Improving flood forecasting through feature selection by a genetic algorithm - experiments based on real data from an Amazon rainforest river

This paper addresses the problem of feature selection aiming to improve a flood forecasting model. The proposed model is carried out through a case study that uses 18 different time series of thirty-five years of hydrological data, forecasting the level of the Xingu River, in the Amazon rainforest in Brazil. We employ a Genetic Algorithm for the task of feature selection and exploit several different genetic parameters seeking to improve the accuracy of the prediction. The features selected by the Genetic Algorithm are used as input of a Linear Regression model that performs the forecasting. A statistical analysis verifies that the final model can predict the river level with high accuracy, which obtains a coefficient of determination equal to 0.988. Hence, the proposed Genetic Algorithm showed to be successful in selecting the most relevant features.

Automated summary

This study addresses the problem of feature selection in order to improve a flood forecasting model. Through a case study using 18 different time series of thirty-five years of hydrological data, the proposed model predicts the level of the Xingu River in the Amazon rainforest in Brazil. By employing a Genetic Algorithm for feature selection and a Linear Regression model for forecasting, the final model achieves a high accuracy in predicting the river level, with a coefficient of determination equal to 0.988.