Drought PredictionA Comparative Analysis of Supervised Machine Learning Techniques
- Title
- Drought PredictionA Comparative Analysis of Supervised Machine Learning Techniques
- Creator
- Choesang T.; Ryntathiang S.; Jacob B.A.; Krishnan B.; Kokatnoor S.A.
- Description
- Drought is a natural phenomenon that puts many lives at risk. Over the last decades, the suicide rate of farmers in the agriculture sector has increased due to drought. Water shortage affects 40% of the world's population and is not to be taken lightly. Therefore, prediction of drought places a significant role in saving millions of lives on this planet. In this research work, six different supervised machine learning (SML) models namely support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), convolutional neural networks (CNNs), long short-term memory (LSTM), and recurrent neural networks (RNNs) are compared and analyzed. Three dimensionality reduction techniques principal component analysis (PCA), linear discriminant analysis (LDA), and random forest (RF) are applied to enhance the performance of the SML models. During the experimental process, it is observed that RNN model yielded better accuracy of 88.97% with 11.26% performance enhancement using RF dimensionality reduction technique. The dataset has been modeled using RNN in such a way that each pattern is reliant on the preceding ones. Despite the greater dataset, the RNN model size did not expand, and the weights are observed to be shared between time steps. RNN also employed its internal memory to process the arbitrary series of inputs, which helped it outperform other SML models. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Source
- Smart Innovation, Systems and Technologies, Vol-351, pp. 295-307.
- Date
- 2023-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Artificial neural network; Decision tree; K-nearest neighbors; Machine learning; Random forest; Supervised; Support vector machine
- Coverage
- Choesang T., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Ryntathiang S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Jacob B.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Krishnan B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India
- Rights
- Restricted Access
- Relation
- ISSN: 21903018; ISBN: 978-981992467-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Choesang T.; Ryntathiang S.; Jacob B.A.; Krishnan B.; Kokatnoor S.A., “Drought PredictionA Comparative Analysis of Supervised Machine Learning Techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/19898.