Chen, Y. and Chen, W. and Janizadeh, S. and Bhunia, G.S. and Bera, A. and Pham, Q.B. and Linh, N.T.T. and Balogun, A.-L. and Wang, X. (2022) Deep learning and boosting framework for piping erosion susceptibility modeling: spatial evaluation of agricultural areas in the semi-arid region. Geocarto International, 37 (16). pp. 4628-4654. ISSN 10106049
Full text not available from this repository.Abstract
Piping erosion is one of the water erosions that cause significant changes in the landscape, leading to environmental degradation. To prevent losses resulting from tube growth and enable sustainable development, developing high-precision predictive algorithms for piping erosion is essential. Boosting is a classic algorithm that has been successfully applied to diverse computer vision tasks. Therefore, this work investigated the predictive performance of the Boosted Linear Model (BLM), Boosted Regression Tree (BRT), Boosted Generalized Linear Model (Boost GLM), and Deep Boosting models for piping erosion susceptibility mapping in Zarandieh Watershed located in the Markazi province of Iran. A piping inventory map including 152 piping erosion locations was prepared for algorithm training and testing. 18 initial predisposing factors (altitude, slope, plan curvature, profile curvature, distance from river, drainage density, distance from road, rainfall, land use, soil type, bulk density, CEC, pH, clay, silt, sand, topographical position index (TPI), topographic wetness index (TWI)) was derived from multiple remote sensing (RS) sources to determine the piping erosion prone areas. The most significant predisposing factors were selected using multi-collinearity analysis which indicates linear correlations between predisposing factors. Finally, the results were evaluated for Sensitivity, Specificity, Positive predictive values (PPV) and Negative predictive value (NPV), and Receiver Operation characteristic (ROC) curve. The best Sensitivity (0.80), Specificity (0.84), PPV (0.85), NPV (0.79), and ROC (0.93), were obtained by Deep Boosting model. The results of the piping erosion susceptibility study in agricultural land use showed that 41 of agricultural lands are very sensitive to piping erosion. This outcome will enable natural resource managers and local planners to assess and take effective decisions to minimize damages to agricultural land use by accurately identifying the most vulnerable areas. Hence, this research proved Deep Boosting model�s ability for piping erosion susceptibility mapping in comparison to other popular methods such as BLM, BRT, and Boost GLM. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
Item Type: | Article |
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Additional Information: | cited By 25 |
Uncontrolled Keywords: | agricultural land; land use; machine learning; modeling; piping; remote sensing; semiarid region; water erosion, Iran; Markazi |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 19 Dec 2023 03:24 |
Last Modified: | 19 Dec 2023 03:24 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/17914 |