The Development of Geo-Information System for Finding Potential Zones of Water using Environmental Parameters and Engineering
- 1 , India
Copyright: © 2020 N. Sundara Rajulu and M. Rajani. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Problem statement: Water is a major natural resource for all the living beings in the world. People’s lives and livelihood depends on water. Most of the human beings in this world use ground water for drinking purpose. The reason behind that is the ground water is pollution less or less polluted when compared with the surface water. The need for clean water increases continually with the world population growth. People in different areas in this world are lack of fresh drinkable water which is important for their survival. Maintaining secure water supplies for drinking, industry and agriculture is not possible without ground water. So it is necessary to explore the potential ground water area to dig a well for the utilization of ground water. Approach: In this study, we have used some parameters to identify the level of water in a particular area. Then, the ground water identification system is designed with the help of Histogram Equalization, Neural Network and PCA. The collected data is given as input to data normalization and the feature is computed for every data using PCA. The presence of ground water in particular location is identified using the trained neural network. Results: Finally, the experimentation is carried out using the synthetic data to show the performance of the ground water identification system. Conclusion: The potential zones of ground water can be identified if the specified parameters of the particular location can be given as input to the neural network.
- 1,202 Views
- 1,529 Downloads
- 0 Citations
- Geographic information system
- leaf area index
- support vector machine
- neural network
- Principal Component Analysis (PCA)