**2. Problem statement**

When we design an emergency rescue system, we need to coordinate the manpower with the financial, material resources. It is a complicated process to optimally allocate various elements within a system. It involves a wide range of contents. Repeated researches should be made on several theories and methods. Designing of an emergency rescue system covers the following four aspects (See Figure 1):

Fig. 1. Research Course for the Emergency Rescue System.

These four aspects have been cross-linked each other essentially.

1. Demand Forecasting of Emergency Resources

In recent years, unconventional emergencies frequently broke out, severely endangered people's life and property. How to timely predict people's demand on resources after the disasters? This issue has become an important problem for us.

Here, a precise predictive method has been designed by combining the Fuzzy Set Theory with the Learning Rules of Hebb Neural Network, Multiple Linear Regression and Case

in disaster-stricken areas. The disaster-stricken people didn't have enough emergency resources to make their living. Consequently, they ransacked shops for food and medicine

These two examples have fully revealed the importance of designing a sophisticated emergency rescue system. The loss of public emergencies would be greatly reduced by understanding the distribution of disaster-stricken people and providing appropriate emergency resources to them. Otherwise, the public emergencies would be uncontrollable. To make things worse, the situation of disasters might be more serious, and even lead to the

When we design an emergency rescue system, we need to coordinate the manpower with the financial, material resources. It is a complicated process to optimally allocate various elements within a system. It involves a wide range of contents. Repeated researches should be made on several theories and methods. Designing of an emergency rescue system covers

> Optimal dispatching of emergency resources Optimal allocation of emergency resources

Site selection for the base station of emergency resources Demand forecasting of emergency resources

and severely undermined the local social order.

breakout of secondary disasters.

the following four aspects (See Figure 1):

Fig. 1. Research Course for the Emergency Rescue System.

1. Demand Forecasting of Emergency Resources

These four aspects have been cross-linked each other essentially.

disasters? This issue has become an important problem for us.

In recent years, unconventional emergencies frequently broke out, severely endangered people's life and property. How to timely predict people's demand on resources after the

Here, a precise predictive method has been designed by combining the Fuzzy Set Theory with the Learning Rules of Hebb Neural Network, Multiple Linear Regression and Case

**2. Problem statement** 

Reasoning. By applying this method, we have solved the problems of information insufficiency and inaccuracy when we predict the resource demand after unconventional emergencies, and could correctly predict people's demand on resources.

2. Optimal Site Selection for the Base Station of Emergency Resources

If the resource demand has been determined, sufficient emergency resources need to be transported to the emergency base stations (Emergency rescue station). To achieve this goal, how many base stations for emergency resources should be established, and where should we establish these stations, these issues will be worth considering. In other words, we should optimally select the sites of base stations and find appropriate locations within a certain region as the base stations of emergency resources. The number of location should also be suitable. When disasters break out, we could allocate resources from these base stations to deal with the emergencies. By optimally selecting the sites of base stations, we could not only reduce costs, but could also ensure the timeliness of emergency resources, making these resources arrive at the emergency scenes quickly, safely and timely.

Here, a summarization has been made on relevant site selection knowledge, and the Operations Research theories have been applied based on the existing site selection methods. A multistage model of site selection has been designed to make an optimal planning on the number and location of base stations. Example analysis has also been made to verify the results of calculation. It has been proved that this model is simple, convenient for use, and could get results quickly. This model would be suitable for the site selection and planning of base stations of emergency resources.

3. Appropriate Allocation of Emergency Resources

The emergency resources deployment is a hardcore of emergency management. After the happening of the public emergency, it is important to study how to deliver the emergency resources to base stations quickly. When we've determined the location of base stations, we should optimally allocate emergency resources. More to this point, it should predetermine the number, type and quality of resources for each base station. Otherwise, there's an important constraint condition for us to consider: the costs.

This chapter proposed the dynamic optimal process of emergency resources deployment planning, making use of Markov decision processes, and discovered the optimal deployment planning to guarantee the timelines.

4. Optimal Dispatching of Emergency Resources

Aiming at solving the resource allocation problems in case of emergency events, this chapter presented an optimum mathematical simulation model based on the dynamic programming.

In accordance with the number of emergency base stations, the given model tries to divide the resource allocation procedure into the some stages. The stated variable stands for the amount of the emergency resource available for allocation can be used at the beginning of each stage. As is depicted in the dynamic programming theory, the remaining resource of the previous stage may have a strong influence on the succeeding stage. During each stage, three factors may restrict the object function, that is, the remaining resource, the decision, and the demand. The total function is the sum of the object function of each stage. In

Theories and Methods for the Emergency Rescue System 125

Over the last few years an alternative reasoning paradigm and computational problem solving method has increasingly attracted more and more attention. Case-based reasoning (CBR) solves new problems by adapting previously successful solutions to similar problems. CBR is attracting attention because it seems to directly address the problems outlined above. CBR does not require an explicit domain model and so elicitation becomes a task of gathering case histories, implementation is reduced to identifying significant features that describe a case, an easier task than creating an explicit model, by applying database techniques largely volumes of information can be managed, and CBR systems can learn by

The work Schank and Abelson in 1977 is widely held to be the origins of CBR [7]. They proposed that our general knowledge about situations is recorded as scripts that allow us to set up expectations and perform inferences. Whilst the philosophical roots of CBR could perhaps be claimed by many what is not in doubt is that it was the work of Roger Schank's group at Yale University in the early eighties that produced both a cognitive model for CBR and the first CBR applications based upon this model [8]. Janet Kolodner developed the first CBR system called CYRUS [9-11]. An alternative approach came from Bruce Porter's work, at The University of Texas in Austin, into heuristic classification and machine learning

In the U.S., Edwina Rissland's group at the University of Massachusetts in Amherst developed HYPO [14]. This system was later combined with rule-based reasoning to

In Europe, the first one is that of Derek Sleeman's group from Aberdeen in Scotland. They studied the uses of cases for knowledge acquisition, developing the REFINER system [16]. Mike Keane, from Trinity College Dublin, undertook cognitive science research into analogical reasoning [17]. Michael Richter and Klaus Althoff in the University of Kaiserslautern applied CBR to complex diagnosis [18]. This has given rise to the PATDEX system [19] and subsequently to the CBR tool S3-Case. In the University of Trondheim, Agnar Aamodt has investigated the learning facet of CBR and the combination of cases and

In the UK, CBR seemed to be particularly applied to civil engineering. A group at the University of Salford was applying CBR techniques to fault diagnosis, repair and refurbishment of buildings [22]. Yang & Robertson [23] in Edinburgh developed a CBR system for interpreting building regulations, a domain reliant upon the concept of precedence. Another group in Wales applied CBR to the design of motorway bridges [24].

According to the characteristics of emergency resource demand prediction process, both the risk analysis and case-based reasoning method are introduced into the process, accordingly a case-based reasoning method for emergency resource demand prediction based on risk analysis is obtained, which improves the scientificity of emergency resource demand. The case-based reasoning flow for emergency resource demand prediction based on risk analysis

Further, there are active CBR groups in Israel [25-26], India [27] and Japan [28].

acquiring new knowledge as cases thus making maintenance easier.

resulting in the PROTOS system [12-13].

general domain knowledge resulting in CREEK [20-21].

**3.1.2 Methods for emergency resource demand prediction** 

produce CABARET [15].

is shown in Fig.2.

addition, the concrete case can be used to confirm the model's validity and practicability. The results of our repetitive experimental application of the model show that it works perfectly for its duty in improving the efficiency of emergency management and overcoming the problem of wasting emergency resource as well as low efficiency in emergency rescue.
