Deep-learning algorithm development for river flow prediction: PNP algorithm

Abstract

Deep-learning algorithms developed in recent decades have performed well in prediction and classification using accumulated big data. However, as climate change has recently become a more serious global problem, natural disasters are occurring frequently. When analyzing natural disasters from the perspective of a data analyst, they are considered outliers, and the ability to predict outliers (natural disasters) using deep-learning algorithms based on big data acquired by computers is limited. To predict natural disasters, deep-learning algorithms must be enhanced to be able to predict outliers based on information such as the correlation between the input and output. Thus, algorithms that specialize in one field must be developed, and specialized algorithms for abnormal values must be developed to predict natural disasters. Therefore, considering the correlation between the input and output, we propose a positive and negative perceptron (PNP) algorithm to predict the flow rate of rivers using climate change-sensitive precipitation. The PNP algorithm consists of a hidden deep-learning layer composed of positive and negative neurons. We built deep-learning models using the PNP algorithm to predict the flow of three rivers. We also built comparative deep-learning models using long short-term memory (LSTM) to validate the performance of the PNP algorithm. We compared the predictive performance of each model using the root mean square error and symmetric mean absolute percentage error and demonstrated that it performed better than the LSTM algorithms.