Comparison of the Proposed Rainfall Prediction Model Designed using Data Mining Techniques with the Existing Rainfall Prediction Methods
Deepak Sharma1, Priti Sharma2
1Deepak Sharma, DCSA, Maharshi Dayanand University, Rohtak (Haryana), India.
2Dr. Priti Sharma, DCSA, Maharshi Dayanand University, Rohtak (Haryana), India.
Manuscript received on 15 May 2023 | Revised Manuscript received on 22 May 2023 | Manuscript Accepted on 15 November 2023 | Manuscript published on 30 November 2023 | PP: 7-10 | Volume-3 Issue-2 November 2023 | Retrieval Number: 100.1/ijdm.B1627113223 | DOI: 10.54105/ijdm.B1627.113223
Open Access | Editorial and Publishing Policies | Cite | Zenodo | Indexing and Abstracting
© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Weather prediction is a very old practice and people are doing predictions about weather much before the discovery of the weather measuring instrument. In ancient times, people give weather predictions by observing the sky for a long time and patterns of the stars at night. Things are a bit different now. People more relay on the past trends and patterns followed by the weather parameters. Data mining and machine leaning is used to analysis the historical weather trends by analyzing weather data using various Data mining techniques. In this paper three rainfall prediction model based on data mining techniques are proposed and compared with the other rainfall prediction model. The comparison has been done on the basis of accuracy, precision, Recall and RMSE. The proposed models are based on ensemble methods such as bagging, boosting, and stacking. Ensemble methods are used to enhance the overall performance and accuracy of the prediction. In both bagging and boosting based proposed rainfall prediction models, artificial neural network is used as a base leaner and daily weather data from the year 1988 to 2022 is used. In stacking based proposed rainfall prediction model, random forest, Logistic regression, and K-Nearest neighbor are used as base leaners or level -0 learners and Artificial neural network is used as Meta model.
Keywords: Data Mining, Data Collection, Secondary Data Sources, Weather Data, Rainfall Prediction, Machine Learning.
Article of the Scope: Data Mining