A local patch regression-based generative model for urban flood prediction in data-poor areas

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127489 - 127489

Опубликована: Апрель 1, 2025

Язык: Английский

---

The enhanced integration of proven techniques to quantify the uncertainty of forecasting extreme flood events based on numerical weather prediction models

Weather and Climate Extremes, Год журнала: 2025, Номер unknown, С. 100767 - 100767

Опубликована: Апрель 1, 2025

Язык: Английский

---

Long-term prediction of Poyang Lake water level by combining multi-scale isometric convolution network with quantile regression

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 59, С. 102365 - 102365

Опубликована: Апрель 17, 2025

Язык: Английский

---

Urban Flood Prediction Model Based on Transformer-LSTM-Sparrow Search Algorithm

Water, Год журнала: 2025, Номер 17(9), С. 1404 - 1404

Опубликована: Май 7, 2025

Global climate change and accelerated urbanization have intensified extreme rainfall events, exacerbating urban flood risks. Although data-driven models shown potential in prediction, the ability of single to capture complex nonlinear relationships their sensitivity hyperparameters still limit prediction accuracy. To address these challenges, this study proposes an model by integrating Transformer, Long Short-Term Memory (LSTM), Sparrow Search Algorithm (SSA), combining Transformer’s global feature extraction with LSTM’s temporal modeling. The SSA was adopted optimize for Transformer-LSTM model. Dropout early stopping techniques were mitigate overfitting. Applied Zhengzhou city Henan province, China, achieves a Nash-Sutcliffe Efficiency (NSE) 0.971, indicating that proposed has high performance flooding. experimental results demonstrate Transformer-LSTM-SSA outperforms standalone LSTM, 12.9%, 10.1%, 2.9% NSE accuracy, respectively, while reducing MAE 62.12%, 56.9%, 34.21%, MAPE 21.69%, 22.2%, 10.89%, respectively. Furthermore, exhibits enhanced stability superior generalization capability. among comparative methods, thereby demonstrating model’s viability providing reliable solution real-time warning.

Язык: Английский

---

Comprehensive objective function- guided decomposition-prediction co-optimization framework: Enhanced Transformer model for high-accuracy forecasting of non-stationary runoff

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 60, С. 102482 - 102482

Опубликована: Май 21, 2025

Язык: Английский

---

Enhancing flood forecasting performance using effective and transparent explainable hybrid deep learning model

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Май 28, 2025

Язык: Английский

---

Incorporating Recursive Feature Elimination and Decomposed Ensemble Modeling for Monthly Runoff Prediction

Water, Год журнала: 2024, Номер 16(21), С. 3102 - 3102

Опубликована: Окт. 29, 2024

Monthly runoff prediction is crucial for water resource allocation and flood prevention. Many existing methods use identical deep learning networks to understand monthly patterns, neglecting the importance of predictor selection. To enhance predictive accuracy reliability, this study proposes an RFECV–SSA–LSTM forecasting approach. It iteratively eliminates predictors derived from SSA decomposition PACF using recursive feature elimination cross-validation (RFECV) identify most relevant subset predicting target flow. LSTM modeling then used forecast flows 1–7 months into future. Furthermore, RFECV–SSA framework complements any machine-learning-based method. demonstrate method’s reliability effectiveness, its outputs are compared across three scenarios: direct LSTM, MIR–LSTM, RFECV–LSTM, historical data Yangxian Hanzhong hydrological stations in Hanjiang River Basin, China. The results show that RFECV–LSTM method more robust efficient than MIR–LSTM counterparts, with smallest number outliers NSE, NRMSE, PPTS under all scenarios. approach exhibits worst performance, indicating single-metric-based selection may eliminate valuable information. time–frequency superior, NSE values remaining stably around 0.95 value greater almost scenarios, outperforming other benchmark models. Therefore, effective highly nonlinear series, exhibiting high generalization ability.

Язык: Английский

---

High-risk driving factors of rain-induced flooding hazard events on the Loess Plateau and its ecological subregions

Journal of Hydrology, Год журнала: 2024, Номер unknown, С. 132475 - 132475

Опубликована: Дек. 1, 2024

Язык: Английский

---

Analyzing watershed system state through runoff complexity and driver interactions using multiscale entropy and deep learning

Ecological Indicators, Год журнала: 2024, Номер 168, С. 112779 - 112779

Опубликована: Окт. 30, 2024

Язык: Английский

---

Innovative Methods Predicting the Remaining Useful Life of Transformer Using Limited Data

Опубликована: Янв. 1, 2024

Язык: Английский

---