Urban flood risk mapping using the GARP and QUEST models:a comparative study of machine learning techniques

Abstract Flood risk mapping and modeling is important to prevent urban flood damage. In this study, a flood risk map was produced with limited hydrological and hydraulic data using two state-of-the-art machine learning models: Genetic Algorithm Rule-Set Production (GARP) and Quick Unbiased Efficient Statistical Tree (QUEST). The flood conditioning factors used in modeling were: precipitation, slope, curve number, distance to river, distance to channel, depth to groundwater, land use, and elevation. Based on available reports and field surveys for Sari city (Iran), 113 points were identified as flooded areas (with each flooded zone assigned a value of 1). Different conditioning factors, including urban density, quality of buildings, age of buildings, population density, and socio-economic conditions, were taken into account to analyze flood vulnerability. In addition, the weight of these conditioning factors was determined based on expert knowledge and Fuzzy Analytical Network Process (FANP). An urban flood risk map was then produced using flood hazard and flood vulnerability maps. The area under the receiver-operator characteristic curve (AUC-ROC) and Kappa statistic were applied to evaluate model performance. The results demonstrated that the GARP model (AUC-ROC = 93.5%, Kappa = 0.86) had higher performance accuracy than the QUEST model (AUC-ROC = 89.2%, Kappa = 0.79). The results also indicated that distance to channel, land use, and elevation played major roles in flood hazard determination, whereas population density, quality of buildings, and urban density were the most important factors in terms of vulnerability. These findings demonstrate that machine learning models can help in flood risk mapping, especially in areas where detailed hydraulic and hydrological data are not available

The versatility and adaptation of bacteria from the genus <em>Stenotrophomonas</em>

The genus Stenotrophomonas comprises at least eight species. These bacteria are found throughout the environment, particularly in close association with plants. Strains of the most predominant species, Stenotrophomonas maltophilia, have an extraordinary range of activities that include beneficial effects for plant growth and health, the breakdown of natural and man-made pollutants that are central to bioremediation and phytoremediation strategies and the production of biomolecules of economic value, as well as detrimental effects, such as multidrug resistance, in human pathogenic strains. Here, we discuss the versatility of the bacteria in the genus Stenotrophomonas and the insight that comparative genomic analysis of clinical and endophytic isolates of S. maltophilia has brought to our understanding of the adaptation of this genus to various niches