Weather Prediction in Bangladesh Using Distinct Artificial Intelligence Techniques

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dc.contributor.authorJoysri Rani Das
dc.date.accessioned2025-11-23T06:53:11Z
dc.date.available2025-11-23T06:53:11Z
dc.date.issued2024-05-30
dc.description.abstractWeather forecasting holds immense significance in Bangladesh due to its heavy reliance on agriculture and vulnerability to weather-related risks. This study aims to develop reliable weather forecast models for Rainfall, Temperature, and Humidity using artificial intelligence algorithms such as Decision Trees, Random Forests, K-Nearest Neighbors, Support Vector Machines, and Multilayer Perceptron Neural Networks. Historical weather data from the Bangladesh Meteorological Department (BMD) undergo preprocessing to handle missing values and outliers. Various AI techniques are then applied, leveraging their ability to address complex and nonlinear interactions in data. The prediction models are evaluated using performance metrics like Mean Absolute Error and Root Mean Square Error, Root Mean Square Error, R-square accuracy, R-square Value, and Relative Absolute Error. This research contributes to advancing weather forecasting in Bangladesh, benefiting sectors like agriculture, disaster management, and public safety. We divided our Temperature, Humidity, and Rainfall Datasets into two intervals: 1999-2022 which consist of 34 weather stations and 1980- 2022 consist of 26 weather stations. And we used 80% of our data for training and 20% for testing our models. We analyzed our data using both interval and did a detailed analysis of the yearly, monthly, and seasonal trends of Temperature, Rainfall, and Humidity. It also highlights the relationship between These weather parameters. From our results, we have found that Random Forest and Extreme Gradient Boosting Perform better than the Support vector machine, K-Nearest Neighbor, Multilayer neural network, and Decision Tree algorithm. And It is also noticeable that The results of the time interval 1999- 2022 are better than the time interval 1980-2022. KEYWORDS: Artificial Intelligence; Machine Learning; Random Forest; Decision Tree; Support Vector Machine; K-Nearest Neighbor; Extreme Gradient Boosting; MSE; RMSE; MAE; RAE; R- Square Value; R-Square Accuracy.
dc.identifier.urihttps://dspace.nstu.ac.bd/handle/123456789/149
dc.titleWeather Prediction in Bangladesh Using Distinct Artificial Intelligence Techniques

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