Climate Forecasting in Bangladesh Using Distinct Artificial Intelligence Techniques
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Date
2024-10-02
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Abstract
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.