[01] Kuldeep Pareta, Water Resource Department, DHI (India) Water & Environment Pvt. Ltd., New Delhi, India. [02] Debashish Goswami, Water Resource Department, DHI (India) Water & Environment Pvt. Ltd., New Delhi, India.

In this paper, a new stochastic method has been presented for prediction of short-term morphological change, and bankline system using ARIMA model and multi-temporal Landsat satellite imageries in the meandering river. Multi-temporal satellite remote sensing data i.e. Landsat series imageries from 2006 to 2019 has been used for time-series analysis through ARIMA model. We have identified 105 morphological active vulnerable sites through multi-criteria analysis (MCA), and we have developed the short-term morphological change, and bankline shifting prediction model for these 105 vulnerable sites. We have analysed the erosion / deposition pattern, river migration, sinuosity ratio, soil characterise, soil texture, bank material, and water discharge data for these vulnerable sites. We have also developed an equation for generation of predicted points (x, y) in GIS. Statistical analysis of river bankline shifting rate at each vulnerable site (2006 to 2019) has been carried out and that was compared with river bank soil type, and sinuosity ratio. Sandy soil has the highest river bankline shifting rate and sinuosity ratio and clay and clay loam soil have the lowest river bankline shifting rate and sinuosity ratio. As we have developed that model based on banklines from Landsat imageries. We are recommending that the high-resolution satellite images i.e. QuickBird, GeoEye, WorldView to be used for digitization of banklines, subsequent this model will give more accurate result.

Morphological Change, Prediction Model, ARIMA Model, Landsat Satellite Imagery, Rapti River

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