Flood Forecasting Analysis in Bandar Segamat Catchment Utilizing the Artificial Bee Colony Algorithm

Abstract

Floods are one of the most devastating natural disasters in Malaysia. Therefore, flood forecasting is a critical component in managing and mitigating the adverse effects of floods, particularly in vulnerable areas such as Bandar Segamat catchment in Johor, Malaysia. This study presents an innovative approach to flood forecasting by employing the Artificial Bee Colony (ABC) algorithm, a nature-inspired optimization technique. By incorporating with MATLAB software, it can leverage its robust computational, visualization tools, modifications such as hybridization, dynamic parameter tuning, and adaptive strategies, the study demonstrates improved solution quality, faster convergence, and greater robustness. The aims are to identify parameters for flood forecasting in the Bandar Segamat catchment using the Artificial Bee Colony (ABC) algorithm and to develop a flood prediction model for the catchment area utilizing the ABC algorithm, The model has been adeptly employed to emulate future floods under typical circumstances, conditions incorporating current rainfall data, temperature data, runoff, and Curve Number (CN) value. The findings underscore MATLAB's utility in optimization research and provide valuable insights for refining bio-inspired algorithms for practical applications. The findings underscore MATLAB's utility in optimization research and provide valuable insights for refining bio-inspired algorithms for practical applications. The projected flood probabilities manifest a more uniform schema in comparison to historical records, possibly signifying enhanced predictability of flood occurrences in the forecast interval attributable to refined modelling methodologies. Using this information, the organizations linked to flood oversight in Johor, notably in Bandar Segamat, can develop a proactive strategic framework for the next fourteen years to reduce the chances of floods.