GIS-based crash hotspot identification: a comparison among mapping clusters and spatial analysis techniques.

Knowing the locations of traffic crash hotspots can provide us with valuable insights into the root causes of crash occurrence over the area under study. This knowledge helps decision-makers to better assess the risk associated with road crashes and, as a result, help them to propose more effective countermeasures in order to reduce the annual crash rate. Nonetheless, identifying the areas with the highest potential of crash occurrence is a complicated task. In this regard and within this study, five various types of hotspot identification techniques, consisting of Average Nearest Neighbor, Getis-Ord Gi*, Global Moran's I, kernel density estimation (KDE) and mean centre, were compared to each other, using three different performance measures, including Predictive Accuracy Index (PAI), Recapture Rate Index (RRI) and hit rate. According to the results, the most accurate model with the highest PAI values (PAI = 1.61 and 1.76), Moran's I, had the third-highest reliability value (RRI = 1.003). On the other hand, while the Gi* method was the most precise and reliable technique with the highest RRI value (RRI = 1.121), it showed the second-lowest accuracy (PAI= 0.83 and 0.74). Overall, it seems that Moran's I method is superior to other methods in locating hotspots, which is not only the most accurate technique but also precise enough to rely on.

Estimation of nonlinear parameters of the type 5 Muskingum model using SOS algorithm.

The Symbiotic Organisms Search Algorithm (SOS) is used as an algorithm based on the social behavior of Symbiotic Organisms in optimization of Non-linear 5 model parameters for flood routing. The data used in this article is 4 day observations from 30 November 2008 to 3 December 2008, which is located between the Molasani, and Ahwaz station on the Karun River. The time series data used included river inflow, storage volume, and river outflow. The results of the developed model with the Symbiotic Organisms Search Algorithm (SOS) were compared with the other Evolutionary algorithms including Genetic Algorithm (GA, and Harmony Search Algorithm (HS). The analysis showed that the best solutions achieved from the objective function by the SOS, GA, and HS algorithms were 143052.02, 143252.35, and 142952.45, respectively. The processes of these datasets determined that the SOS algorithm was premiere to GA, and HS algorithms on the optimal flood routing river problem.•In this article applied the Symbiotic Organisms Search Algorithm for Estimation of nonlinear parameters of the Muskingum hydrologic model of the Karun River located in Iran.•This method can be useful for managers of water, and wastewater companies, water resource facilities for predicting the flood process downstream of the rivers.•The present algorithm performs better than the other algorithms in the discussion of the optimization of Nonlinear 5 parameters of Muskingum model in flood routing.

Optimization of water distribution of network systems using the Harris Hawks optimization algorithm (Case study: Homashahr city).

In this article applies the Harris Hawks Optimization Algorithm for optimization of the water distribution network of the Homashahr located in Iran for a period of one month (from 30 September 2018 to 30 October 2019). The utilized time-series data included water demand, reservoir storage. In this article, a model based on the Harris Hawks Optimization Algorithm (HHO) was developed for the optimization of the water distribution network. The analysis showed that the best solutions achieved by the Harris Hawks Optimization Algorithm (HHO) were 35,508 $. The results revealed that the HHO algorithm was well in the optimal design of water supply networks problem. At the end, about 12% of the optimization was done by this algorithm.•In this article applied the Harris Hawks Optimization Algorithm for optimization of the water distribution network of the Homashahr located in Iran.•The method presented in this article can be useful for managers of water and wastewater companies, water resource facilities and water distribution system managing director for optimal network design to reduce costs.•The present algorithm performs better than the other algorithms in the discussion of the optimization of water distribution networks.