This statement being proved by the studies in , shows that the machine learning based model give a better prediction than the linear regression algorithm to predict rainfall. An artificial neural network computational tool is strong and data driven.Its characteristic is self-adaptive, flexible which has the capacity to learn and handle nonlinear and tough underlying characteristics of any physical process with high grade of accuracy. Thanks to its ability in providing a better solution to complex problems that significantly reduce noise and optimize approximations,ANN is getting more attention from a lot of scholars in their attempts to unravel the inner workings of weather forecasting process. Traditional methodologies are often found to encounter constant challenges in addressing non-linear physical, spatial and temporal processes as well as the underlying uncertainties that exist in certain parameters used.
The attributes of the ANN model have therefore made it clear about its suitability to be deployed in studies pertaining to weather forecasting. ANN consists of several techniques such as the feed forward neural networks,back propagation neural networks and radial basis function neural network. RBFNN structure is similar to typical ANN structure, rolling bench that consists of one hidden layer that contain three main parameter that is output weights,widths and centered. highlights that RBFNN is the most suitable machine learning technique to be deployed in the study that scrutinizes rainfall forecast in comparison to other techniques including Generic Programming, Support Vector Regression, M5-Rules, M5-Model trees and k-Nearest Neighbor. This argument has been further resonated by , in which they conclude that RBFNN provides higher accuracy of results in rainfall forecast as compared to the back propagation neural network technique. Besides, another study conducted by finds out that the use of multilayer perceptron is suitable for in Atner while RBFNN in Dharni RBFNN.
Thus, we have proposed a model of weather forecasting using Radial Basis Function Neural Networks order to predict weather in a very effective and efficient way and to enhance the rainfall prediction in Malaysia by utilizing the RBFNN model. Other than its proven ability to provide a better rainfall prediction,this model also employs a better algorithm that is far more simplistic and with a faster learning capability. Rainfall prediction relies heavily on important weather-related parameters that include air pressure, temperature and wind speed that should certainly be considered in the development of a sound algorithm in rainfall prediction. Therefore, an analysis has been conducted across a few meteorology datasets as data input centered around the important parameters such as temperature, wind speed,grow table hydroponic humidity as well as air pressure. The concept of Artificial Neural Network is a network composed by a collection of processing units that are computer-programmed based on the working of human brain. The human brain is a complex system capable of processing a large amount of information at a time. The neural network is a processor that is made up of artificial neurons as the main the processing element.
The application of the neural network has been extensive in the areas of pattern classification,grouping, prediction and optimization among many others. Rainfall is one of the natural phenomena with non-linear attributes, thus, requiring anon-statistical method that is far more complex as to understand the rainfall behavioral patterns just like how the Artificial Neural Network does.RBFNN was first introduced by Broom head dan Lowe in the year 1988.This paper use the algorithm of Radial Basis Function Network because RBFN is one of the neural networks with capability to predict non-linear attributes and with faster learning speed. RBFN is also a particularly distinctive artificial neural network as it leverages on the basis function network as the active function. The application of RBFN is used widely particularly for function approximation, time series forecasting and classification. RBFN model that comprises of three layers, the input layer, the hidden layer and the output layer is shown in Figure 1.Every layer is made up of nodes that connected one another as illustrated by Figure 1. As demonstrated in Figure 1, every neuron is also connected with one another. This information will later enter the hidden layer where the learning process shall commence. This directly implies that the calculations are performed so that system learns the patterns by manipulating the weighted value until it matches the output value.Thus, the limitation of radial basis function formula must contain only real values. In this study, statistical analysis that has been used is root mean square error and the percentage of accuracy. The optimal RBFNN structure is determined by manipulating the hidden neuron value between 4 and 99. The RMSE values between the predicted values and measured values for the RNFNN model are illustrated in Table 1. This study discovers that the hidden neuron value has less affect on the performance of the RMSE value which is between49.92% up to 51.10%.