Abstract
In this paper, a fuzzy rule-based K Nearest Neighbor (KNN) approach is proposed to forecast rainfall. All the existing rainfall forecasting systems are first examined, and all the climatic factors that cause rainfall are then briefly analyzed. Based on that analysis, a new hybrid method is proposed to forecast rainfall for a certain day. In the proposed system, a robust knowledge base is constructed using some belief rules. The belief rules are formed using the fuzzy membership functions on some historical data provided by Bangladesh Meteorological Department (BMD). A detailed description of the collected data as well as its observation stations is provided in this paper. Finally, KNN is applied to forecast rainfall based on some given inputs. A numeric study is provided to illustrate the forecasting accuracy of the proposed methodology.
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Acknowledgements
This study is done upon the data of Bangladesh Metrological Department. All the information, opinions, and conclusions are recommended by the authors. These materials do not reflect the opinions of Bangladesh Metrological Department.
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Zahid Hasan, M., Hossain, S., Zubair Hasan, K.M., Uddin, M.S., Ehteshamul Alam, M. (2021). Fuzzy Rule-Based KNN for Rainfall Prediction: A Case Study in Bangladesh. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Advances in Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-0586-4_41
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