Exploring a similarity search-based data-driven framework for multi-step-ahead flood forecasting-Reference-Cited by-同舟云学术

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Exploring a similarity search-based data-driven framework for multi-step-ahead flood forecasting

Published:2023-09 Issue: Volume:891 Page:164494
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Lin Kangling,Chen Hua,Zhou Yanlai,Sheng Sheng,Luo Yuxuan,Guo Shenglian,Xu Chong-Yu

Funder

Norges Forskningsråd

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference67 articles.

1. New double decomposition deep learning methods for river water level forecasting;Ahmed;Sci. Total Environ.,2022

2. Markov chain-incorporated and synthetic data-supported conditional artificial neural network models for forecasting monthly precipitation in arid regions;Aksoy;J. Hydrol.,2018

3. Flood mitigation data analytics and decision support framework: Iowa Middle Cedar Watershed case study;Alabbad;Sci. Total Environ.,2022

4. User profiling approaches for demographic recommender systems;Al-Shamri;Knowl.-Based Syst.,2016

5. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling;Bai,2018

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1. Improving Long-Term Flood Forecasting Accuracy Using Ensemble Deep Learning Models and an Attention Mechanism;Journal of Hydrologic Engineering;2024-12

2. Enhancing physically-based flood forecasts through fusion of long short-term memory neural network with unscented Kalman filter;Journal of Hydrology;2024-09

3. Research on Runoff Prediction Based on Time2Vec-TCN-Transformer Driven by Multi-Source Data;Electronics;2024-07-09

4. Optimizing complementary operation of mega cascade reservoirs for boosting hydropower sustainability;Sustainable Energy Technologies and Assessments;2024-04

5. Advancing real-time error correction of flood forecasting based on the hydrologic similarity theory and machine learning techniques;Environmental Research;2024-04

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