A stacking neuro-fuzzy framework to forecast runoff from distributed meteorological stations-Reference-Cited by-同舟云学术

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A stacking neuro-fuzzy framework to forecast runoff from distributed meteorological stations

Published:2022-03 Issue: Volume:118 Page:108535
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Querales Marvin^ORCID,Salas Rodrigo^ORCID,Morales Yerel,Allende-Cid Héctor^ORCID,Rosas Harvey^ORCID

Publisher

Elsevier BV

Subject

Software

Reference64 articles.

1. A reliable rainfall–runoff model for flood forecasting: Review and application to a semi-urbanized watershed at high flood risk in Italy;Masseroni;Hydrol. Res.,2017

2. Artificial neural networks in hydrology. II: Hydrologic applications;Govindaraju;J. Hydrol. Eng.,2000

3. Support vector regression for rainfall-runoff modeling in urban drainage: A comparison with the EPA’s storm water management model;Granata;Water,2016

4. Physics-informed data-driven models to predict surface runoff water quantity and quality in agricultural fields;Liang;Water,2019

5. Rainfall-runoff modeling using clustering and regression analysis for the river brahmaputra basin;Mishra;J. Geol. Soc. India,2018

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1. Research on Coupling Knowledge Embedding and Data-Driven Deep Learning Models for Runoff Prediction;Water;2024-07-27

2. A data-driven implicit deep adaptive neuro-fuzzy inference system capable of manifold learning for function approximation;Applied Soft Computing;2024-04

3. Runoff simulation driven by multi-source satellite data based on hydrological mechanism algorithm and deep learning network;Journal of Hydrology: Regional Studies;2024-04

4. Predicting the incidence of infectious diarrhea with symptom surveillance data using a stacking-based ensembled model;BMC Infectious Diseases;2024-02-26

5. Predictions of runoff and sediment discharge at the lower Yellow River Delta using basin irrigation data;Ecological Informatics;2023-12

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