Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/306978
Title: | Adaptive Data Mining Approaches for Efficient Predictive Analytics |
Researcher: | Razeef Mohd |
Guide(s): | Butt,Muheet Ahmed |
Keywords: | Computer Science Data Mining Engineering and Technology |
University: | University of Kashmir |
Completed Date: | 2018 |
Abstract: | newline eather prediction plays an important role in the field of agriculture and industries. newlineAccurate rainfall prediction is the only phenomenon for detecting the massive newlinerainfall and to provide the information of warnings regarding the disasters. Modelling newlineweather is considered as an important topic around the world as it can predict the threats newlinebeforehand to prevent the damages caused by extreme rainfall conditions. Prediction of newlineweather plays a vital role as it protects the social infrastructures and is favourable to newlinefarmers for taking precautions before the damage. Various data mining techniques were newlinedeveloped to predict the rainfall data, but effective modeling of rainfall in case of newlinechanging weather conditions are unpredictable. These issues can be handled if occurrence newlineof the weather conditions are predicted in advance and warnings are provided on time. newlineThus, by considering the importance of the rainfall prediction system, this research newlineprovides three contributions for addressing these issues. The first contribution of the newlineresearch is to predict the rainfall in spite of changing weather conditions by adapting newlineNonlinear Autoregressive Network with Exogenous Inputs (NARX), which is trained newlineusing the proposed Self Adaptive Levenberg Marquardt (SALM) algorithm for newlinedetermining the optimal weights. The algorithm is responsible for enhancing the rate of newlinelearning to make it more adaptive for yielding prediction with more accuracy. The second newlinecontribution is to develop a technique; named Grey Wolf- based Linear Regression newline(GWLR), using Grey Wolf Optimizer (GWO) and linear regression, for rainfall prediction. newlineThe linear regression is used for predicting the dependent variable value from an newlineindependent variable using regression coefficient, which is generated using GWLR newlinealgorithm. The third contribution is to design an effective rainfall prediction model by newlineintegrating GWO and Levenberg Marquardt (LM) algorithm, called Grey Wolf newlineLevenberg-Marquardt algorithm (GWLM) algorithm, along with NARX model. GWLM newline...... |
Pagination: | |
URI: | http://hdl.handle.net/10603/306978 |
Appears in Departments: | Department of Computer Science |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 271.01 kB | Adobe PDF | View/Open |
02_certificate.pdf | 249.59 kB | Adobe PDF | View/Open | |
03_declaration.pdf | 264.15 kB | Adobe PDF | View/Open | |
04_acknowledgement.pdf | 121.04 kB | Adobe PDF | View/Open | |
05_dedication.pdf | 349.2 kB | Adobe PDF | View/Open | |
06_contents.pdf | 151.6 kB | Adobe PDF | View/Open | |
07_list_of_tables_figures.pdf | 101.45 kB | Adobe PDF | View/Open | |
08_abbreviations.pdf | 96.17 kB | Adobe PDF | View/Open | |
09_abstract.pdf | 166.4 kB | Adobe PDF | View/Open | |
10_chapter_01.pdf | 450.58 kB | Adobe PDF | View/Open | |
11_chapter_02.pdf | 469.09 kB | Adobe PDF | View/Open | |
12_chapter_03.pdf | 661.94 kB | Adobe PDF | View/Open | |
13_chapter_04.pdf | 605.85 kB | Adobe PDF | View/Open | |
14_chapter_05.pdf | 1.43 MB | Adobe PDF | View/Open | |
15_chapter_06.pdf | 572.19 kB | Adobe PDF | View/Open | |
16_chapter_07.pdf | 317.87 kB | Adobe PDF | View/Open | |
17_publications.pdf | 300.5 kB | Adobe PDF | View/Open | |
18_references.pdf | 381.49 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 317.87 kB | Adobe PDF | View/Open |
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