- Adnan A., Yolanda A. M., and Natasya F. 2021. A comparison of bagging and boosting on classification data: Case study on rainfall data in Sultan Syarif Kasim II meteorological station in Pekanbaru. Journal of Physics. https://doi.org/10.1088/1742-6596/2049/1/012053.
- Asakereh H., and Akbarzadeh Y. 2017. Simulation of temperature and precipitation changes of Tabriz Synoptic Station using statistical downscaling and Canesm2 climate change model output. Journal of Geography and Environmental Hazards 21: 153-174. https://doi.org/10.22067/GEO.V6I. (In Persian with English abstract)
- Barrera A., Oyedele L., Bilal M., Akinosho T., Delgado J., and Akanbi L. 2022. Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting. Machine Learning with Applications 7. https://doi.org/10.1016/j.mlwa.2021.100204.
- Breiman L. 1996. Bagging predictors. Machine Learning 24: 123–140.
- Bushara N., and Abraham A. 2015. Novel Ensemble Method for Long Term Rainfall Prediction. International Journal of Computer Information Systems and Industrial Management Applications 7: 116-130.
- Cabezuelo 2022. Prediction of Rainfall in Australia Using Machine Learning. Information 13(163). https://doi.org/10.3390/info13040163.
- Choubin B., Zehtabian Gh., Azareh A., Rafiei‑Sardooi E., Sajedi‑Hosseini F., and Kisi O. 2018. Precipitation forecasting using classification and regression trees(CART) model: a comparative study of different approaches. Environmental Earth Sciences. https://doi.org/10.1007/s12665-018-7498-z.
- Dastourani M. T., Habibipoor A., Ekhtesasi M. R., Talebi A., and Mahjoobi J. 2013. Evaluation of the Decision Tree Model in Precipitation Prediction (Case study: Yazd Synoptic Station). Iran-Water Resources Research 8(3): 14-27. (In Persian with English abstract)
- Endalie D., Hailea G., and Taye W. 2022. Deep learning model for daily rainfall prediction: case study of Jimma, Ethiopia. Water Supply 3(22). https://doi.org/10.2166/ws.2021.391.
- Kalmegh S. 2015. Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News. International Journal of Innovative Science, Engineering & Technology 2: 438-446.
- Kira K., and Rendell L.A. 1992. The feature selection problem: Traditional methods and a new algorithm. AAAI-92 Proceedings of the tenth national conference on Artificial intelligence 129-134.
- Liyew , and Melese H. 2021. Machine learning techniques to predict daily rainfall amount. Journal of Big Data 8(153). https://doi.org/10.1186/s40537-021-00545-4.
- Mishra , Soni H., Sharma S., and Upadhyay A. 2017. A Comprehensive survey of data mining techniques on time series data for rainfall prediction. Journal of ICT Research and Applications 11(2): 168-184. https://doi.org/10.5614/ itbj.ict.res.appl.2017.11.2.4.
- Nagahamulla , Ratnayake U., and Ratnaweera A. 2014. Selecting most suitable members for neural network ensemble rainfall forecasting model. Recent Advances on Soft Computing and Data Mining 591–601. https://doi.org/10.1007/978-3-319-07692-8_56.
- Omidvar K., and Azhdarpoor M. 2013. Comparison of artificial neural network and HEC-HMS model in assessment- runoff in Herat Azam catchment river. Geographical Research Quarterly 4: 139-159. (In Persian)
- Omidvar K., Shafie Sh., Taghizade Z., and Alipoor M. 2014. Evaluating the efficiency of the decision tree model in predicting rainfall in Kermanshah synoptic station. Journal of Applied Research in Geographical Sciences 14(34): 89-110. (In Persian)
- Sattari M.T., and Nahrein F. 2014. Monthly rainfall prediction using Artificial Neural Networks and M5 model tree (Case study: Station s of Ahar and Jolfa ). Journal of Irrigation and Water Engineering 4(14): 83-98. (In Persian with English abstract)
- Sattari M. T., Falsafian K., Irvem A., Shahab S., and Qasem S. 2020. Potential of kernel and tree-based machine-learning models for estimating missing data of rainfall. Engineering Applications of Computational Fluid Mechanics 14(1): 1078-1094. https://doi.org/10.1080/19942060.2020.1803971.
- Tahroudi M., Ahmadi F., and Khalili K. 2017. Evaluation the Trend and Trend Chang Point of Urmia Lake Basin Precipitation. Journal of Water and Soil 31: 644-659. https://doi.org/22067/JSW.V31I2.55338. (In Persian with English abstract)
- Wang Y., and Witten I.H. 1997. "Inducing model trees for continuous classes", in Proceedings of the Ninth European Conference on Machine Learning. Prague, Czech Republic: Springer 128-137.
- Yobero C. 2018. Determining Creditworthiness for Loan Applications Using C5.0 Decision Trees. RPubs by RStudio.
- Yu N., and Haskins T. 2021. Bagging Machine Learning Algorithms: A Generic Computing Framework Based on Machine-Learning Methods for Regional Rainfall Forecasting in Upstate New York. Informatics 8 (47). https://doi.org/10.3390/informatics8030047.
- Zhou Z.H. 2012. Ensemble Methods: Foundations and Algorithms (New York (US): Chapman & Hall/CRC Press).
|