Collection of Weather Data from Authentic Websites and Secondary Data Sources for Rainfall Prediction
Deepak Sharma1, Priti Sharma2
1Deepak Sharma, Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak (Haryana), India.
2Dr. Priti Sharma, Assistant Professor, Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak (Haryana), India.
Manuscript received on 09 May 2023 | Revised Manuscript received on 19 May 2023 | Manuscript Accepted on 15 November 2023 | Manuscript published on 30 November 2023 | PP: 1-6 | Volume-3 Issue-2 November 2023 | Retrieval Number: 100.1/ijdm.B1626113223 | DOI: 10.54105/ijdm.B1626.11322
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© 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: The field of data mining and machine learning has been grown many folds from the last two decades. Almost every other problem can be solved using data mining and this becomes the most tempting part of it for the scientist and researchers all over the world. Data mining can be viewed as a process of discovering knowledge. This discovery of knowledge starts with the collection of data and ends with the acquired knowledge in the form of patterns. Data collection lays the foundation for the process of knowledge discovery. In this paper, various secondary data sources from where data can be collected for rainfall prediction are deeply studied and analyzed. Some of these authentic websites and secondary data sources are NCDC (National climate data center), Kaggle, Datahub.io, UCI machine learning repository, Earth Data etc. The data collected from these secondary data sources for rainfall prediction have been critically analyzed and compared on the parameters of Accuracy, Completeness, reliability, relevance, and timeliness.
Keywords: Data Mining, Data Collection, Secondary Data Sources, Weather Data, Rainfall Prediction, Machine Learning.
Article of the Scope: Data Mining