Hybrid Machine Learning Model for Rainfall Forecasting

Abstract

The state of the weather became a point of attraction for researchers in recent days. It control in many fields as agriculture, the country determines the types of crops depend on state of the atmosphere. It is therefore important to know the weather in the coming days to take precautions. Forecasting the weather in future especially rainfall won the attention of many researchers, to prevent flooding and other risks arising from rainfall. This Paper presents a vigorous hybrid technique was applied to forecast rainfall by combining Particle Swarm Optimization (PSO) and Multi-Layer Perceptron (MLP) which is popular kind used in Feed Forward Neural Network (FFNN). The purpose of using PSO with MLP is not just to forecast the rainfall but, to improve the performance of the network; this was proved by comparison with various Back Propagation (BP) an algorithm such as Levenberg-Marquardt (LM) through results of Root Mean Square Error (RMSE). RMSE for MLP based PSO is 0.14 while RMSE for MLP based LM is 0.18.

Short-Term Rainfall Prediction Using Supervised Machine Learning

“…On the other hand, Abdel-Kader et al [8] showed a vigorous hybrid technique by particle swarm optimization (PSO) and multi-layer perceptron (MLP) for the prediction of rainfall. Also, Samsiahsani et al [9] evaluated many machine learning classifiers based on Malaysian data for rainfall prediction.…”

Precipitation Forecast for Thi-Qar Province of Iraq Utilizing Machine Learning Approaches

“…The results of this study highlighted that the rate of rainfall is reasonably good in various states of India in the main three months (March, April, and May). Abdel-Kader, Abd-El Salam [11] ware considered as a recent study that proposed a powerful hybrid technology has been implemented to predict rainfall by combining Particle Swarm Optimization (PSO) and Multi-Layer Perceptron (MLP) which is the popular kind used in Feed Forward Neural Network (FFNN). The proposed hybrid technique has two phases, in the first phase is developed neural network by determining the number of neurons for input layer, neurons for hidden layer, and number of neurons for output layer; in the second phase PSO mainly used for automatic generation of optimized weights which used in the first phase for training network.…”