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.
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Dhar, D., Bagchi, S., Kayal, C.K., Mukherjee, S., Chatterjee, S. (2019). Quantitative Rainfall Prediction: Deep Neural Network-Based Approach. In: Chakraborty, M., Chakrabarti, S., Balas, V., Mandal, J. (eds) Proceedings of International Ethical Hacking Conference 2018. Advances in Intelligent Systems and Computing, vol 811. Springer, Singapore. https://doi.org/10.1007/978-981-13-1544-2_37
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DOI: https://doi.org/10.1007/978-981-13-1544-2_37
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