Classifying Images of Drought-Affected Area Using Deep Belief Network, kNN, and Random Forest Learning Techniques

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Author(s): Sanjiban Sekhar Roy (VIT University, India), Pulkit Kulshrestha (VIT University, India)and Pijush Samui (NIT Patna, India)
Copyright: 2018
Pages: 18
Source title: Deep Learning Innovations and Their Convergence With Big Data
Source Author(s)/Editor(s): S. Karthik (SNS College of Technology, Anna University, India), Anand Paul (Kyungpook National University, South Korea)and N. Karthikeyan (Mizan-Tepi University, Ethiopia)
DOI: 10.4018/978-1-5225-3015-2.ch006

Keywords: Artificial Intelligence / Computer Science & IT / Data Mining and Databases / Information Science Reference

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Abstract

Drought is a condition of land in which the ground water faces a severe shortage. This condition affects the survival of plants and animals. Drought can impact ecosystem and agricultural productivity, severely. Hence, the economy also gets affected by this situation. This paper proposes Deep Belief Network (DBN) learning technique, which is one of the state of the art machine learning algorithms. This proposed work uses DBN, for classification of drought and non-drought images. Also, k nearest neighbour (kNN) and random forest learning methods have been proposed for the classification of the same drought images. The performance of the Deep Belief Network(DBN) has been compared with k nearest neighbour (kNN) and random forest. The data set has been split into 80:20, 70:30 and 60:40 as train and test. Finally, the effectiveness of the three proposed models have been measured by various performance metrics.

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Classifying Images of Drought-Affected Area Using Deep Belief Network, kNN, and Random Forest Learning Techniques

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