## 5 Discussion  
The image classification procedure using the remote sensing techniques had a crucial role in the present paper because it allowed us to assess the changes that occurred in land use/land cover during the last 30 years, and further, to analyze the correlation between these changes and those  
Figure 11: Spatial distribution of the r correlation coefficient for GWR and Moran’s spatial autocorrelation report on GWR residuals. that occurred in flash-flood potential. The importance of this remote sensing technique it is even greater as the results obtained had a very high accuracy. According to the results achieved by [PERSON] and [PERSON] [110], the accuracy obtained by the maximum likelihood method (82.9%) was close to the accuracy of the artificial neural network (85.1%) and higher than the accuracy of the support vector machine classification (79.73%). The fact that the maximum likelihood achieved accuracies almost equal to or higher than more advanced techniques in some situations is a solid argument that this method does not have relevant limitations against the quality of the outputs. The reliability of this method was also highlighted by the accuracy of the classification undertaken in the present study. Thus, according to the results presented above, the maximum likelihood supervised classification method performed very well on the Zabala river catchment and provided very precise results with an overall accuracy higher than 93% and the Kappa index higher than 0.92. The accuracy of these results was higher than the accuracy of 81.4% achieved by [PERSON] et al. [111], who used the maximum likelihood algorithm to classify a Landsat scene in a study carried out on a region of Ghana. An accuracy of 91.34%, closer to that obtained in the present study, resulted from a study carried out by [PERSON] et al. [112], who classified a Landsat scene from Egypt through the maximum likelihood method. [PERSON] et al. [113] managed to extract the land use/land cover from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) imagery with an overall accuracy of 94.25% and a value of Kappa index of 0.921. In the present research, the results achieved through remote sensing techniques were analyzed with the help of the Markov matrix, which revealed that the forest surfaces decreased by more than 5500 hectares. Due to the fact that this area represented approximately 13.1% from the total surface covered by forest in 1989, we consider that deforestation was an important factor that contributed to the increase of flash-flood potential across the Zabala catchment. The situation of these areas is all the more worrying as the deforestation occurred in areas with high slopes and a high degree of hydrographic convergence that favours, due the absence of the forest, a very high potential for flash-flood genesis. In this regard, there is no doubt of the fact that the assessment of flash-flood potential, especially within the mountain and hilly catchments, is of a crucial importance for future measurements that the authorities should take in order to mitigate the flash-flood damages. This is also the case for the present study, in which the FFPI was estimated through the multilayer perceptron method. It should be remarked that the use of the MLP model provided accurate results with AUC values higher than 0.8. The MLP model was also involved in the determination of the FFPI values across the Prahova river catchment from Romania [87], where this machine learning algorithm also provided very accurate results (AUC \(>\) 0.8). An optimization of multilayer neural networks was tried by [PERSON] et al. [49], who attempted to estimate the flash-flood susceptibility across a mountain catchment from Vietnam. In this study, the AUC values of the optimized neural networks were also situated above 0.8, which indicates the high accuracy of machine learning model [114; 115; 116; 117; 118; 119; 120; 121].