Due to the physical processes of floods, the use of data-driven machine learning (ML) models is a cost-efficient approach to flood modeling. The innovation of the current study revolves around the development of tree-based ML models, including Rotation Forest (ROF), Alternating Decision Tree (ADTree), and Random Forest (RF) via binary particle swarm optimization (BPSO), to estimate flood susceptibility in the Maneh and Samalqan watershed, Iran. Therefore, to implement the models, 370 flood-prone locations in the case study were identified (2016–2019). In addition, 20 hydrogeological, topographical, geological, and environmental criteria affecting flood occurrence in the study area were extracted to predict flood susceptibility. The area under the curve (AUC) and a variety of other statistical indicators were used to evaluate the performances of the models. The results showed that the RF-BPSO (AUC=0.935) has the highest accuracy compared to ROF-BPSO (AUC=0.904), and ADTree-BPSO (AUC=0.923). In addition, the findings illustrated that the chance of flooding in the center of the area in question is greater than in other points due to lower elevation, lower slope, and proximity to rivers. Therefore, the ensemble framework proposed here can also be used to predict flood susceptibility maps in other regions with similar geo-environmental characteristics for flood management and prevention.

Gray water is a great resource for replacing fresh water to be used in standardized usages. The use of treated gray water reduces water consumption and the entry of pollutants into the environment. However, if left untreated, it can be dangerous. The present study examines the efficiency of a gray water treatment system consisting of primary filter, aeration, secondary filter and ultraviolet disinfection unit. After examining the characteristics of gray water, the efficiency of this system was analyzed to remove the pH, TSS, BOD, COD, ABS and total coliform parameters. Then, the gray water treated through this system was compared with the environmental standard of Iran. The pH of the treated gray water was 7.5–7.6. The efficiency of this system for removing the BOD and COD parameters was 98–100 and 76–100%, respectively. This system had an efficiency of 96–97% to eliminate the ABS parameter. Also, this system was able to eliminate total coliform with 100% efficiency. Results showed that in the three series of experiments performed on this system, according to the Iranian standard, the treated wastewater is suitable for irrigation and agricultural uses. However, this system could not be licensed for the COD parameter regarding the discharge to surface water and absorbent wells.