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http://hdl.handle.net/10603/468735
Title: | An optimization framework for Weather forecast prediction and Analysis |
Researcher: | Krishnaveni, N |
Guide(s): | Padma, A |
Keywords: | Engineering and Technology Computer Science Computer Science Information Systems prediction and Analysis Weather forecast optimization framework |
University: | Anna University |
Completed Date: | 2021 |
Abstract: | Weather forecasting is an emerging domain that predicts the weather condition at a particular time and location. Weather forecasting is considered one of the most sensitive research fields which facing a lot of real-time issues such as inaccurate prediction, lack of handling huge data volume and inadequate technology advancement. newlineEven after the technological and scientific development, the accuracy in forecasting weather has never been satisfactory. Even at present, this domain remains as an area of research in which experts and statisticians are working to produce a model or an algorithm that predicts weather accurately. There have been huge enhancements in the sensors which are responsible for recording the data from the atmosphere and removes the noise present in them. This innovative models which contain different attributes related to weather have been recommended to make a precise prediction. newlineData mining is one of the most extensively used techniques for forecasting weather at present. Data mining can be used for predictions by analyzing data and extracting rules statistically. Currently, it is being used in many fields such as disease prediction, stock market, banking sector etc. Researchers have now understood that data mining can be used as a tool for forecasting weather as well. A decision tree is one of the most influential and extensively used techniques for classification and prediction. newlineDecision Tree Mining is a category of data mining technique that is used to construct classification models. A decision tree is a classification algorithm which is used regularly and has an easy structure to clarify. Decision Tree converts a huge fact into a decision tree based on rules.SPRINT(Scalable Parallelizable Introduction of Classification Tree) is a usual decision-tree-based classification algorithm in which decision trees are built in a top-down recursive divide-and-conquer manner by adapting a greedy approach. newline |
Pagination: | xiii,112p. |
URI: | http://hdl.handle.net/10603/468735 |
Appears in Departments: | Faculty of Information and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 25.94 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 2.17 MB | Adobe PDF | View/Open | |
03_content.pdf | 13.86 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 10.16 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 844.94 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 965.37 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 696.58 kB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 776.61 kB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 1.06 MB | Adobe PDF | View/Open | |
10_annexures.pdf | 149.37 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 79.89 kB | Adobe PDF | View/Open |
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