A Novel GA-LSTM Method For Water Flow Prediction With Real Climate Data

Salih, A, Zhang, Q ORCID: 0000-0002-0651-469X, Raschella, A ORCID: 0000-0002-1626-8947, Mohammed, A ORCID: 0009-0003-8426-6964, Abdullah, BM ORCID: 0000-0002-1281-148X, Aldhaibani, OA ORCID: 0000-0003-0235-2862, Maheshwari, MK and Qiu, Y ORCID: 0000-0003-0266-1294 (2026) A Novel GA-LSTM Method For Water Flow Prediction With Real Climate Data. In: 2026 1st International Conference on Emerging Trends in Advancements and Applications of Computational Intelligence Techniques (ETAACT) . pp. 1-6. (2026 1st International Conference on Emerging Trends in Advancements and Applications of Computational Intelligence Techniques (ETAACT), 10th Apr- 11th Apr 2026, Bhubaneswar, India).

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Abstract

Accurate water storage forecasts provide time for authorities and the public to enact response measures. In this contest, Long Short-Term Memory (LSTM) is widely used in inflow forecasting to ensure sufficient response time. This paper introduces a novel Genetic Algorithm (GA-LSTM) model, which integrates LSTM with GA to improve the inflow river water forecasting in dam operation. To evaluate the proposed model, we considered a case study that pertains to the inflow water level forecasting of the Euphrates River in Iraq. The experimental results show that our GA-LSTM model effectively improves the accuracy of water storage prediction compared to a standard LSTM model.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	© 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Civil Engineering and Built Environment Computer Science and Mathematics Engineering
Publisher:	IEEE
Date of acceptance:	10 January 2026
Date of first compliant Open Access:	4 June 2026
Date Deposited:	04 Jun 2026 13:59
Last Modified:	04 Jun 2026 13:59
DOI or ID number:	10.1109/etaact69135.2026.11541970
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28730

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