Performance Analysis of Multilayer Perceptron Neural Network Models in Week-Ahead Rainfall Forecasting

Lemuel Clark P Velasco,Bryan F,Junneil C,Ruth P,Mohammad Shahin

doi:10.14569/ijacsa.2019.0100374

Abstract

Multilayer perceptron neural network (MLPNN) is considered as one of the most efficient forecasting techniques which can be implemented for the prediction of weather occurrence. As with any machine learning implementation, the challenge on the utilization of MLPNN in rainfall forecasting lies in the development and evaluation of MLPNN models which delivers optimal forecasting performance. This research conducted performance analysis of MLPNN models through data preparation, model designing, and model evaluation in order to determine which parameters are the best-fit configurations for MLPNN model implementation in rainfall forecasting. During rainfall data preparation, imputation process and spatial correlation evaluation of weather variables from various weather stations showed that the geographical location of the chosen weather stations did not have a direct correlation between stations with respect to rainfall behavior leading to the decision of utilizing the weather station having the most complete weather data to be fed in the MLPNN. By conducting performance analysis of MLPNN models with different combinations of training algorithms, activation functions, learning rate, and momentum, it was found out that MLPNN model having 100 hidden neurons with Scaled Conjugate Gradient training algorithm and Sigmoid activation function delivered the lowest RMSE of 0.031537 while another MLPNN model having the same number of hidden neurons, the same activation function but Resilient Propagation as training algorithm had the lowest MAE of 0.0209. The results of this research showed that performance analysis of MLPNN models is a crucial process in model implementation of MLPNN for week-ahead rainfall forecasting.

Highlights

Multilayer perceptron neural network (MLPNN) is considered as a widely used artificial neural networks architecture in predictive analytics functions
Performance analysis of MLPNN models was conducted in this study among the weather station datasets in order to identify which MLPNN models can be optimally implemented in week-ahead rainfall forecasting
Techniques on weather data preparation, MLPNN model design along with its training and testing was conducted in this study

Summary

Introduction

Multilayer perceptron neural network (MLPNN) is considered as a widely used artificial neural networks architecture in predictive analytics functions. The design of MLPNN is motivated by the structure of a biological neuron system capable of parallel processing like a human brain, but the processing elements of this machine learning tool has gone far from their biological inspiration [1, 2, 3] For this reason, MLPNN have been successfully used by most of the researchers in the field of forecasting, science and engineering to predict the behavior of both linear and nonlinear systems without the need to make assumptions that are implicit in most traditional statistical approaches [2, 4, 5, 6]. Modelers and researchers who use MLPNN in forecasting still rely on performance analysis of MLPNN models in order to implement domain-specific applications that generate close to accurate predictions

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