PREDICTING RAINFALL BASED ON MACHINE LEARNING ALGORITHM: AN EVIDENCE FROM BOGURA DISTRICT, BANGLADESH
- Associate Professor, Data Mining Research Group, Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh.
- Professor, Environmental Science Research Group, Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh.
- Cite This Article as
- Corresponding Author
Accurately and timely predicting climatic variables are most challenging task for the researchers. Scientists have been trying numerous methods for forecasting environmental data with different methods and found confusing performance of different methods. Recently machine learning tools are considering as a robust technique for predicting climatic variables because these tools extracted hidden relationship from the data and can predict more correctly than existing methods. In this paperwe compare the forecasting performance of various machine learning algorithms such as Classification and Regression Trees (CART), Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (K-NN) and Random Forest (RF) in case of Bogura district in Bangladesh. The weekly rainfall related time series data such as temperature, humidity, wind speed, sunshine, minimum temperature and maximum temperature for the time period January, 1971 to December, 2015 were considered. The model evaluation criteria precision, recall and f-measure and overall accuracy confirms that Random Forest algorithm give best forecasting performance and cross validation approach which produce some graphical view model comparison also confirm that the Random Forest algorithm is the most suitable algorithm for predicting rainfall in case of Bogura district, Bangladesh during this study period.
[Md. Mostafizur Rahman and M. Sayedur Rahman (2022); PREDICTING RAINFALL BASED ON MACHINE LEARNING ALGORITHM: AN EVIDENCE FROM BOGURA DISTRICT, BANGLADESH Int. J. of Adv. Res. 10 (Aug). 850-858] (ISSN 2320-5407). www.journalijar.com
Associate Professor, Data Mining Research Group, Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh.
Article DOI: 10.21474/IJAR01/15243
DOI URL: http://dx.doi.org/10.21474/IJAR01/15243
This work is licensed under a Creative Commons Attribution 4.0 International License.