Effects of climate change on soil erosion risk assessed by clustering and artificial neural network

Abstract

The erosivity index, as a combination of the Fournier index (FI) and the Bagnouls-Gaussen aridity index (BGI), has been suggested to assess soil erosion risk. It can be easily calculated from meteorological data, i.e., precipitation and temperature. As an example application, data from 55 meteorological stations in Turkey corresponding to a period of 39years from 1975 to 2013 are considered herein. The stations were classified using cluster analysis to obtain a zonation of Turkey based on EI yearly averages. Clustering techniques were applied to a similarity matrix between stations obtained based on the complement of the probability of the similarity ratio index. Four clusters were defined according to the maximal evenness of the eigenvalues of the within each cluster similarity matrices corresponding to different hierarchical levels of the dendrograms. The probability of similarity was calculated using a permutation technique. Time series of the EI of the clusters were used to predict their annual average values for the years from 2014 to 2040 using a multilayer back-propagation neural network (MLPBP-NN). The results showed that the four clusters represent a gradient of increasing EI. The clusters corresponding to northern and central Turkey have lower EI values and EI variability than those for southern and western Turkey. The results of the MLPBP-NN predict that the erosion risk will increase for all zones, but with high increments in southern and western Turkey. Therefore, the regions corresponding to these clusters should be subjected to detailed soil erosion risk analysis.