AN ARTIFICIAL NEURAL NETWORK BASED DEVELOPMENT OF CLOUD BURST FORECASTING MODEL BY USING TIME-SERIES DATA

Over the last decades, weather forecasting models using the numerical data of various previous years have been improving steadily to provide a more predictable and accurate model. However, the atmosphere conditions are a highly chaotic system and always vary with time and weather forecasts are a major benefit for society and sustainable development. So, in this research we focus to develop a model using the optimized Artificial Neural Network (ANN) for prediction of cloud bursting in India and developed model in known as Cloud Burst Foresting (CBF) model for forecasting of rainfall or cloud burst based on the previous record of the bursting in different state. Here the concept of Particle Swarm Optimization (PSO) is used as an optimization technique in preprocessing step to separate the previous year rainfalls data into two categories such minimum and maximum rainfalls. Basically, PSO separate the cloud burst recorded data using a novel fitness function that help to train the CBF model accuracy and if training is better, then the prediction accuracy will be high. By utilizing the concept of optimized ANN, the prediction accuracy is high in terms of percentage of correct predication and with minimum percentage of incorrect prediction. At last, the performance of the model is calculated to validate the proposed CBF model and this shows that it is possible to use ANN as a machine learning technique in order to estimate future cloud burst forecast uncertainty from past forecasts data of bursting. The main constraint in the performance of our proposed CBF model seems to be the number of past forecasts available for training the ANN.