Junaidah Ariffin

Optimization approaches for macroscopic emergency evacuation planning: A survey

Emergency evacuation planning is essential in disaster management. Numerous evacuation model and optimization algorithms have been developed and implemented for various types of emergencies mainly in natural and man-made disaster. This paper reviews such approaches focusing on macroscopic in emergency evacuation planning. The strength and weaknesses of algorithms and models are described. Variables considered in algorithms such as number of evacuee and evacuation time have been seen important to be applied to real evacuation scenario. Most of optimization algorithms demonstrate promising results in minimizing travelling time from the affected area to safety. Thus, algorithms should be enhanced to consider different scales of evacuation in different type of disasters. With the provision of efficient evacuation planning, it would help relevant authorities to reduce risk disaster management by offering a tool to support evacuation processes.

Solving vehicle assignment problem using evolutionary computation

This paper examines the use of evolutionary computation (EC) to find optimal solution in vehicle assignment problem (VAP) The VAP refers to the allocation of the expected number of people in a potentially flooded area to various types of available vehicles in evacuation process A novel discrete particle swarm optimization (DPSO) algorithm and genetic algorithm (GA) are presented to solve this problem Both of these algorithms employed a discrete solution representation and incorporated a min-max approach for a random initialization of discrete particle position A min-max approach is based on minimum capacity and maximum capacity of vehicles We analyzed the performance of the algorithms using evacuation datasets The quality of solutions were measured based on the objective function which is to find a maximum number of assigned people to vehicles in the potentially flooded areas and central processing unit (CPU) processing time of the algorithms Overall, DPSO provides an optimal solutions and successfully achieved the objective function whereas GA gives sub optimal solution for the VAP.

Revised equations for Manning's coefficient for Sand‐Bed Rivers

Abstract The procedure for selecting values of Manning n is subjective and requires judgment and skill which are developed primarily through experience. Government agencies and private sectors in developed nations such as the USA are still doing research on predicting n values for rivers. Since flow and boundary roughness vary with river conditions, such research is therefore pertinent for rivers in Malaysia where floods are one of primary concerns. Research on Manning n value was started by River Engineering and Urban Drainage Research Centre (REDAC), Universiti Sains Malaysia (USM) since 2000 at the Kinta River catchment. Further data collections were later made at two other major rivers i.e. Langat River and Kulim River. Two new equations are proposed for determining Manning n for sand‐bed rivers in Malaysia based on 163 data collected from these three rivers. On average, both equations have an error less than 10% in predicting flow discharge for all 163 data.

A discrete particle swarm optimization with random selection solution for the shortest path problem

This article proposes a discrete particle swarm optimization (DPSO) for solution of the shortest path problem (SPP). The proposed DPSO adopts a new solution mapping which incorporates a graph decomposition and random selection of priority value. The purpose of this mapping is to reduce the searching space of the particles, leading to a better solution. Detailed descriptions of the new solution and the DPSO algorithm are elaborated. Computational experiments involve an SPP dataset from previous research and road network from Malaysia. The DPSO is compared with a genetic algorithm (GA) using naive and new solution mapping. The results indicate that the proposed DPSO is highly competitive and shows good performance in both fitness value and processing time.

An Improved Discrete Particle Swarm Optimization in Evacuation Planning

In any flash flood evacuation operation, vehicle assignment at the inundated areas is vital to help eliminate loss of life. Vehicles of various types and capacities are used to evacuate victims to relief centers. This paper examines combinatorial optimization approach with the objective function to assign a specified number of vehicles with maximum number of evacuees to the potential inundated areas. Discrete particle swarm optimization (DPSO) and improved DPSO are proposed and experimented on. Results are presented and compared. Improved DPSO with the proposed min-max approach yields better performance for all four testing categories. Experimenting on a large number of evacuees could further improve the performance of the DPSO.

DPSO based on a min-max approach and clamping strategy for the evacuation vehicle assignment problem

Abstract The most challenging task during a flood event is to evacuate people from the affected areas to safer locations. The difficulty of this task is attributed to an uneven distribution of vehicles, a lack of timely assistance, a deficiency in decision making and poor coordination at the operational level. Although it is important to identify flood-prone areas, the assignment of vehicles to the appropriate evacuation routes is of primary importance. The latter consideration is a crucial determinant of the number of people who are saved in any emergency evacuation. This paper proposes an improved discrete particle swarm optimisation (DPSO) algorithm for solving the evacuation vehicle assignment problem (EVAP). Discrete particle positions are proposed that support the implementation of this DPSO. A min-max approach is used for the initial calculations of particle positions for the EVAP. This algorithm was computationally tested using different numbers of potentially flooded areas and compared with an average DPSO approach and with genetic algorithms. The findings of this study reveal that DPSO with a min-max approach that incorporates a new velocity clamping procedure offers good performance with respect to maximising the number of individuals who can be evacuated by vehicles from diverse locations for situations involving various numbers of potential flooded areas.

A multi-valued discrete particle swarm optimization for the evacuation vehicle routing problem

An optimal evacuation route plan has to be established to overcome the problem of poor coordination and uneven distribution of vehicles before or during disaster. This article introduces the evacuation vehicle routing problem (EVRP) as a new variant to the vehicle routing problem (VRP). EVRP is a process of moving vehicles from a vehicle location to the potentially flooded area (PFA), and from PFA to relief center using a number of capacitated vehicles. This paper examines the application of a multi-valued discrete particle swarm optimization (DPSO) for routing of vehicles from vehicle location to PFA. A solution representation is adopted and modified from the solution of the shortest path problem (SPP) to accommodate this problem. Experimental results were tested based on the objective function of finding a minimum total travelling time using datasets from a flash flood evacuation operation. DPSO was found to yield better results than a genetic algorithm (GA).

Optimal capacitor placement and sizing in an unbalanced three-phase distribution system using particle swarm optimization technique

Installing shunt capacitors in a radial distribution system can provide a lot of benefit to utility and users. There are many ways to install capacitors as long as the sizes do not exceed the original inductive reactive power of a system. But it is very difficult to determine the best locations and size of capacitors. In addition, the unbalance nature of distribution system make the placement becomes more complicated. Optimal placement of capacitor is a highly nonlinear optimization problem which requires discrete control variables. This paper proposed an artificial intelligence approach to obtain optimal total line loss reduction and total cost of capacitor while improving the voltage profile along the feeders. It was done by integrating the circuitry distribution model in SIMULINK with particle swarm optimization (PSO) technique constructed in MATLAB. The optimization process was done in two stage where the first stage is to determine the early optimal locations while second stage is to determine optimal sizes. A modified IEEE 13-bus three phase unbalanced radial distribution system is used to validate effectiveness of the proposed technique in solving the problem.

Model Reliability Assessment: A Hydrodynamic Modeling Approach for Flood Simulation in Damansara Catchment Using InfoWorks RS

This paper presents the flood inundation model using a hydrodynamic approach for flood simulation. A Digital Elevation Model (DEM) for Damansara Catchment was developed and integrated into the InfoWorks River Simulation (RS) program. Hydrographic surveying activity was carried out to collect existing Damansara River cross-section. The 3D flood model was calibrated using the 26th February 2006 and 15th October 2008 flooding event data. The model was validated using the flooding event on 21st March 2007. This computer simulation results produced hydrograph that indicates the capability of the model in dealing with regional flood analysis for future usage in designing structural flood measures.

Mapping of regional potential groundwater springs using Logistic Regression statistical method

Increasing demand for fresh water extraction in the semi-arid regions necessitates the exploration of groundwater spring potential areas notwithstanding the importance of both conservation and management aspects for planning development. Potential map of groundwater springs reduces the costs of horizontal well drilling that provides useful tool for engineers to locate probable region for groundwater existence. The objective of this study is to establish a model of the potential map of groundwater spring occurrences. A statistical and probabilistic Logistic Regression (LR) model was developed in association with the specified spring location and effective occurrence factors. The most statistically significant effective factors on spring occurrences were selected to zone groundwater spring potential areas. The proposed model was evaluated statistically. Results showed a satisfactory prediction for the proposed model. The outcome of this study facilitates the low-cost utilization of groundwater resources when policy makers need strategic development planning.