Monthly inflow forecasting utilizing advanced artificial intelligence methods: a case study of Haditha Dam in Iraq-Reference-Cited by-同舟云学术

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Monthly inflow forecasting utilizing advanced artificial intelligence methods: a case study of Haditha Dam in Iraq

Published:2021-06-24 Issue:11 Volume:35 Page:2391-2410
ISSN:1436-3240
Container-title:Stochastic Environmental Research and Risk Assessment
language:en
Short-container-title:Stoch Environ Res Risk Assess

Author:

Allawi Mohammed Falah,Hussain Intesar Razaq,Salman Majid Ibrahim,El-Shafie Ahmed

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science,Safety, Risk, Reliability and Quality,Water Science and Technology,Environmental Chemistry,Environmental Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00477-021-02052-7.pdf

Reference35 articles.

1. Abraham A, Khan MR (2004) Neuro-fuzzy paradigms for intelligent energy management. Springer, Berlin, pp 285–314

2. Alecsandru C, Ishak S (2004) Hybrid model-based and memory-based traffic prediction system. Transp Res Rec J Transp Res Board 1879:59–70. https://doi.org/10.3141/1879-08

3. Allawi MF, El-Shafie A (2016) Utilizing RBF-NN and ANFIS methods for multi-lead ahead prediction model of evaporation from reservoir. Water Resour Manag 30:4773–4788. https://doi.org/10.1007/s11269-016-1452-1

4. Allawi MF, Jaafar O, Ehteram M et al (2018) Synchronizing artificial intelligence models for operating the dam and reservoir system. Water Resour Manag. https://doi.org/10.1007/s11269-018-1996-3

5. Allawi MF, Jaafar O, Mohamad Hamzah F et al (2017) Reservoir inflow forecasting with a modified coactive neuro-fuzzy inference system: a case study for a semi-arid region. Theor Appl Climatol. https://doi.org/10.1007/s00704-017-2292-5

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1. Forecasting for Haditha reservoir inflow in the West of Iraq using Support Vector Machine (SVM);PLOS ONE;2024-09-06

2. A novel approach to forecast water table rise in arid regions using stacked ensemble machine learning and deep artificial intelligence models;Journal of Hydrology;2024-08

3. Daily reservoir inflow prediction using stacking ensemble of machine learning algorithms;Journal of Hydroinformatics;2024-04-16

4. Enhancing Accuracy of Forecasting Monthly Reservoir Inflow by Using Comparison of Three New Hybrid Models: A Case Study of The Droodzan Dam in Iran;Iranian Journal of Science and Technology, Transactions of Civil Engineering;2024-04-12

5. A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years;Journal of Marine Science and Engineering;2024-01-13

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