Abstract

The present study aims to analyze the shift in shoreline due to coastal processes and formulate available for best estimate of future shoreline positions based on precedent shorelines. Information on rates and trends of shoreline change can be used to improve the understanding of the underlying causes and potential effects of coastal erosion which can support informed coastal management decisions. In this paper, researchers go over the changes in the recent positions of the shoreline of the Balasore coast for the 38 years from 1975 through 2013. The study area includes the Balasore coastal region from Rasalpur to Udaypur together with Chandipur, Choumukh, Chandrabali as well as Bichitrapur. Transects wise shoreline data base were developed for approximately 67 kilometers of shoreline and erosional/accretional scenario has also been analysed by delineating the shoreline from Landsat imageries of 1975, 1980, 1990, 1995, 2000, 2005, 2010 and 2013. A simple Linear Regression Model and End Point Rate (EPR) have been adopted to take out the rate of change of shoreline and its future positions, based on empirical observations at 67 transects along the Balasore coast. It is found that the north eastern part of Balasore coast in the vicinity of Subarnarekha estuary and Chandrabali beach undergo high rates of shore line shift. The shoreline data were integrated for long- (about 17 years) and short-term (about 7 years) shift rates analysis to comprehend the shoreline change and prediction. For the prediction of future shoreline, the model has been validated with the present shoreline position (2013). The rate of shoreline movement calculated from the fixed base line to shoreline position of 1975, 1980, 1990, 1995, 2000, 2005 and 2010 and based on this, the estimated shoreline of 2013 was calculated. The estimated shoreline was compared with the actual shoreline delineated from satellite imagery of 2013. The model error or positional shift at each sample point is observed. The positional error varies from ?4.82 m to 212.41 m. It has been found that model prediction error is higher in the left hand side of river Subarnarekha. The overall error for the entire predicted shoreline was found to be 41.88 m by Root Mean Square Error (RMSE). In addition, it was tested by means difference between actual and predicted shoreline positions using “t” test and it has been found that predicted shore line is not significantly different from actual shoreline position at (t132 = 0.278) p < 0.01.

Highlights

  • Shoreline shifting is the uncontrollable result of coastal erosion/accretion, the consequence of near shore currents

  • Several studies have already been done on shoreline change and prediction, such as empirical analysis relating to hard stabilization structures to beach dynamics [4] [5] analysis of natural beach loss and gain [6] identification of relative changes among coastal units [7] and process response of a shoreline [8]

  • The obtained results of the present study suggest that the utilization of remote sensing data in addition with the Geographic Information System (GIS) technology and statistical technique are very appropriate for extraction of shoreline and its shifting calculation

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Summary

Introduction

Shoreline shifting is the uncontrollable result of coastal erosion/accretion, the consequence of near shore currents. The EPR model is based on the supposition that observed past rate of change is the best approximation available for predicting future while LR model is based on robust linear prediction method which detects short-range changes in the long-standing trend. This process recognizes the linear and high-order polynomial model which best fits the data according to that Minimum Description Length (MDL) condition that determines the nature and regularities in observed data. The present study involves an endeavor to appraise an investigative model for predicting the future shoreline position in order to monitor the shoreline shift along the coast in Balasore district of Orissa, India

Study Area
Position of Predicted Shoreline
Validation of EPR Model
Future Shoreline Prediction Using EPR Model and Error Adjustment
Findings
Conclusion
Full Text
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