Quantitative improvement of streamflow forecasting accuracy in the Atlantic zones of Canada based on hydro-meteorological signals: A multi-level advanced intelligent expert framework-Reference-Cited by-同舟云学术

Quantitative improvement of streamflow forecasting accuracy in the Atlantic zones of Canada based on hydro-meteorological signals: A multi-level advanced intelligent expert framework

Published:2024-05 Issue: Volume:80 Page:102455
ISSN:1574-9541
Container-title:Ecological Informatics
language:en
Short-container-title:Ecological Informatics

Author:

Jamei Mozhdeh,Jamei Mehdi,Ali Mumtaz,Karbasi Masoud,Farooque Aitazaz A.,Malik Anurag,Cheema Saad Javed,Esau Travis J.,Yaseen Zaher Mundher

Funder

University of Prince Edward Island

Natural Sciences and Engineering Research Council of Canada

Atlantic Canada Opportunities Agency

Publisher

Elsevier BV

Reference80 articles.

1. Improving drought modeling using hybrid random vector functional link methods;Adnan;Water,2021

2. Streamflow prediction using a hybrid methodology based on variational mode decomposition (VMD) and machine learning approaches;Ahmadi;Appl Water Sci,2023

3. Prediction of local scour around circular piles under waves using a novel artificial intelligence approach;Ahmadianfar;Mar. Georesources Geotechnol.,2019

4. LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios;Ahmed;Stoch. Env. Res. Risk A.,2021

5. Computational machine learning approach for flood susceptibility assessment integrated with remote sensing and GIS techniques from Jeddah, Saudi Arabia;Al-Areeq;Remote Sens.,2022

Cited by 4 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 the streamflow simulation of a process-based hydrological model using machine learning and multi-source data;Ecological Informatics;2024-09

2. Ensemble learning of decomposition-based machine learning models for multistep-ahead daily streamflow forecasting in northwest China;Hydrological Sciences Journal;2024-08-02

3. Investigating the potential of EMA-embedded feature selection method for ESVR and LSTM to enhance the robustness of monthly streamflow forecasting from local meteorological information;Journal of Hydrology;2024-06

4. Daily Streamflow Forecasting Using Networks of Real-Time Monitoring Stations and Hybrid Machine Learning Methods;Water;2024-04-30