Interpretable prediction, classification and regulation of water quality: A case study of Poyang Lake, China

The Science of The Total Environment, Год журнала: 2024, Номер 951, С. 175407 - 175407

Опубликована: Авг. 9, 2024

Язык: Английский

---

Stacked-based hybrid gradient boosting models for estimating seepage from lined canals

Journal of Water Process Engineering, Год журнала: 2025, Номер 70, С. 106913 - 106913

Опубликована: Янв. 9, 2025

Язык: Английский

---

Enhancing carbon price point-interval multi-step-ahead prediction using a hybrid framework of autoformer and extreme learning machine with multi-factors

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126467 - 126467

Опубликована: Янв. 1, 2025

Язык: Английский

---

Multi-objective optimal scheduling of cascade reservoirs in complex basin systems: Case study of the Jinsha River-Yalong River confluence basin in China

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 58, С. 102240 - 102240

Опубликована: Фев. 16, 2025

Язык: Английский

---

Enhance carbon emission prediction using bidirectional long short-term memory model based on text-based and data-driven multimodal information fusion

Journal of Cleaner Production, Год журнала: 2024, Номер 471, С. 143301 - 143301

Опубликована: Июль 31, 2024

Язык: Английский

---

A Comparative Assessment of Machine Learning and Deep Learning Models for the Daily River Streamflow Forecasting

Water Resources Management, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 29, 2024

Язык: Английский

---

Variable sensitivity analysis in groundwater level projections under climate change adopting a hybrid machine learning algorithm

Environmental Modelling & Software, Год журнала: 2024, Номер 183, С. 106264 - 106264

Опубликована: Ноя. 13, 2024

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

---

A novel hybrid model based on dual-layer decomposition and kernel density estimation for VOCs concentration forecasting considering influencing factors

Atmospheric Pollution Research, Год журнала: 2025, Номер unknown, С. 102439 - 102439

Опубликована: Фев. 1, 2025

Язык: Английский

---

Identification of the formation temperature field by Explainable Artificial Intelligence: A case study of Songyuan City, China

Energy, Год журнала: 2025, Номер unknown, С. 135172 - 135172

Опубликована: Фев. 1, 2025

Язык: Английский

---