Revegetation following human-induced damage to vegetation is now a common phenomenon in many ecologically fragile areas around the globe. However, more attention has been paid to climate and ecological engineering factors as influences on the effectiveness of vegetation restoration, while the extent to which socioeconomic factors influence vegetation restoration remains a question that has not been clearly answered. In this study, socio-economic data were obtained through field and household surveys, and then the extent to which socio-economic factors influence the effects of vegetation restoration and their mechanisms of action were assessed using a generalized linear mixed effects model, a partial least squares variable projection significant indicator approach, and a partial least squares path modeling approach. It was found that among the socioeconomic factors, variables such as percentage of cars, conservation awareness, and agricultural practices significantly influenced vegetation restoration (the R2 values are 0.21, 0.15 and 0.15). In terms of importance analysis, economic factors ranked first in terms of importance, followed by psychological factors, agricultural system factors, cultural factors, and natural factors in that order. From the comprehensive impact analysis, economic factors, cultural factors, and agricultural system factors positively affect vegetation recovery (the path coefficients are 0.26, 0.06 and 0.08), and psychological factors negatively affect vegetation recovery (the path coefficient is -0.31). To summarize, in addition to ecological engineering, the remaining socio-economic factors are also important and cannot be ignored for their influence on the effectiveness of vegetation restoration.

Ouagadougou, the capital city of Burkina Faso, is facing significant economic and social damages due to recurring floods. This study aimed to develop a flood susceptibility map for Ouagadougou using a logistic regression (LR) model and 14 flood conditioning factors, including elevation, slope, aspect, profile curvature, plan curvature, topographic position index (TPI), topographic roughness index (TRI), flow direction, topographic wetness index (TWI), distance to river, rainfall, land use/land cover (LULC), normalized difference vegetation index (NDVI) and soil type. A historical flood inventory map was created from household survey data, identifying 1026 flooded sites which were divided into a training dataset (70%) and a validation dataset (30%). The factors that had a statistically significant influence (p-value 1.96) at the 95% confidence level were, in order of importance, elevation, distance to river, rainfall, plan curvature and NDVI. The receiver operating characteristic (ROC) curve method was used to validate the model. The area under the curve (AUC) values of the model were 81% for the prediction rate and 82% for the success rate indicating its effectiveness in identifying areas susceptible to flooding. The results showed that 18.48% of the city is very high susceptible to flooding, 18.99% has high susceptibility, 18.43% has moderate susceptibility, and 19.98% and 24.18% have low and very low susceptibility, respectively. This research provides valuable information for policy makers to develop effective flood prevention and urban development strategies.

The Earth is currently experiencing severe economic and social consequences as a result of frequent floods. This study is crucial for effective risk management and mitigation, protecting lives and property from potential flood damage in the Deme watershed. This study endeavors to assess the efficacy of a logistic regression model in generating a flood susceptibility map for the Deme watershed in Ethiopia. Fourteen factors contributing to flooding were considered, including digital elevation model, slope, aspect, profile curvature, plane curvature, Topographic Position Index (TPI), Topographic Roughness Index (TRI), flow direction, Topographic WetnessIindex (TWI), distance to the river, rainfall, land use/land cover (LULC), Normalized Difference Vegetation Index (NDVI), and soil type. The receiver operating characteristic (ROC) curve method was employed to validate the model. The area under the curve (AUC) values for the model were determined to be 81% for the training dataset and 82% for the validation dataset, indicating its effectiveness in delineating flood-prone areas. The findings revealed that 18% of the watershed is very highly susceptible to flooding, 19% exhibits high susceptibility, 18% shows moderate susceptibility, while 20 and 24% have low and very low susceptibility, respectively. This research provides insights into comprehensive flood prevention and urban development strategies. HIGHLIGHTS center dot Flood susceptibility is determined by historical flood patterns and their influencing factors. center dot Logistic regression can be used to map flood-susceptible areas in a small watershed. center dot A multicollinearity test is necessary to ensure a linear relationship in flood conditioning factors. center dot Factors with high multicollinearity should be removed from models to improve prediction accuracy.