Quantitative Rainfall Prediction: Deep Neural Network-Based Approach Debraj Dhar, Sougato Bagchi, Chayan Kumar Kayal, Soham Mukherjee and Sankhadeep Chatterjee Abstract Forecasting the weather has always been a challenge using conventional methods of climatology, analogue and numerical weather prediction. To improvise the prediction of weather much further, the proposed method can be used. In this work, authors proposed a method which uses the advantages of deep neural network to achieve high degree of performance and accuracy compared to the old conventional ways of forecasting the weather. It is done by feeding the perceptrons of the DNN some specific features like temperature, relative humidity, vapor and pressure. The output generated is a highly accurate amount of the rainfall based on the given input data. Keywords Forecast · Rainfall prediction · Deep neural network · Perceptrons 1 Introduction A meteorologist’s biggest job is to predict how the weather will change depending upon the climate changing parameters and it has always been a challenging job to predict this on a higher scale of accuracy [1]. Using DNN (deep neural network) [2], the task of predicting the weather [3] can be achieved with much greater accuracy. D. Dhar · S. Bagchi · C. K. Kayal · S. Mukherjee · S. Chatterjee (B ) Department of Computer Science & Engineering, University of Engineering & Management, Kolkata, India e-mail: chatterjeesankhadeep.cu@gmail.com D. Dhar e-mail: debrajdhar100@gmail.com S. Bagchi e-mail: sougato97@gmail.com C. K. Kayal e-mail: chayankayal32@gmail.com S. Mukherjee e-mail: mukherjee.soham56@gmail.com © Springer Nature Singapore Pte Ltd. 2019 M. Chakraborty et al. (eds.), Proceedings of International Ethical Hacking Conference 2018, Advances in Intelligent Systems and Computing 811, https://doi.org/10.1007/978-981-13-1544-2_37 455