Hybrid k-GA Model: A Robust Framework for Chaos-Based River Water Level Predictions Across Different Elevations of the Pahang River
DOI:
https://doi.org/10.37134/jsml.vol14.2.15.2026Keywords:
Hybrid k-GA model, Chaos approach, Genetic Algorithm, Time series forecasting, Pahang RiverAbstract
Improving the chaos-based method is important for increasing the accuracy of predictions in uncertain river water level conditions. This paper introduces the k-GA model, a hybrid method that combines chaos theory with genetic algorithms to forecast river water levels, particularly at different elevations of the Pahang River. Traditional chaos-based techniques often struggle with the uncertain and unpredictable nature of river systems, especially when water levels are high. The proposed k-GA model applies chaos theory to identify meaningful patterns in hydrological time series data. At the same time, the genetic algorithm component improves the forecasting process by selecting the most suitable parameters and features. To evaluate its effectiveness, the model was compared with existing chaos-based methods, namely the Local Mean Average Method (LMAM) and the Local Linear Approximation Method (LLAM), across three river zones which are upstream, midstream and downstream area. The results show that the k-GA model outperformed both comparison methods by effectively capturing the complex behavior of water level changes and producing more accurate forecasts, achieving over 99% accuracy compared to the 93% to 96% accuracy of existing chaos-based methods. The main contribution of this study lies in the development of a hybrid chaos-genetic algorithm model that enhances forecasting performance in complex hydrological environments, providing a more accurate and adaptive tool for flood risk management and water resource planning.
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Adenan NH, Karim NSA, Mashuri A, Hamid NZA, Adenan MS, Armansyah, Siregar I. (2021). Traffic flow prediction in urban area using inverse approach of Chaos Theory. Civil Engineering and Architecture, 9(4), 1277-1282. doi:10.13189/CEA.2021.090429
Afan HA, Wan Mohtar WHM, Aksoy M, Ahmed AN, Khaleel F, Khan MMH, Kamel AH, Sherif M, El-Shafie A. (2025). A multi-functional genetic algorithm-neural network model for predicting suspended sediment loads. Water Resources Management, 39(5), 2033-2048. doi:10.1007/S11269-024-04054-W/TABLES/3
Along NZB, Deep S. (2023). Indigenous local knowledge (ILK) and authorities’ knowledge integration: Case study of flood risk management in Pekan district, Pahang, Malaysia. XVII Mexican Symposium on Medical Physics, 2947(1), 080003. doi:10.1063/5.0167885/2915014
Alsayed A, Sadir H, Kamil R, Sari H. (2020). Prediction of epidemic peak and infected cases for COVID-19 disease in Malaysia, 2020. International Journal of Environmental Research and Public Health, 17(11), 1-15. doi:10.3390/ijerph17114076
Barrera-Animas AY, Oyedele LO, Bilal M, Akinosho TD, Delgado JMD, Akanbi LA. (2022). Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting. Machine Learning with Applications, 7, 100204. doi:10.1016/J.MLWA.2021.100204
Cicek ZIE, Ozturk ZK. (2021). Optimizing the artificial neural network parameters using a biased random key genetic algorithm for time series forecasting. Applied Soft Computing, 102, 107091. doi:10.1016/J.ASOC.2021.107091
El Alaoui El Fels A, Alaa N, Bachnou A. (2018). Use of genetic algorithm in new approach to modeling of flood routing. Journal of Oceanology and Limnology, 37(1), 72-78. doi:10.1007/S00343-018-7293-4
Ghorbani MA, Khatibi R, Danandeh Mehr A, Asadi H. (2018). Chaos-based multigene genetic programming: A new hybrid strategy for river flow forecasting. Journal of Hydrology, 562, 455-467. doi:10.1016/j.jhydrol.2018.04.054
Hamidon AM, Musa S. (2022). Short-term time-series forecasting by using exponential smoothing techniques at Sungai Pahang, Malaysia. Springer Proceedings in Physics, 273, 443-452. doi:10.1007/978-981-16-8903-1_38
Jiang Y, Bao X, Hao S, Zhao H, Li X, Wu X. (2020). Monthly streamflow forecasting using ELM-IPSO based on phase space reconstruction. Water Resources Management, 34(11), 3515-3531. doi:10.1007/S11269-020-02631-3/METRICS
Kamarudin MKA, Toriman ME, Abd Wahab N, Abu Samah MA, Abdul Maulud KN, Mohamad Hamzah F, Mohd Saudi AS, Sunardi S. (2023). Hydrological and climate impacts on river characteristics of pahang river basin, Malaysia. Heliyon, 9(11), e21573. doi:10.1016/J.HELIYON.2023.E21573
Lahsen M, Ribot J. (2022). Politics of attributing extreme events and disasters to climate change. Wiley Interdisciplinary Reviews: Climate Change, 13(1), e750. doi:10.1002/WCC.750
Lambora A, Gupta K, Chopra K. (2019). Genetic algorithm - A Literature review. Proceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing: Trends, Prespectives and Prospects, 380-384. doi:10.1109/COMITCON.2019.8862255
Lindenschmidt KE, Huokuna M, Burrell BC, Beltaos S. (2018). Lessons learned from past ice-jam floods concerning the challenges of flood mapping. International Journal of River Basin Management, 16(4), 457-468. doi:10.1080/15715124.2018.1439496
Lyche Solheim A, Globevnik L, Austnes K, Kristensen P, Moe SJ, Persson J, Phillips G, Poikane S, van de Bund W, Birk S. (2019). A new broad typology for rivers and lakes in Europe: Development and application for large-scale environmental assessments. Science of the Total Environment, 697, 134043. doi:10.1016/J.SCITOTENV.2019.134043
Mashuri A, Adenan NH, Karim NSA, Tho SW, Zeng Z. (2024). Application of Chaos Theory in different fields - A literature review. Journal of Science and Mathematics Letters, 12(1), 92-101. doi:10.37134/jsml.vol12.1.11.2024
Mashuri A, Adenan NH, Karim SA, Shahriman A, Rani CNA. (2022). Performance of water level forecasting based on Chaos approach using data splitting. Environment and Ecology Research, 10(2), 218-224. doi:10.13189/eer.2022.100211
McCall J. (2005). Genetic algorithms for modelling and optimisation. Journal of Computational and Applied Mathematics, 184(1), 205-222. doi:10.1016/J.CAM.2004.07.034
Mihailović DT, Malinović-Milićević S, Han J, Singh VP. (2023). Complexity and chaotic behavior of the U.S. Rivers and estimation of their prediction horizon. Journal of Hydrology, 622, 129730. doi:10.1016/J.JHYDROL.2023.129730
Mustaffa H, Kamarudin MKA, Toriman ME, Rosli MH, Sunardi S. (2023). Impact of suspended sediment on Pahang River development using geographic information system. Planning Malaysia, 21(1), 116-133. doi:10.21837/PM.V21I25.1228
Nagasubramanian K, Jones S, Sarkar S, Singh AK, Singh A, Ganapathysubramanian B. (2018). Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems. Plant Methods, 14(1), 1-13. doi:10.1186/S13007-018-0349-9/FIGURES/6
Pretorius K, Pillay N. (2024). Neural network crossover in genetic algorithms using genetic programming. Genetic Programming and Evolvable Machines, 25(1), 1-30. doi:10.1007/S10710-024-09481-7/FIGURES/9
Ramadevi B, Bingi K. (2022). Chaotic time series forecasting approaches using machine learning techniques: A review. Symmetry, 14(5), 955. doi:10.3390/SYM14050955
Salih SQ, Sharafati A, Ebtehaj I, Sanikhani H, Siddique R, Deo RC, Bonakdari H, Shahid S, Yaseen ZM. (2020). Integrative stochastic model standardization with genetic algorithm for rainfall pattern forecasting in tropical and semi-arid environments. Hydrological Sciences Journal, 65(7), 1145-1157. doi:10.1080/02626667.2020.1734813
Singh P, Dhiman G. (2018). A hybrid fuzzy time series forecasting model based on granular computing and bio-inspired optimization approaches. Journal of Computational Science, 27, 370-385. doi:10.1016/J.JOCS.2018.05.008
Sridhar V, Karim F, Tshimanga RM, Kilinc HC, Yurtsever A. (2022). Short-term streamflow forecasting using Hybrid Deep Learning Model based on Grey Wolf Algorithm for Hydrological Time Series. Sustainability, 14(6), 3352. doi:10.3390/SU14063352
Swagatika S, Paul JC, Sahoo BB, Gupta SK, Singh PK. (2024). Improving the forecasting accuracy of monthly runoff time series of the Brahmani River in India using a hybrid deep learning model. Journal of Water and Climate Change, 15(1), 139-156. doi:10.2166/WCC.2023.487
Tahir M, Tubaishat A, Al-Obeidat F, Shah B, Halim Z, Waqas M. (2022). A novel binary chaotic genetic algorithm for feature selection and its utility in affective computing and healthcare. Neural Computing and Applications, 34(14), 11453-11474. doi:10.1007/S00521-020-05347-Y/METRICS
Takens F. (1981). Detecting strange attractors in turbulence. Dynamical Systems and Turbulence, Warwick. doi:10.1007/BFb0091924
Truong DS, Hien LT, Tung NT. (2022). An effective algorithm for computing reducts in decision tables. Journal of Computer Science and Cybernetics, 38(3), 277-292. doi:10.15625/1813-9663/38/3/17450
Vagheei H, Laini A, Vezza P, Palau-Salvador G, Boano F. (2023). Climate change impact on the ecological status of rivers: The case of Albaida Valley (SE Spain). Science of The Total Environment, 893, 164645. doi:10.1016/J.SCITOTENV.2023.164645
Wang X, Babovic V. (2016). Application of hybrid Kalman filter for improving water level forecast. Journal of Hydroinformatics, 18(5), 773-790. doi:10.2166/hydro.2016.085
Yousefi P, Courtice G, Naser G, Mohammadi H. (2020). Nonlinear Dynamic Modeling of Urban Water Consumption Using Chaotic Approach (Case Study: City of Kelowna). Water, 12(3), 753. doi:10.3390/W12030753
Yusoff SHM, Hamzah FM, Jaafar O, Tajudin H. (2021). Long Term trend analysis of upstream and middle-stream river in Langat Basin, Selangor, Malaysia. Sains Malaysiana, 50(3), 629-644. doi:10.17576/jsm-2021-5003-06
Zakaria NH, Adenan NH, Karim NSA, Mashuri A. (2021). Prediction of water level time series data for Dam at Selangor using Chaotic Approach and Local Linear Approximation method. Journal of Science and Mathematics Letters, 9, 10-17. doi:10.37134/jsml.vol9.sp.2.2021
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Copyright (c) 2026 Adib Mashuri, Nur Hamiza Adenan, Azurah A Samah, Ahmad Basri Ruslan, Nor Suriya Abd Karim, Muhammad Faizan
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