Advancing real-time error correction of flood forecasting based on the hydrologic similarity theory and machine learning techniques-Reference-Cited by-同舟云学术

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Advancing real-time error correction of flood forecasting based on the hydrologic similarity theory and machine learning techniques

Published:2024-04 Issue: Volume:246 Page:118533
ISSN:0013-9351
Container-title:Environmental Research
language:en
Short-container-title:Environmental Research

Author:

Shi Peng,Wu Hongshi,Qu Simin,Yang Xiaoqiang,Lin Ziheng,Ding Song,Si Wei^ORCID

Funder

HSBC Bank USA

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference78 articles.

1. Extended lead time accurate forecasting of palmer drought severity index using hybrid wavelet-fuzzy and machine learning techniques;Altunkaynak;J. Hydrol.,2021

2. Using a linear discriminant analysis (LDA)-based nomenclature system and self-organizing maps (SOM) for spatiotemporal assessment of groundwater quality in a coastal aquifer;Amiri;J. Hydrol.,2021

3. Enhancing hydrological model calibration through hybrid strategies in data-scarce regions;Anand;Hydrol. Process.,2024

4. An extensive comparative study of cluster validity indices;Arbelaitz;Pattern Recognit,2013

5. Future change in urban flooding using new convection-permitting climate projections;Archer;Water Resour. Res.,2024

Cited by 1 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. Enhancing Urban Resilience to Flooding in Hydrogeological Risk Areas Through Big Data Analytics Using Deep Neuro-Fuzzy System;2024-07-23

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