Flood Susceptibility Appraisal in Ponnaiyar River Basin, India using Frequency Ratio (FR) and Shannon’s Entropy (SE) Models

Jothibasu A., Anbazhagan S., (doi: 10.23953/cloud.ijarsg.73)

Abstract


In any Watershed management studies, demarcation of flood prone area is one of the key tasks. Flood management is essential to shrink the flood effects on human lives and livelihoods. Main goal of the present research is to investigate the application of the Frequency Ratio (FR) and Shannon’s Entropy (SE) models for flood susceptibility appraisal of Ponnaiyar River basin in Tamil Nadu, India. Initially, the flood inventory map was prepared using overlay analysis (slope, 20 meter contour intervals and drainage patterns) and extensive field surveys. In total, 136 flood locations were noted in the study area. Out of these, 95 (70%) floods were randomly selected as training data and the remaining 41 (30%) floods were used for the validation purposes. Further, flood conditioning factors such as lithology, land-use, distance from rivers, soil depth, rainfall, slope angle, slope aspect, curvature, topographic wetness index (TWI) and altitude were prepared from the spatial database. Then, the receiver operating characteristic (ROC) curves were drawn for produced flood susceptibility maps and the area under the curves (AUCs) was computed. The final results indicated that the FR (AUC = 80.20%) and SE (AUC = 79.30%) models have almost similar and reasonable results. Therefore, these flood susceptibility maps can be useful for researchers and planner in flood mitigation strategies.


Keywords


Geographic Information System; Remote Sensing; Flood Susceptibility; Ponnaiyar River

Full Text: PDF

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

*2016 Journal Impact Factor was established by dividing the number of articles published in 2014 and 2015 with the number of times they are cited in 2016 based on Google Scholar, Google Search and the Microsoft Academic Search. If ‘A’ is the total number of articles published in 2014 and 2015, and ‘B’ is the number of times these articles were cited in indexed publications during 2016 then, journal impact factor = A/B. To know More: (http://en.wikipedia.org/wiki/Impact_factor)