Application of Artificial Neural Networks to Rainfall Forecasting in the Geum River Basin, Korea

Jeongwoo Lee,Jeong Lee,Hyeonjun Kim,Nam Kim,Chul-Gyum Kim

doi:10.3390/w10101448

Abstract

This study develops a late spring-early summer rainfall forecasting model using an artificial neural network (ANN) for the Geum River Basin in South Korea. After identifying the lagged correlation between climate indices and the rainfall amount in May and June, 11 significant input variables were selected for the preliminary ANN structure. From quantification of the relative importance of the input variables, the lagged climate indices of East Atlantic Pattern (EA), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), East Pacific/North Pacific Oscillation (EP/NP), and Tropical Northern Atlantic Index (TNA) were identified as significant predictors and were used to construct a much simpler ANN model. The final best ANN model, with five input variables, showed acceptable performance with relative root mean square errors of 25.84%, 32.72%, and 34.75% for training, validation, and testing data sets, respectively. The hit score, which is the number of hit years divided by the total number of years, was more than 60%, which indicates that the ANN model successfully predicts rainfall in the study area. The developed ANN model, incorporated with lagged global climate indices, could allow for more timely and flexible management of water resources and better preparation against potential droughts in the study region.

Highlights

Rainfall prediction is of great importance to prevent flooding and manage water resources, saving lives and property and securing economic activities
The artificial neural network (ANN) is a form of machine learning technique that has been widely used in rainfall prediction given its ability to identify highly complex non-linear relationships between input and output variables without the need to understand the nature of the physical processes
The results show that the network performance with different numbers of hidden neurons was not significantly different

Summary

Introduction

Rainfall prediction is of great importance to prevent flooding and manage water resources, saving lives and property and securing economic activities. Accurate rainfall forecasting is a challenging task in operational water resources management [1]. Models, statistical methods, and machine learning techniques. Machine learning techniques, such as artificial neural network (ANN), k-nearest neighbor, support vector machine, and random forest model, are more suitable for rainfall forecasting because physical processes affecting rainfall occurrence are highly complex and non-linear [2]. The ANN is a form of machine learning technique that has been widely used in rainfall prediction given its ability to identify highly complex non-linear relationships between input and output variables without the need to understand the nature of the physical processes

Methods

Results

Conclusion