Falzon, GregAlbalawi, Salem2023-12-242023-12-242023-12-19https://hdl.handle.net/20.500.14154/70375Accurately forecasting river water levels has profound implications for various socio- economic and environmental aspects. The immediate benefit lies in the realm of disaster preparedness. Flooding, a common and devastating natural disaster, can be better managed with an accurate forecast of river water levels (Ward et al., 2015). By predicting river swellings in advance, local authorities can implement evacuation plans, reducing economic damages and loss of life. Moreover, these forecasts play a pivotal role in water resource management. With increasing water scarcity issues worldwide, having precise data on river water levels helps distribute and efficiently use freshwater resources (Adamowski & Karapataki, 2010). Predictive models also aid in managing dams and reservoirs, ensuring optimal electricity generation and sustainable ecological flows downstream.This dissertation delves into the predictive capabilities of two prominent modelling techniques, Extreme Learning Machine window (ELM) and the Adaptive Autoregressive Integrated Moving Average (ARIMA), for short-term forecasting of river water levels. With increasing environmental uncertainties, accurate predictions of water levels are crucial for effective water management and flood prevention. Through rigorous data processing and model training, this research employs recent river data to evaluate the performance of both models over four forecasting horizons: 1-day, 3-day, 5-day, and 7-day. The evaluation metrics, including Root Mean Squared Errors (RMSE), Mean Absolute Deviation (MAD), and Mean Squared Errors (MSE), revealed insightful patterns about the accuracy and reliability of each model. Further, the distribution of forecast errors was analysed to understand the consistency and potential biases in predictions. The thesis findings indicate nuanced differences in the performance of Adaptive ELM and Adaptive ARIMA, shedding light on the specific conditions and scenarios where one model may outperform the other. This comparative analysis serves as a comprehensive guide for researchers and practitioners in selecting the most suitable model for river water level forecasting under varying circumstances. The insights from this study also pave the way for future research opportunities, exploring the integration of both models or the incorporation of additional data sources to enhance forecasting accuracy.79enmathThesis