Abstract:
Weather forecasting has several effects in our everyday life from farming to event planning. In the northwestern part of Bangladesh, various natural calamities cause the tragic death of many people and economic loss which impacts the total economic growth in Bangladesh. However, the nonlinear relationship between the input parameters and output data in the weather forecasting system makes it more complex. This study investigates the machine learning-based weather forecasting model for the north-western part of Bangladesh to enhance the accuracy of forecasting results in short periods. Artificial neural networks and extreme learning machine algorithms were used for a strong weather prediction purpose. In this experiment, thirty years of historical weather data of temperature, rain, wind, and humidity from seven weather stations in the northwestern part were collected from the Bangladesh Meteorological Department (BMD). The Extreme Machine Learning (ELM) model performs better than Artificial Neural Network and the accuracy rate is 95%.
