Retracted: A Systematic Review of Machine Learning and Artificial Intelligence Methods to Tackle Climate Change Impacts

Weather prediction is vital in sustaining human beings and all living objects. It’s been years since humans have been forecasting weather for growing desired crops and migrating from region to region. With this era of advancement and automation, machine learning and deep learning have advanced in all domains, and innovations are taking place for the betterment of beings. The field of weather is no longer far away from its advancements. Precise and timely weather predictions can save crops from being demolished and alarm farmers to protect their fields from getting wasted. Classic numerical weather prediction (NWP) has faced numerous challenges recently. It has built an ambiguity in the systematic data. Still, with the advancement of IOT and machine learning techniques like motion detection, speech recognition, and computer vision, it has been seen that these techniques can predict change in an environment more precisely and effectively. Currently, several innovations in weather forecasting employ machine learning and artificial neural network techniques, and these researchers see precise benchmarks. In this paper, we study the different methods of deep learning, machine learning, and advanced IOT devices for better prediction of weather and their comparative analysis.