Flood Forecasting Using Artificial Neural Networks: an Application of Multi-Model Data Fusion technique

Abstract

Floods are among the natural disasters that cause human hardship and economic loss. Establishing a viable flood forecasting and warning system for communities at risk can mitigate these adverse effects. However, establishing an accurate flood forecasting system is still challenging due to the lack of knowledge about the effective variables in forecasting. The present study has indicated that the use of artificial intelligence, especially neural networks is suitable for flood forecasting systems (FFSs). In this research, mathematical modeling of flood forecasting with the application of Artificial Neural Networks (ANN) and data fusion technique were used in estimating the flood discharge. Sensitivity analysis was performed to investigate the significance of each model input and the best MLP ANN architecture. The data used in developing the model comprise discharge at different time steps, precipitation and antecedent precipitation index for a major river basin. Application of model on a case study (Karun River in Iran) indicated that rainfall-runoff process using data fusion approach produces results with higher degrees of precision.