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A Novel Machine Learning Based Approach for Rainfall Prediction

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Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1 ( ICTIS 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 83))

Abstract

The climate changes effortlessly nowadays, prediction of climate is very hard. However, the forecasting mechanism is the vital process. It is also a valuable thing as it is the important part of the human life. Accordingly to the research, the weather forecast of rainfall intensity conducted. The remarkable commitment of this proposal is in the implementation of a hybrid intelligent system data mining technique for solving novel practical problems, Hybrid Intelligent system data mining consists of the combination of Artificial Neural Network and the proper usage of Genetic Algorithm. In this research, Genetic algorithm is utilized the type of inputs, the connection structure between the inputs and the output layers and make the training of neural network more efficient. In ANN, Multi-layer Perceptron (MLP) serves as the center data mining (DM) engine in performing forecast tasks. Back Propagation algorithm used for the trained the neural network. During the training phase of the proposed approach, it gains the optimal values of the connection weights which, in fact, utilized as the part of the testing phase of the MLP. Here, the testing phase is used to bring about the rainfall prediction accuracy. It may be noted that the information/data is used to cover the information from the variables namely temperature, cloud fraction, wind, humidity, and rainfall.

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Authors and Affiliations

  1. U & P U. Patel Department of Computer Engineering, Chandubhai S Patel Institute of Technology, Changa, India

    Niharika Solanki & Gaurang Panchal

Authors
  1. Niharika Solanki
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  2. Gaurang Panchal
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Corresponding author

Correspondence to Gaurang Panchal .

Editor information

Editors and Affiliations

  1. Department of CSE, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Sabar Institute of Technology for Girls , Ahmedabad, Gujarat, India

    Amit Joshi

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Solanki, N., Panchal, G. (2018). A Novel Machine Learning Based Approach for Rainfall Prediction. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1. ICTIS 2017. Smart Innovation, Systems and Technologies, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-319-63673-3_38

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  • DOI: https://doi.org/10.1007/978-3-319-63673-3_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63672-6

  • Online ISBN: 978-3-319-63673-3

  • eBook Packages: EngineeringEngineering (R0)

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