Flood Magnitude Prediction Model using Autoregressive and Integrated Moving Average

Disaster is imminent and humans have to learn to manage it. Flood is one of the most common natural disasters in Nigeria with adequate negative impact on people, properties and farmlands. Flooding tends to occur during heavy down pour (rainfall) when water absorption is low. The extent of flood is proportional to the negative impact that is experienced by the victims. The adverse effects of flood cannot be overemphasized. It causes loss of life, properties and economic degradation to mention but a few. These effects can be mitigated via Flood Prediction Models (FPMs). Several successful Flood Prediction Models have been developed, but there are still limitations in the prediction of the magnitude of the flood when it occurs. Flood magnitude is the degree (extent) of the flood. The aim of this study is to build an efficient time series model in forecasting flood magnitude as a natural phenomenon that occurs frequently in nature. In this study, secondary data was collected and used for model training and testing. An autoregressive integrated moving average (ARIMA) Time Series was used to develop a flood magnitude prediction model. The ARIMA model is a combination of autoregressive (AR) and Moving average (MA) models. The implementation was done using Python Spider IDE in ANACONDA for the model simulation. The model was trained, successfully tested and evaluated via coefficient of determination and standard error diagnostic tools. The ARIMA model prediction results indicate that in the year 2024 the flood magnitude will be low level, while that of 2025 will be normal. The result also shows that the year 2026 will have high levels of flood. The results show that the coefficient of AR technique produced a score of 0.0554 indicating a higher and better performance than the MA model with a score value of -0.9077. The higher the coefficient of determination value, the better and more successful the models’ performance. Keywords: Disaster, Flood, Flood Magnitude, Machine Learning, Autoregressive, Moving Average, Time Series