Survival Review on Different Environmental Climate Prediction Methods with Meteorological Data

Environmental mining is a multi-disciplinary field for performing efficient data analysis and data monitoring to handle the environmental impacts. Environmental climate prediction is employed to perform long-term climate variables like temperature, precipitation, humidity, sea-level rise or climate patterns. With the rapid growth of meteorological data from weather stations, satellites, and remote sensing technologies, researchers access large and complex datasets for analysis to observe the climatic trends and forecast the hazardous events. Artificial Intelligence (AI) particularly machine learning and deep learning has emerged as a great tools in environmental climate prediction by facilitating the observing of difficult and immense meteorological datasets. Machine Learning (ML) gives powerful computational techniques that learn from historical meteorological data to produce consistent climate predictions. Deep Learning (DL) has gained prominence in climate prediction owing to its ability to scrutinize large-scale time-series and spatial meteorological data. Therefore, AI models comprising ML and DL based methods are reviewed in this article for accurate and time efficient environmental climate prediction. The accurate and time efficient environmental climate prediction enables better crop yield prediction (i.e., rice crop) in agriculture domain.