Spatial mapping of gully erosion susceptibility using an efficient metaheuristic neural network

Developing a susceptibility map is a crucial primary step for dealing with undesirable natural phenomena, gully erosion included. On the other hand, recent computational progress call for employing new methodologies to keep the solutions updated. In this work, the performance of a conventional artificial neural network (ANN) is improved by applying a metaheuristic algorithm (symbiotic organisms search—SOS) for generating the gully erosion susceptibility map of an area in Golestan Province, Northern Iran. A geo-database is created from the gully erosion inventory and twenty conditioning factors. After analyzing the interrelated relationships between the geo-database components, training and testing data sets are formed. The models are executed with proper configurations and according to the results, the SOS algorithm could enhance the training accuracy of the ANN from 92.8% to 98.4%, and testing accuracy from 89.8% to 91.4%. In addition, comparing the performance of the SOS with shuffled complex evolution (SCE-NN) and electromagnetic field optimization (EFO-NN) algorithms revealed the greater accuracy of the SOS. However, the SCE-NN and EFO-NN performed more accurately than conventional ANN. Therefore, it can be concluded that the use of metaheuristic techniques may improve the prediction ability of the ANN in gully erosion susceptibility mapping. Finally, a monolithic equation is extracted from the SOS–ANN model to be used as a predictive formula for similar purposes.