Application, interpretability and prediction of machine learning method combined with LSTM and LightGBM-a case study for runoff simulation in an arid area-Reference-Cited by-同舟云学术

Application, interpretability and prediction of machine learning method combined with LSTM and LightGBM-a case study for runoff simulation in an arid area

Published:2023-10 Issue: Volume:625 Page:130091
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Bian Lekang,Qin Xueer,Zhang Chenglong^ORCID,Guo Ping,Wu Hui

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference49 articles.

1. Groundwater Potential Mapping in Hubei Region of China Using Machine Learning, Ensemble Learning, Deep Learning and AutoML Methods;Bai;Natural Resources Research,2022

2. An ensemble neural network model for real-time prediction of urban floods;Berkhahn;Journal of Hydrology,2019

3. Improving management of windrow composting systems by modeling runoff water quality dynamics using recurrent neural network;Bhattacharjee;Ecological Modelling,2016

4. Impact of training data size on the LSTM performances for rainfall–runoff modeling;Boulmaiz;Modeling Earth Systems and Environment,2020

5. Random forests;Breiman;Machine Learning,2001

Cited by 22 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Research on the prediction algorithm of aero engine lubricating oil consumption based on multi-feature information fusion;Applied Intelligence;2024-09-02

2. Runoff prediction based on the IGWOLSTM model: Achieving accurate flood forecasting and emergency management;Journal of Hydro-environment Research;2024-09

3. Interpretable machine learning guided by physical mechanisms reveals drivers of runoff under dynamic land use changes;Journal of Environmental Management;2024-09

4. Forecasting ecological water demand of an arid oasis under a drying climate scenario based on deep learning methods;Ecological Informatics;2024-09

5. Streamflow Prediction in Human‐Regulated Catchments Using Multiscale Deep Learning Modeling With Anthropogenic Similarities;Water Resources Research;2024-09