Browsing by Author "Jannatul Naim"

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    Climate Forecasting in Bangladesh Using Distinct Artificial Intelligence Techniques
    (2024-10-02) Jannatul Naim
    In Bangladesh, a nation that is primarily dependent on agriculture and is extremely vulnerable to weather-related dangers, weather forecasting is of utmost importance. This research paper aims to develop accurate weather prediction models for rainfall, flood, temperature, and drought using distinct artificial intelligence regression techniques, including Support Vector Regression (SVR), Decision Tree, Random Forest, k-Nearest Neighbours (KNN), and Multilayer Perceptron Neural Network (MLPNN). Additionally, the optimization algorithms Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are sequentially applied to enhance the performance of these models. We utilized historical weather data from 1980 to 2022 provided by the Bangladesh Meteorological Department (BMD). Splitting the data into 80% for training and 20% for testing, we evaluated model accuracy using Root Mean Square Error (RMSE) and Mean Squared Error (MSE). Among the techniques tested, Random Forest showed the best performance, demonstrating its efficacy in weather prediction. Therefore, we will use Random Forest to predict the temperature and rainfall for the next 10 years (2023 - 2033). From our research, we have found that over the next 10 years, the average temperature in the country will increase by approximately 0.5 degrees Celsius, and the summer season will be longer. The winter season will be shorter, and the average temperature during winter will increase compared to previous years. The annual average rainfall in the country will decrease, and this reduced rainfall, coupled with higher temperatures, will nearly double the risk of drought. Additionally, due to climate change, the frequency of floods is expected to increase suddenly in the coming years, and there is a possibility of over flood in some years. The results of this study will advance the field of weather forecasting in Bangladesh by giving insight into the effectiveness of various AI regression approaches and optimization algorithms. Accurate weather forecasting can have a substantial positive impact on a number of industries, including agriculture, disaster relief, and public safety. Keywords - Weather Prediction, Artificial Intelligence, Regression Techniques, Support Vector Regression (SVR), Decision Tree, Random Forest, k-Nearest Neighbours (KNN), Multilayer Perceptron Neural Network (MLPNN), Optimization Algorithms, Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), Weather Data.