Development of Ensembled Probabilistic Machine Learning models on Agri-Climatic data
| dc.contributor.advisor | Rajput, Ravinder Singh | |
| dc.contributor.author | Mathpal, Tanuja | |
| dc.date.accessioned | 2024-08-27T10:42:22Z | |
| dc.date.available | 2024-08-27T10:42:22Z | |
| dc.date.issued | 2023-07 | |
| dc.description.abstract | Mathematical models serve as formal representations of real-world phenomena, employing mathematical equations, symbols, and logical relationships. They are indispensable tools for describing, analyzing, and predicting the behaviour of complex systems using mathematical principles and techniques. This study investigates deterministic and probabilistic mathematical models, including Logistic Regression, Naïve Bayes Classifier Models, Hidden Markov Models, Artificial Neural Network Models with a SoftMax output layer, Decision Tree Models, and K Neighbors Models, for rainfall prediction based on agri-climatic data. The models are trained using secondary data obtained from the NASA POWER database, specifically the Agriclimatology database related to Pantnagar, Uttarakhand. The dataset consists of 4,383 rows and 14 columns, representing daily data from 2010 to 2021. Model validation is conducted using data from the year 2022, and performance evaluation utilizes standard measures commonly employed in machine learning studies. The results indicate that the probabilistic models, particularly Logistic Regression, Hidden Markov Models, Naïve Bayes Classifier Models, and Artificial Neural Network Models with a SoftMax output layer, exhibit promising performance for rainfall prediction. Ensemble models are developed using averaging, max voting, and stacking techniques, effectively combining multiple models to enhance prediction accuracy. Among the ensemble models, the stacking model demonstrates the highest accuracy, followed by the Max Voting model and the averaging model. | |
| dc.identifier.citation | Theses of M.Sc. | |
| dc.identifier.uri | https://krishikosh.egranth.ac.in/handle/1/5810213791 | |
| dc.keywords | Probabilistic Machine Learning models | |
| dc.language.iso | English | |
| dc.pages | 72 p.p. | |
| dc.publisher | G. B. Pant University of Agriculture & Technology, Pantnagar-263145 | |
| dc.relation.ispartofseries | 10889; 58432 | |
| dc.sub | Statistics | |
| dc.theme | Academic Research | |
| dc.these.type | M.Sc | |
| dc.title | Development of Ensembled Probabilistic Machine Learning models on Agri-Climatic data | |
| dc.type | Thesis |