A Novel Machine Learning Based Approach for Rainfall Prediction

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.