Citation :

Siti Hajar Binti Arbain, Rozaida binti Ghazali, Sani Inusa Milala, Mohamed Hafiz Bin Kamaluddin, "Hybrid Models and Techniques of Flood Forecasting: Steering NARX and Taguchi Method; An Iceberg Overview," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 6, pp. 75-103, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I6P106

Abstract

Floods are still one of the most serious natural disasters, which are a major threat to human lives, infrastructure, and ecosystems. Existing approaches (hybrid modeling, machine learning methods such as data-driven models) can present a number of issues, such as computational inefficiency, susceptibility to sudden changes in environmental conditions, and dependency on extensive datasets; though developments in flood prediction have come, many are still struggling to build predictive analysis. To overcome these limitations, we propose an innovative hybrid framework that combines the Nonlinear Autoregressive with Exogenous Input (NARX) model and the Taguchi optimization technique. This method is intended to enhance prediction accuracy, improve computational efficiency, and optimize model parameters, thereby yielding an efficient platform that enhances generalizability across time-to-time outcomes. This technique enhances prediction precision, computational efficiency, and model parameterization, leading to significant advancements in hydrological modelling. A comprehensive survey of flood forecasting techniques was undertaken, seeking to find results from Elsevier (62% of works), MDPI (12%), IEEE (8%), and Wiley Online Library (4%), with results spanning the last quarter century. This review indicates that the flood forecasting literature is robust to date, as 53% of this area has been evaluated between 2021 and 2025, highlighting the need to propose novel alternative practices to cope with the growing flood risk, particularly given the more recent years of literature over 25 years. Using a comprehensive methodology that includes studying the most suitable methods in the available state of the art methods, as well as performing a multi-context analysis using the proposed model of a NARX-Taguchi hybrid model from our work. The methods outlined above provide considerable improvements in prediction reliability, climate adaptability, and computational efficiency compared with traditional flood prediction methods. Finally, through this hybrid framework, we shall also create a new gold standard to benchmark flood forecasting systems against, and thus, more effective disaster planning mechanisms in the age of increased climate hazards.

Keywords

Flood, Techniques of Flood Forecasting, NARX Model, Taguchi Method, Flood Risk Management, and Predictive Modeling.

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