Abstract
Natural disasters have tended to increase and become more severe over the last decades. A preparation measure to cope with future floods is flood forecasting in each particular area for warning involved persons and resulting in the reduction of damage. Machine learning (ML) techniques have a great capability to model the nonlinear dynamic feature in hydrological processes, such as flood forecasts. Internet of Things (IoT) sensors are useful for carrying out the monitoring of natural environments. This study proposes a machine learning-based flood forecast model to predict average regional flood inundation depth in the Erren River basin in south Taiwan and to input the IoT sensor data into the ML model as input factors so that the model can be continuously revised and the forecasts can be closer to the current situation. The results show that adding IoT sensor data as input factors can reduce the model error, especially for those of high-flood-depth conditions, where their underestimations are significantly mitigated. Thus, the ML model can be on-line adjusted, and its forecasts can be visually assessed by using the IoT sensors’ inundation levels, so that the model’s accuracy and applicability in multi-step-ahead flood inundation forecasts are promoted.
Highlights
Flood is one of the most disruptive natural hazards, which causes significant damage to life, agriculture, and economy, and has a great impact on city development
We propose a methodology that couples machine learning models, i.e., the recurrent nonlinear autoregressive with exogenous inputs (RNARX) model [20], with the Internet of Things (IoT) sensor data for providing multi-step-ahead average regional inundated depth (ARID) during storm events
There is a temporal relationship between observations of the rainfall and IoT sensors’
Summary
Flood is one of the most disruptive natural hazards, which causes significant damage to life, agriculture, and economy, and has a great impact on city development. Physically based models have been commonly used and showed great capabilities for flood estimation, while they often require hydro-geomorphological monitoring datasets and intensive computation, which prohibits short-term prediction [1–3]. Statistical models, such as the multiple linear regression (MLR) [4–6] and autoregressive integrated moving average (ARIMA) [7–10] are frequently used for flood modeling. Their capability for short-term forecasting has been restricted because of the nonlinear dynamic feature of storm events resulting in a lack of accuracy and robustness of the statistical methods [11]
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