River Water Level Forecasting with Adaptive ARIMA and Extreme Learning Machine Models

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