Work

Missing Daily Rainfall Prediction Using Grey Wolf Optimizer-based Neural Network

Year: 2024

Type: book-chapter

Abstract: This research chapter presents the integration of the Grey Wolf Optimizer (GWO) algorithm for training a Feedforward Neural Network (FNN) to address the issue of missing daily rainfall records. A case study was conducted to evaluate the efficacy and reliability of GWO in overcoming the limitations associated with conventional FNN training algorithms, which often get stuck in local optima. The performance of the developed GWOFNN approach was assessed in handling 20% of missing daily rainfall observations at Kuching Third Mile Station. Comparative analyses were conducted against the Levenberg-Marquardt Feedforward Neural Network (LMFNN) and the K-Nearest Neighbour (KNN) algorithm, both of which are recognized for their reliability in addressing missing rainfall data. The results indicate that GWOFNN outperformed KNN and LMFNN in terms of the coefficient of correlation and mean absolute error performance criteria.

Source: Unimas Institutional Repository (Universiti Malaysia Sarawak)

Authors: Lai, Wai Yan, Kuok, King Kuok, Chiu, Po Chan, Md. Rezaur, Rahman, Muhammad Khusairy, Bakri

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: green

OpenAlex ID: W7002201440

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