Using multinomial logistic regression for prediction of soil depth in an area of complex topography in Taiwan
2019
Chan, H.C. | Chang, C.H. | Chen, P.A. | Lee, J.T.
Increasing land developments, unreasonable utilization of land, earthquakes, typhoons, and torrential rain often cause hillside disasters. Soil depth is a crucial parameter influencing the scale of shallow landslides and sustainable land use in hillside areas. This paper presents a statistical model, integrated with environmental factors, for estimating soil depth in a catchment area with complex topography. The study area selected was upstream of the Houlong River in Taiwan. The model was developed using multinomial logistic regression and environmental factors such as hill slope, hill aspect, elevation, topographical curvature, and the normalized difference vegetation index. The results were then compared with those obtained from previous models applying Ordinary Kriging, Regression Kriging, and topographical wetness index methods. A classification error matrix and the Kappa index were then employed to assess the various models. The results showed that, for the data set used for model establishment, the overall accuracy and Kappa index were 76.6% and 0.65, respectively, whereas for the data set used for verification, they were 70.5% and 0.57, respectively. By contrast, the Ordinary Kriging method yielded an overall accuracy and a Kappa index of 45.7% and 0.15, respectively; the Regression Kriging method yielded results of 46.7% and 0.16, respectively; and the topographical wetness index method yielded results of 30.5% and −0.05, respectively. The proposed model was therefore determined to be superior to the others in terms of soil depth estimation accuracy. The proposed model can predict soil depth on a regional scale and serve as a reliable tool that provides reference data for future research on land use and shallow landslides.
اظهر المزيد [+] اقل [-]الكلمات المفتاحية الخاصة بالمكنز الزراعي (أجروفوك)
المعلومات البيبليوغرافية
تم تزويد هذا السجل من قبل National Agricultural Library