Weather Forecasting using Radial Basis Function Network
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Date
2025-05
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Kenyatta University
Abstract
Weather forecasting plays a vital role across sectors such as agriculture, disaster management, transportation, and urban planning. While traditional models like Numerical Weather Prediction (NWP) and Autoregressive Integrated Moving Average (ARIMA) have been instrumental, they often fall short in handling the nonlinear and chaotic nature of atmospheric systems, especially in long-term predictions. This study addresses these limitations by applying and evaluating the performance of machine learning models—Radial Basis Function Networks (RBFNs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory networks (LSTMs)—using a decade-long dataset (2013–2023) obtained from the Kenya Meteorological Department, encompassing temperature, humidity, rainfall, sea-level pressure, and windspeed. Data preprocessing involved K-Nearest Neighbours (KNN) imputation and Min-Max normalization. Models were developed using TensorFlow in Python, optimized through grid search, and deployed via Docker containers on Google Cloud Platform to simulate operational forecasting conditions. Comparative analysis showed that RBFNs outperformed CNNs and LSTMs across all variables, achieving the lowest Root Mean Squared Error (e.g., 0.239°C for temperature versus 5.975°C and 8.701°C for CNN and LSTM, respectively) and the fastest training time, making them highly suitable for real-time forecasting in resource-constrained environments. Hybrid models combining RBFNs with CNNs and LSTMs showed improved accuracy, particularly for complex variables like rainfall. These findings suggest that RBFNs, either standalone or in hybrid configurations, provide an efficient, scalable, and accurate alternative for operational weather forecasting. The study concludes by recommending further exploration of hybrid model integration and the incorporation of satellite imagery to enhance spatial resolution.
Description
Research Project Documentation Submitted in Partial Fulfillment of the Requirements for the Award of the Degree of Master of Science in Computer Science in the School of Pure and Applied Sciences of Kenyatta University, May 2025.
Supervisor
Abraham Matheka Mutua