Techniques Leveraging from Artificial Intelligence for the Prediction of Climate Change in Pakistan: A Systematic Literature Review

Abstract

Forecasting weather is crucial for sustaining life and ecosystems. Throughout history, farmers have predicted weather patterns to optimize crop growth. Weather prediction can be useful for other more crucial factors as well, including flood and drought predictions. In today's era of technological advancement, machine learning, and deep learning have made significant strides across various domains, including weather forecasting. For safeguarding crops and alarming farmers for the protection of their fields, it is necessary to have the ability to provide precise and timely weather predictions. While traditional weather prediction has faced advancements in IoT and machine learning, such as motion detection, speech recognition, and computer vision, it also offers more accurate environmental change predictions. In Pakistan, rising temperatures, erratic weather patterns, and changing precipitation pose significant challenges for the population. Climate change and the emission of harmful industrial gases are accountable for altering rainfall patterns, subsequently impacting economic growth since Pakistan's major economy relies on it. At present, numerous advancements in weather prediction utilize artificial intelligence techniques resulting in notable benchmarks for researchers. This study explores diverse methods of deep learning, machine learning, and advanced IoT devices to enhance weather prediction through a systematic literature review.