasets explore applications varied hydrological climatic conditions.

Язык: Английский

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

Study on Motion Response Prediction of Offshore Platform Based on Multi-Sea State Samples and EMD Algorithm

Water, Год журнала: 2024, Номер 16(23), С. 3441 - 3441

Опубликована: Ноя. 29, 2024

The complexity of offshore operations demands that platforms withstand the variability and uncertainty marine environments. Consequently, analyses platform motion responses must extend beyond single sea state conditions. This study employs Computational Fluid Dynamics (CFDs) software STAR-CCM+ for data acquisition investigates from two perspectives: adaptability analysis to different wave directions varying significant heights. aim is develop a model capable predicting across multiple results demonstrate integrating empirical mode decomposition (EMD) algorithm with residual convolutional neural networks (ResCNNs) Long Short-Term Memory (LSTM) effectively resolves challenge insufficient prediction accuracy under diverse maritime Following EMD incorporation, model’s performance within predictive range was significantly enhanced, coefficient determination (R2) consistently exceeding 0.5, indicating high degree fit data. Concurrently, mean squared error (MSE) Mean Absolute Percentage Error (MAPE) metrics exhibited commendable performance, further substantiating precision reliability. methodology introduces an innovative approach forecasting dynamic structures, providing more rigorous accurate foundation operational decisions. Ultimately, research enhances safety productivity activities.

Язык: Английский