Author: Salem Albalawi
Albalawi, Salem, 2023 River Water Level Forecasting with Adaptive ARIMA and Extreme Learning Machine Models, Flinders University, College of Science and Engineering
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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.
Keywords: Mathematics, ARIMA, ELM, forecasting
Subject: Mathematics thesis
Thesis type: Masters
Completed: 2023
School: College of Science and Engineering
Supervisor: Greg Falzon