Renewable energy (RE) can play an important role in Sustainable Development in India. India is a growing economy, and it would need an assured supply of 3–4 times than the whole energy consumed today. This research paper discussed about RE sources, drivers, challenges and policies. It can be concluded from review of literature and other information provided in paper that RE have a huge potential in India and with latest technologies use it can fulfill India’s energy demand in future. Quality of life can be improved with the help of RE. It is predictable that large number of domestic jobs can be created in future in green energy and RE. The study found that RE can reduce environment pollution issues, carbon emission and scarcity of nonrenewable energy sources .RE has a positive relation with economic growth, job creation and welfare. This paper recommends some suggestions for strategic policy in sustainable energy sources.
Deep learning is widely used for the classification of images that have various attributes. Image data are used to extract colour, texture, form, and local features. These features are combined in feature-level image fusion to create a merged remote sensing image. A trained depth belief network (DBN) processes and divides fusion images, while a Softmax classifier determines the land type. As tested, the proposed approach can categorise all types of land. Traditional methods of detecting distant sensing photographs have limitations that can be overcome by using convolutional neural networks (CNN). Traditional techniques are incapable of combining deep learning elements while doing badly in classification. After PCA decreases data dimensionality, deep learning is applied to generate effective features that employ deep learning after PCA has reduced the dimensionality of the data. Principal component analysis is commonly used because of its effectiveness in attaining linear dimension reduction. It may be used on its own or as a starting point for further study into various different dimensionality reduction approaches. Data can be altered by remapping onto a new set of orthogonal axes using a process known as projection-based principal component analysis. Following remote sensing of land resources, the pictures were classified using a support vector machine. Euroset satellite images are used to assess the suggested approach. Accuracy and kappa have both increased. It was accurate and within 95.83 % of the planned figures. The classification findings’ kappa value and reasoning time were 95.87 % and 128 milliseconds, respectively. Both the model’s performance and the classification effect are excellent.
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