INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY (IRJET) E-ISSN: 2395-0056 VOLUME: 06 ISSUE: 04 | APR 2019 WWW.IRJET.NET P-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 3227 Estimation of Water Level Variations in Dams based on Rainfall Data using ANN Ankita Bhapkar 1 , Sumit Bante 2 , Shubham Deshmukh 3 , Mr.Rajesh Shekokar 4 1,2,3 Student, Department of Electronics and Telecommunication, RMD Sinhgad school of Engineering, Warje, Pune-58 Assistant Professor 4 , Department of Electronics and Telecommunication, RMD Sinhgad school of Engineering, Warje, Pune-58 -------------------------------------------------------------------------***------------------------------------------------------------------------ Abstract:- A method for estimating water level at Sungai Bedup in Sarawak is presented here. The method makes use of Artificial Neural Network (ANN) – a new tool that is capable of modeling various nonlinear hydrological processes. ANN was chosen based on its ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets. In this study, the networks were developed to forecast daily water level for Sungai Bedup station. Specially designed networks were simulated using data obtained from Drainage and Irrigation Department with MATLAB 6.5 computer software. Various training parameters were considered to achieve the best result. ANN Recurrent Network using Backpropagation algorithm was adopted for this study. Keywords: Artificial Neural Network, Water Level Prediction, Flood Forecasting. 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.[1] 1.1. Artificial Neural Network (ANN) The development of Artificial Neural Networks began approximately 50 years ago, inspired by a desire to understand the human brain and emulate its functioning. Within the last two decades, it has experienced a huge resurgence due to the development of more sophisticated algorithms and the emergence of powerful computation tools. It has been proved that ANN models show better results in river stage-discharge modeling in comparison to traditional models. The human brain always stores the information as a pattern. Any capability of the brain may be viewed as a pattern recognition task. The high efficiency and speed with which the human brain processes the patterns inspired the development of ANN and its application in field of pattern recognition. ANN is a computing model that tries to mimic the human brain and the nervous system in a very primitive way to emulate the capabilities of the human being in a very limited sense. ANNs have been developed as a generalization of mathematical models of human cognition or neural biology. Comparison to a conventional statistical stage-discharge model shows the superiority of an approach. using ANN. Basic principle of ANN is shown in Fig. 1.1 Enlargement of ANN is based on the following rules: 1. Information processing occurs at nodes that are single elements and are also denoted as units, neurons or cells. 2. Signals are passed between nodes through connection links.