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Algorithm Design for GIS using BFO and Neural Network Approach for Identification of Groundwater
Objectives: Groundwater is an important resource contributing significantly in development of natural life. However, over exploitation has depleted groundwater ratio deliberately and also led to land subsidence at some places. Methods/Statistical Analysis: Groundwater zones are demarcated using remote sensing and Geographic Information System (GIS) techniques. Findings: In this research a definitive methodology is proposed to determine groundwater using integration of BFO and neural network technique. For the training purpose we use fuzzy logic and after that we use optimization techniques to find suitable feature set which can classify more accurate. Applications/Improvements: Finally, it is concluded that the Geoinformatics technology are very efficient and useful for the identification of groundwater detection. We evaluate parameters like kappa coefficient, water level, accuracy of algorithm to detect the water percentage in the region from where we conduct the satellite image through remote sensing. Therefore, this research will be useful for effectual identification of suitable locations for extraction of water.
BFO, Fuzzy Logic, Ground Water Detection, Kappa Coefficient. Neural Network.
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