Application of soft computing techniques in coastal study – A review

Abstract

Coastal zone is the triple interface of air, water and land and it is so dynamic in nature which requires expeditious management for its protection. Impulsive change in shoreline and submergence of low lying areas due to sea level rise are the solemn issues that need to be addressed. Indian coastline of about 7516km is under threat due to global warming and related human interventions. Remote sensing data products provide synoptic and repetitive view of the earth in various spatial, spectral, temporal and radiometric resolutions. Hence, it can be used in monitoring coastal areas on a temporal scale. Critical Erosion hotspots have to be given proper protection measures to avoid further damages. Satellite images serve in delineating shoreline and extracting the hotspots to plan the mitigation works. Coastal inundation maps can be created using remote sensing and geospatial technologies by assuming different sea level rises. Those maps can serve as a base for planning management activities. Soft computing techniques like Fuzzy Logic, Artificial Neural Network, Genetic Algorithm and Support Vector Machine are upcoming soft computing algorithms that find its application in classification, regression, pattern recognition, etc., across multi-disciplinary sciences. They can be used in classifying remote sensing images which in turn can be used for studying the coastal vulnerability. The present paper reviews the works carried out for coastal study using conventional remote sensing techniques and the pertinency of soft computing techniques for the same.

Keywords

• Artificial neural network
• Coastal inundation
• Remote sensing
• Shoreline
• Soft computing
• Support vector machine