Perspective Chapter: Big Data and Deep Learning in Hydrological Modeling DOI Creative Commons

Li Zhou

IntechOpen eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 4, 2024

This chapter delves into the integration of physical mechanisms with deep learning models to enhance interpretability and accuracy hydrological process modeling. In era big data rapid advancements in AI, synergy between traditional principles machine opens new opportunities for improved water resource management, flood prediction, drought monitoring. The presents a comprehensive framework that leverages vast datasets from sources such as remote sensing, reanalysis data, situ It explores potential models, particularly when combined insights, address challenges data-scarce regions, improving transparency predictions. By analyzing strengths limitations current approaches, study highlights value hybrid balancing interpretability. These not only predictive performance but also provide more transparent insights underlying processes. contributes sustainable disaster resilience, climate adaptation, pushing forward both scientific progress practical applications. offers valuable methodologies case studies underscore importance domain knowledge development explainable reliable reshaping future forecasting.

Language: Английский

Enhancing dynamic flood risk assessment and zoning using a coupled hydrological-hydrodynamic model and spatiotemporal information weighting method DOI

Li Zhou,

Lingxue Liu

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 366, P. 121831 - 121831

Published: July 16, 2024

Language: Английский

Citations

8
Detection and attribution of eco-hydrological alteration based on deep learning-driven gap-filled runoff in a large-scale catchment DOI Creative Commons
Zhibao Dong, Xuan Ji, Kai Ma

et al.

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 58, P. 102228 - 102228

Published: Feb. 12, 2025

Language: Английский

Citations

0
MACHINE LEARNING-BASED HYDROGRAPH MODELING WITH LSTM: A CASE STUDY IN THE JATIGEDE RESERVOIR CATCHMENT, INDONESIA DOI Creative Commons
Neil Andika,

Piter Wongso,

Faizal Immaddudin Wira Rohmat

et al.

Results in Earth Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 100090 - 100090

Published: April 1, 2025

Language: Английский

Citations

0
Runoff Simulation in Data-Scarce Alpine Regions: Comparative Analysis Based on LSTM and Physically Based Models DOI Open Access
Jiajia Yue,

Li Zhou,

Juan Du

et al.

Water, Journal Year: 2024, Volume and Issue: 16(15), P. 2161 - 2161

Published: July 31, 2024

Runoff simulation is essential for effective water resource management and plays a pivotal role in hydrological forecasting. Improving the quality of runoff forecasting continues to be highly relevant research area. The complexity terrain scarcity long-term observation data have significantly limited application Physically Based Models (PBMs) Qinghai–Tibet Plateau (QTP). Recently, Long Short-Term Memory (LSTM) network has been found learning dynamic characteristics watersheds outperforming some traditional PBMs simulation. However, extent which LSTM works data-scarce alpine regions remains unclear. This study aims evaluate applicability basins QTP, as well performance transfer-based (T-LSTM) regions. Lhasa River Basin (LRB) Nyang (NRB) were areas, model was compared that by relying solely on meteorological inputs. results show average values Nash–Sutcliffe efficiency (NSE), Kling–Gupta (KGE), Relative Bias (RBias) B-LSTM 0.80, 0.85, 4.21%, respectively, while corresponding G-LSTM 0.81, 0.84, 3.19%. In comparison PBM- Block-Wise use TOPMEDEL (BTOP), an enhancement 0.23, 0.36, −18.36%, respectively. both basins, outperforms BTOP model. Furthermore, transfer learning-based at multi-watershed scale demonstrates that, when input are somewhat representative, even if amount limited, T-LSTM can obtain more accurate than models specifically calibrated individual watersheds. result indicates effectively improve applied

Language: Английский

Citations

3
Perspective Chapter: Big Data and Deep Learning in Hydrological Modeling DOI Creative Commons

Li Zhou

IntechOpen eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 4, 2024

This chapter delves into the integration of physical mechanisms with deep learning models to enhance interpretability and accuracy hydrological process modeling. In era big data rapid advancements in AI, synergy between traditional principles machine opens new opportunities for improved water resource management, flood prediction, drought monitoring. The presents a comprehensive framework that leverages vast datasets from sources such as remote sensing, reanalysis data, situ It explores potential models, particularly when combined insights, address challenges data-scarce regions, improving transparency predictions. By analyzing strengths limitations current approaches, study highlights value hybrid balancing interpretability. These not only predictive performance but also provide more transparent insights underlying processes. contributes sustainable disaster resilience, climate adaptation, pushing forward both scientific progress practical applications. offers valuable methodologies case studies underscore importance domain knowledge development explainable reliable reshaping future forecasting.

Language: Английский

Citations

1