Value of process understanding in the era of machine learning: A case for recession flow prediction DOI
Prashant Istalkar, Akshay Kadu, Basudev Biswal

et al.

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 626, P. 130350 - 130350

Published: Oct. 21, 2023

Language: Английский

Investigating the impacts of climate change on hydroclimatic extremes in the Tar-Pamlico River basin, North Carolina DOI
Thanh‐Nhan‐Duc Tran,

Mahesh R Tapas,

Son K.

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 363, P. 121375 - 121375

Published: June 8, 2024

Language: Английский

Citations

56
Machine learning in modelling the urban thermal field variance index and assessing the impacts of urban land expansion on seasonal thermal environment DOI
Maomao Zhang,

Shukui Tan,

Cheng Zhang

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 106, P. 105345 - 105345

Published: March 14, 2024

Language: Английский

Citations

55
Coupling the remote sensing data-enhanced SWAT model with the bidirectional long short-term memory model to improve daily streamflow simulations DOI Creative Commons

Lei Jin,

Huazhu Xue, Guotao Dong

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 634, P. 131117 - 131117

Published: March 24, 2024

Global climate change has led to an increase in the frequency and scale of extreme weather events worldwide, there is urgent need develop better-performing hydrological models improve accuracy streamflow simulations facilitate water resource planning management. The Soil Water Assessment Tool (SWAT) a notable physical foundation widely used research. However, it uses simplified vegetation growth model, introducing uncertainty into simulation results. This study focused on improving model based remotely sensed phenological leaf area index (LAI) data. Phenological data were define dormancy, LAI replaced corresponding simulated by original model. approach improved describing dynamics. Then, enhanced SWAT was coupled with bidirectional long short-term memory (BiLSTM) validate processes upstream Hei River. During validation, performance simulating (R2 = 0.835, NSE 0.819) better than that 0.821, 0.805). In terms evapotranspiration, demonstrated even greater advantages. verification period, compared those R2 values for daily-scale increased from 0.196 −0.269 0.777 0.732, respectively. monthly-scale 0.782 0.678 0.906 0.851, Simultaneously, levels two coupling approaches prediction compared, i.e., direct BiLSTM (SWAT-BiLSTM) (enhanced SWAT-BiLSTM). results showed SWAT-BiLSTM always performed during entire especially which could more accurately predict peak changes. deep learning accuracy.

Language: Английский

Citations

18
Stepwise decomposition-integration-prediction framework for runoff forecasting considering boundary correction DOI
Zhanxing Xu, Mo Li, Jianzhong Zhou

et al.

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 851, P. 158342 - 158342

Published: Aug. 26, 2022

Language: Английский

Citations

50
Enhancing SWAT model with modified method to improve Eco-hydrological simulation in arid region DOI
Yunfei Cai, Zhang Fei,

Jingchao Shi

et al.

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 403, P. 136891 - 136891

Published: March 22, 2023

Language: Английский

Citations

23
Advancing climate-resilient flood mitigation: Utilizing transformer-LSTM for water level forecasting at pumping stations DOI
Pu-Yun Kow,

Jia-Yi Liou,

Ming-Ting Yang

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 927, P. 172246 - 172246

Published: April 7, 2024

Language: Английский

Citations

13
Deep learning for monthly rainfall–runoff modelling: a large-sample comparison with conceptual models across Australia DOI Creative Commons
Stephanie Clark, Julien Lerat, Jean‐Michel Perraud

et al.

Hydrology and earth system sciences, Journal Year: 2024, Volume and Issue: 28(5), P. 1191 - 1213

Published: March 13, 2024

Abstract. A deep learning model designed for time series predictions, the long short-term memory (LSTM) architecture, is regularly producing reliable results in local and regional rainfall–runoff applications around world. Recent large-sample hydrology studies North America Europe have shown LSTM to successfully match conceptual performance at a daily step over hundreds of catchments. Here we investigate how these models perform monthly runoff predictions relatively dry variable conditions Australian continent. The matches historic data availability also important future water resources planning; however, it provides significantly smaller training datasets than series. In this study, continental-scale comparison (WAPABA model) performed on almost 500 catchments across Australia with aggregated variety catchment sizes, flow conditions, hydrological record lengths. study period covers wet phase followed by prolonged drought, introducing challenges making outside known – that will intensify as climate change progresses. show matched or exceeded WAPABA prediction more two-thirds catchments, largest gains versus occurred large LSTMs struggled less generalise (e.g. under new conditions), few observations due did not demonstrate clear benefit either LSTM.

Language: Английский

Citations

10
The impact of human activities and climate change on the eco-hydrological processes in the Yangtze River basin DOI Creative Commons

Ning He,

Wenxian Guo, Jiaqi Lan

et al.

Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 53, P. 101753 - 101753

Published: April 8, 2024

The Yangtze River Basin To accurately quantify the impact of climate change and human activities on hydrological regime, Long Short-Term Memory (LSTM) models for meteorology-runoff simulation are constructed multi-year average monthly flow process. Indicators Hydrologic Alteration (IHA) method is used to quantitatively evaluate processes in watershed. Additionally, grey relational analysis employed explore key indicators affecting ecological effects fish. streamflow mainstem its seven tributaries increases as distance from river mouth decreases. degree changes various basins moderate (33–66%). Except Jialing River, Wu Poyang Lake, other mainly influenced by activities. Among them, Yichang most affected (75.43%), while (67.05%). rate land use development has reached 116.9% over past 20 years, vegetation coverage been increasing at a linear 0.003 per year, Summer rainfall significantly positively correlated with flow, temperature negatively correlated. Three Gorges Dam reduced spawning scale fish identified "October runoff" indicator. These findings contribute deeper understanding response watershed water resources effects.

Language: Английский

Citations

10
A state-of-the-art review of long short-term memory models with applications in hydrology and water resources DOI
Zhong-kai Feng, J. Zhang, Wen-jing Niu

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: unknown, P. 112352 - 112352

Published: Oct. 1, 2024

Language: Английский

Citations

9
Dynamic classification and attention mechanism-based bidirectional long short-term memory network for daily runoff prediction in Aksu River basin, Northwest China DOI
Wei Qing, Ju Rui Yang, Fangbing Fu

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 374, P. 124121 - 124121

Published: Jan. 15, 2025

Language: Английский

Citations

1