A Survey on Rainfall Prediction using Artificial Neural Network

Abstract

ABSTRACT Rainfall prediction is one of the most important and challenging task in the modern world. In general, climate and rainfall are highly non-linear and complicated phenomena, which require advanced computer modeling and simulation for their accurate prediction. An Artificial Neural Network (ANN) can be used to predict the behavior of such nonlinear systems. ANN has been successfully used by most of the researchers in this field for the last twenty-five years. This paper provides a survey of available literature of some methodologies employed by different researchers to utilize ANN for rainfall prediction. The survey also reports that rainfall prediction using ANN technique is more suitable than traditional statistical and numerical methods . General Terms Rainfall, Artificial Neural Network, Prediction. Keywords Rainfall, Neural Network, BPN, RBF, SVM, SOM, ANN. 1. INTRODUCTION Rainfall brings the most important role in the matter of human life in all kinds of weather happenings. The effect of rainfall for human civilization is very colossal. Rainfall is natural climatic phenomena whose prediction is challenging and demanding. Accurate information on rainfall is essential for the planning and management of water resources and also crucial for reservoir operation and flooding prevention. Additionally, rainfall has a strong influence on traffic, sewer systems and other human activities in the urban areas. Nevertheless, rainfall is one of the most complex and difficult elements of the hydrology cycle to understand and to model due to the complexity of the atmospheric processes that generate rainfall and the tremendous range of variation over a wide range of scales both in space and time. Thus, accurate rainfall prediction is one of the greatest challenges in operational hydrology, despite many advances in weather forecasting in recent decades. Rainfall means crops; and crop means life. Rainfall prediction is closely related to agriculture sector, which contributes significantly to the economy of the nation. On a worldwide scale, large numbers of attempts have been made by different researchers to predict rainfall accurately using various techniques. But due to the nonlinear nature of rainfall, prediction accuracy obtained by these techniques is still below the satisfactory level. Artificial neural network algorithm becomes an attractive inductive approach in rainfall prediction owing to their highly nonlinearity, flexibility and data driven learning in building models without any prior knowledge about catchment behavior and flow processes. Artificial neural networks have been successfully used in these days in various aspects of science and engineering because of its ability to model both linear and non-linear systems without the need to make assumptions as are implicit in most traditional statistical approaches. ANN has been used as an effective model over the simple linear regression model. This paper provides a literature survey on rainfall prediction using different neural networks used by different researchers. The paper also discusses the concept of some neural network architectures briefly which will be helpful to the new researchers in this field. The objective of this survey is to make the prediction of rainfall more accurate in the recent future. The paper has been constructed with the sections. Section II discussed the concept of neural network. Differentmethodologies used by researchers to predict rainfall has been discussed in section III. Section IV discusses the literature survey of rainfall prediction all over the world. At last a conclusion is discussed in the section V.