Machine Learning Based Weather Forecasting and Climate Prediction Models

Abstract

Weather forecasting and climate prediction is an essential component in environmental monitoring, Disaster Management, Agriculture, Transportation and Renewable energy planning. The traditional forecasting techniques are sometimes not suitable for large-scale meteorological datasets and complex interactions in the atmosphere. In recent years, machine learning and AI technologies have proven to be effective in enhancing the accuracy of forecasts and computational efficiency. The machine learning-based weather forecasting and climate prediction models considered in this study are Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and hybrid deep learning architectures. This paper will explore the use of intelligent forecasting systems to analyse the atmosphere's parameters like temperature, rainfall, humidity, wind speed and solar radiation. Furthermore, the study touches upon new technologies like Internet of Things (IoT) based environmental monitoring, cloud computing, blockchain systems, and AI analytics, which are revolutionizing modern meteorological infrastructures. Important challenges of data quality, computational complexity, climate uncertainty and model interpretability are also underscored. Future research directions focussing on explainable AI, hybrid forecasting systems and smart environmental monitoring platforms are also addressed. In conclusion, the study shows that machine learning-based forecasting systems have great promise in enhancing the accuracy of climate forecasts, to aid in disaster preparedness, and in the field of sustainable environmental management.