Abstract. Task allocation under uncertain conditions is a key problem for agents attempting to achieve harmony in disaster environments. This paper presents an agent-based simulation to investigate task allocation considering appropriate spatial strategies to manage uncertainty in urban search and rescue (USAR) operations. The proposed method is based on the contract net protocol (CNP) and implemented over five phases: ordering existing tasks considering intrinsic interval uncertainty, finding a coordinating agent, holding an auction, applying allocation strategies (four strategies), and implementing and observing the real environment. Applying allocation strategies is the main innovation of the method. The methodology was evaluated in Tehran's District 1 for 6.6, 6.9, and 7.2 magnitude earthquakes. The simulation began by calculating the numbers of injured individuals, which were 28 856, 73 195, and 111 463 people for each earthquake, respectively. Simulations were performed for each scenario for a variety of rescuers (1000, 1500, and 2000 rescuers). In comparison with the CNP, the standard duration of rescue operations with the proposed approach exhibited at least 13 % improvement, with a maximal improvement of 21 %. Interval uncertainty analysis and comparison of the proposed strategies showed that increased uncertainty led to increased rescue time for the CNP and strategies 1 to 4. The time increase was less with the uniform distribution strategy (strategy 4) than with the other strategies. The consideration of strategies in the task allocation process, especially spatial strategies, facilitated both optimization and increased flexibility of the allocation. It also improved conditions for fault tolerance and agent-based cooperation stability in the USAR simulation system.

Schools as social bases and children’s centers are among the most vulnerable areas to flooding. Flood risk mapping (FRM) is very important for flood preparedness and adopting preventive plans for reducing the school vulnerability to flooding. To achieve this, there is a need for the models that can be used in vast areas with high predictive accuracy. This study aims to develop the innovative hybrid models by coupling the support vector regression (SVR), statistical approaches, and two meta-heuristic algorithms, whale optimization algorithms (WOA) as well as grey wolf optimizer (GWO). According to the proposed methodology, a hybrid feature of SVR and frequency ratio (FR-SVR) is optimized by applying the GWO and WOA optimization algorithms to generate the maps related to flood susceptibility (FSMs). The method was utilized for the Ardabil Province located in southwestern Caspian Sea precincts of which faced devastating floods. The GIS database including 147 ground control locations of flooded zones and nine factors which influence flood were utilized to learn and ascertain the validity of the models. The statistical measures of RMSE, MAE, AUC, and ROC curve were then applied for the developed models in order to estimate prophetically. The results indicated that the meta-optimized FR-SVR-GWO as well as FR-SVR-WOA models exceeded the FR-SVR and FR models in training (RMSEFR-SVR-WOA = 0.2016, RMSEFR-SVR-GWO = 0.1885, AUCFR-SVR-WOA = 0.87, AUCFR-SVR-GWO = 0.88) and validation (RMSEFR-SVR-WOA = 0.2025, RMSEFR-SVR-GWO = 0.1986, AUCFR-SVR-WOA = 0.87, AUCFR-SVR-GWO = 0.87) phases. The FR-SVR-WOA and FR-SVR-GWO models were very competitive regarding AUC and RMSE values, but the FR-SVR-WOA model reproduced greater flood susceptibility rates and was considered for school flood risk mapping (SFRM).

In this study, the correlation between specific capacity (SPC) and transmissivity (T) values of groundwater and various geological, topographical, soil, clinical, and forest-related factors was calculated using a probability technique-frequency ratio. Then, the groundwater potential maps were created using the frequency ratio model with a resolution of 30 m for the entire South Korea. The maps were validated using the quantitative ROC (Receiver Operating Characteristic)-AUC (Area Under the Curve) method and the results showed the accuracy of 83.52% for specific capacity and 81.92% for Transmissivity. The groundwater potential maps can be used as basic data of groundwater development and downloaded free of charge from the environmental big data platform (www.bigdata-environment.kr).

Abstract. Task allocation in uncertainty conditions is a key problem for agents attempting to achieve harmony in disaster environments. This paper presents an agent- based simulation to investigate tasks allocation through the consideration of appropriate spatial strategies to deal with uncertainty in urban search and rescue (USAR) operation. The proposed method is presented in five phases: ordering existing tasks, finding coordinating agent, holding an auction, applying allocation strategies, and implementation and observation of environmental uncertainties. The methodology was evaluated in Tehran's District 1 for 6.6, 6.9, and 7.2 magnitude earthquakes. The simulation started by calculating the number of injured individuals, which was 28856, 73195 and 111463 people for each earthquake, respectively. The Simulations were performed for each scenario for a variety of rescuers (1000, 1500, 2000 rescuer). In comparison with contract net protocol (CNP), the standard time of rescue operations in the proposed approach includes at least 13% of improvement and the best percentage of recovery was 21 %. Interval uncertainty analysis and the comparison of the proposed strategies showed that an increase in uncertainty leads to an increased rescue time for CNP of 67.7 hours, and for strategies one to four an increased rescue time of 63.4, 63.2, 63.7, and 56.5 hours, respectively. Considering strategies in the task allocation process, especially spatial strategies, resulted in the optimization and increased flexibility of the allocation as well as conditions for fault tolerance and agent-based cooperation stability in USAR simulation system.