Automated climate prediction using pelican optimization based hybrid deep belief network for Smart Agriculture

Abstract

A significant part of the economies of several nations throughout the world, including India, where agriculture accounts for 16% of the total economy. Due to their dynamic and turbulent natures, climate prediction in this region is the most difficult because statistical methodologies can't provide accurate predictions. Typically, weather forecasts were made using extraordinarily complex physics techniques that took into account various atmospheric conditions over a long time. These circumstances were usually unstable as a result of weather system disturbances, which led to the development of techniques for making unreliable predictions. In this study, an Automated Climate Prediction for Smart Agriculture is developed utilizing a Pelican Optimization-based Hybrid Deep Belief Network (ACP-POHDBN). The purpose of the ACP-POHDBN approach is to identify the appropriate meteorological conditions. The acp-POHDBN method uses the test data to calculate the appropriate weather conditions. Pre-processing, prediction, and hyperparameter tuning are the three key steps that make up the proposed ACP-POHDBN approach. The min-max normalization procedure is the primary procedure used by the ACP-POHDBN model to convert the meteorological data into a standard format. In addition to pre-processing data, the Deep Belief Network (DBN) model is used to forecast weather conditions. Finally, the DBN method's hyperparameters (epoch, batch size, and learning rate) may be properly tuned using the POA-based hyperparameter tuning technique. The development of a Pelican Optimization Algorithm (POA)-based hyperparameter optimizer for the process of predicting the climate demonstrates the originality of the study. The proposed algorithm over other recent approaches with maximum accuracy of 95.03%, sensitivity of 95.03%, specificity of 95.03%, and F-score of 95.03%.