FLOOD PREDICTION OF RIVER USING RAIN CLOUD IMAGES BY NEURAL NETWORK

Abstract

 In order to ensure safe evacuation of construction workers, heavy industrial machinery and machine parts at a river construction site during a flood, it is necessary to take measures several tens of hours before the flood.The person in charge of construction site refers to the results of multiple water level predictions and own experiences and decides the availability and timing of flood countermeasures. However, there are problems, 1) it takes time to prepare data such as the water level required when constructing a forecasting method, 2) the water level is different for each prediction method and the construction worker hesitates to make decisions.This article discusses the development of a neural network flood prediction technique which uses rain cloud images, which are easy to acquire. The technique was applied to Abukuma River and compared predicted values and measured values.Rain cloud images were divided to consider rain distribution with a small amount of data, and center of gravity and rainfall were used as learning data. As a result, the prediction accuracy rate increased by up to 60%.This technique can predict long-term flood by using weather forecast and can be applied in safety management of river construction.