Optimized cascaded CNN for intelligent rainfall prediction model: a research towards statistic-based machine learning

Abstract

Abstract Using artificial intelligence to anticipate weather conditions, according to prior research, can provide positive results. Forecasts of meteorological time series can aid disaster-prevention personnel in making more informed judgments. Deep learning has recently been shown to be a viable technique for solving complicated issues and analyzing large amounts of data. Statistical learning theory is a type of machine learning that combines statistics and functional analysis. To answer the problem of rainfall forecasting, this study employs a statistically-based machine learning technique. The benchmark meteorological data is first pre-processed using data augmentation and data normalization. The machine learning is then given statistical characteristics such as "first order and second order statistical information" for prediction. The Adaptive Searched Scaling factor-based Elephant Herding Optimization (ASS-EHO)is used to optimize the Cascaded Convolutional Neural Network (CNN) for rainfall prediction as an improved prediction model, with parameter tuning such as cascaded CNN count, hidden neuron count, and activation function optimized. The new prediction model is a statistical-based machine learning model in which the aim function is the reduction of the cross entropy loss function. The results are compared to established statistical methodologies, demonstrating that the model may be used to estimate daily rainfall quickly and accurately.