H-029
Water Level Prediction for Disaster Management Using Machine Learning Models

◎Tin Nilar Lin・Hiroshi Watanabe（Waseda Univ.）

Prediction of a flood is one of the challenges for disaster management around the world. In this paper, we have studied and compared some useful machine learning models such as KNN, SVR and Linear Regression for getting better water level prediction. The proposed approach is applied to Ayeyarwady River in Myanmar. The future water level is predicted based on the time series data of past water levels. By the experiment, KNN (K-Nearest Neighbour) model has shown the least mean absolute error and the error rate is just 0.17%. The predicted output of the proposed model agrees in the actual water level.