Search and Rescue (SAR) operations aim at finding missing objects with minimum time in a determined area. There are fundamentally two problems in these operations. The first problem is assessing highly reliable probability distribution maps, and the second is determining the search pattern that sweeps the area from the air as fast as possible.
In this study, geographic information systems (GIS) and multi criteria decision analysis (MCDA) are integrated and a new model is developed based upon Search Theory in order to find the position of the missing object as quickly as possible with optimum resource allocation. Developed model is coded as a search planning tool for the use of search and rescue planners. Inputs of the model are last known position of the missing object and related clues about its probable position.
In the developed model, firstly related layers are arranged according to their priorities based on subjective expert opinion. Then a multi criteria decision method is selected and each data layer is multiplied by a weight corresponding to search expert’s rank. Then a probability map is established according to the result of MCDA methods. In the second phase, the most suitable search patterns used in literature are applied based on established probability map. The developed model is a new approach to shortening the time in SAR operations and finding the suitable search pattern for the data of different crashes.
CHAPTER ONE ON ROLE OF GIS IN SEARCH AND RESCUE OPERATIONS
Search and Rescue (SAR) is an operation to find and rescue the people in distress either in a difficult area, such as in mountains, deserts, forest or at sea (Stone, 1975). SAR operations have several distinct stages. In the first stage, the most likely location of the missing object is determined. This information is then processed with other considerations to decide the initial importance and scale of the operation (Frost, 1999).
The next stage is the search stage, in which a search is mounted by appropriate search tools like searching the area from air or land. When a search mission is required, there are four factors which should be considered immediately:
An adequate description of the search target,
The search area condition, including weather and any possible risks and dangers,
The best search pattern, sweeps the area optimally,
The appropriate track spacing, according to the terrain conditions (Haley and Stone, 1979).
Then the third stage is the Rescue stage, at this stage support is rendered to the object where it is found, to allow it to be safely transported to a place, where more intensive aid can be provided.
Finding the people in distress as quickly as possible requires well designed SAR planning and better use of technology (Zeid and Frost, 2004). For either complex or simple search operations, a search plan should always be developed by the control of the search expert, as many lives may depend on the care with which the search is planned and conducted (NSAR, 1998).
SAR planning operations differ from each other according to the search location. These are; inland search and rescue planning, maritime search and rescue planning, and aeronautical search and rescue planning. The latter is the subject of this study.
Aeronautical Search and Rescue (ASAR) term defines operations that are carried out by aircrafts which are the most satisfactory units for searching large areas quickly (IMCO, 1980). An ASAR planning operation characteristically involves three main parts, and requires several layers of information about probable position of the target (IAMSAR, 2001). The first and second steps in aeronautical search planning are to search along the track visually and electronically and determine the limits of the area containing all possible survivor locations, respectively (NSAR, 1998). This is usually done by determining the planned route of the missing airplane. Currently, search planners are mainly using the New TwoArea Method (NTAM) which sweeps the either side of track the search area along the route of the plane; commonly a search performed in an area of 10 nautical miles either side of track, the method was developed by the Canadian Department of National Defense’s Directorate of Air Operational Research (Zeid and Frost, 2004). This method is based on research of 76 missing aircraft missions conducted in Canada from 1981 to 1986 (NSAR, 1998). The usage of this method requires search planners to have the last known position (LKP) of the missing aircraft and the intended route of the missing aircraft, and the intended destination of the missing aircraft. From this information the search area is defined for prioritizing the search.
The last stage in ASAR planning is computation of a probability area by using navigational tolerance, LKP and signals from the area. Hence, SAR planners have to know the LKP, route, and destination as discussed, but there are other mitigating factors that often allow planners to focus the search efforts. These include a known flight plan, signals from the area, radar, witnesses, or known Emergency Locator Transmitters (ELT) signals or distress calls in the area of possibility of the search.
1.1. Search Theory in SAR
The theory of how to search for missing objects has been a subject of serious scientific research for more than 50 years. It is a branch of the broader applied science known as operations research (Frost, 1999). In more recent years, the principles of operations research have been applied to a wide variety of problems that involve making good decisions in the face of uncertainty about many of the variables involved (Champagne et al., 1999).
Search theory is defined by Cadre and Soiris (2000) as a discipline that treats the problem of how a missing object can be searched optimally, when the amount of searching time is limited and only probabilities of the possible position of the missing object are given. The development of search theory and its search application and rescue consist of three main parts. These are scientific research and subsequent developments, developments of search planning doctrine with SAR manuals and development of computer based search planning decision support tools (Stone, 1989).
The first part is mainly developments in a scientific research side. Search theory was first established by Koopman during World War II using the new techniques of operations research. First applications of search theory were made on military operations (Stone, 1975). Koopman (1980) stated that the principles of search theory could be applied effectively to any situation where the objective is to find a person or object contained in some restricted geographic area. After military applications it was applied to different problems such as; surveillance, explorations, medicine, industry and search and rescue operations (Haley and Stone, 1979). The aim of searching in the context of ASAR is to find the missing aircraft effectively and as quickly as possible with the available resources (Stone 1989). In search theory framework, effectively means minimizing the time required to find the search object while maximizing the chances for finding the object.
Koopman (1956) defined Search theory in three reports. The first one is Kinematics-based, which includes the analytical description of equations of a target and observer movement, description of equations of probability value of connecting an observer and a target and equation describing the randomly distributed targets. The second report was about target detection that consisted of analytical description of instantaneous probability for target location, analytical description of horizontal distance distribution and the analytical description for a common case of a random search. The last report investigated the problem of optimum distribution of search efforts.
The second part of developments in search theory involves establishment of search planning doctrine. In 1957 the U.S coast guard first articulated its search planning doctrine in the form of search and rescue manual and showed how the basic principles of search theory were applied to the SAR planning process. (Stone, 1989)
The third part is development of computer based search planning decision support tools. According to Stone (1989) in the early 1970s Richardson (1972) developed the computer assisted search planning (CASP) system for dynamic planning of search for ships and people lost in the sea. Then, CASP systems were developed to assist U.S navy in planning submarine searches.
The first computer based systems mostly used for marine SAR operations (Stone 1975). In order to reach a target in minimum time with limited resources, it is important to use CASP systems to increase the speed of the search. With the development of search theory CASP systems have been used since 1970s.
CASP systems were first introduced by United States Coast Guard in 1974s (Champagne et al., 1999). It was based on Monte Carlo simulation and applied in naval SAR operations. CASP generates an initial probability distribution taking in to account current wind and environmental information. Richardson and Corvin (1980) used CASP systems in marine SAR operations. They have stated three types of SAR scenarios to construct initial probability map. These are referred as position, area and track line scenarios for distress objects. Position Type initial target probability distribution is modeled as bell shaped distribution because it considers that the missing object is not stationary. In the area type scenario the search area is bounded and the probability in the area is thought to be as uniform for distress object. The third scenario is the track line scenario. It is used if a reported track of the object is assumed to be true.
CASP systems were limited in terms of spatial data (Cooper et al., 1999). Therefore, in order to put up limitations about spatial data, it is important to integrate GIS into CASP systems used in ASAR operations. Use of GIS is highly increased in SAR operations. Hence, GIS permits to analyze the relationships between different data layers easily and effectively.
Cooper et al., (1999) have focused inland search and planning techniques. They developed a methodology for land search planning and developed computer based search planning decision support tool for land SAR. Champagne et al., (1999) tested three of the search patterns used only for naval search and rescue operations. They examined with respect to number of U boats sighted by aircrafts as a measure of search efficiency. Their studies conclude that search patterns have impacts on search efficiency in naval SAR operations.
Wollan (2004) investigated creation of search patterns in ASAR operations. Besides generic search patterns in use; Wollan tested heuristic algorithms in the mean of minimizing the time. In the study of Wollan (2004) added GIS into a developed search management implementation. Also Wollan offered to modify search patterns individually to accommodate the area that needs to be searched instead of using generic search pattern.
Zeid and Frost (2004) have developed a decision support systematic for Canadian search and rescue operations in the case of lost air craft. They developed an optimization module based on search theory, on gradient search methods. They compared their system with current Canadian manual SAR system.
1.2. Statement of Problem
Search and rescue operations are spatial activities (Haley and Stone, 1979). Search planners must combine information on where the missing object was last seen, likely routes, and maps of the areas already searched, time last searched, and available resources to effectively mount a search area (Burrough and Frank, 1995). The main problem is to produce the reliable probability maps, which accounts for these clues (Stone, 1975).
The majority of the SAR planning is made through the ease of methods described in the United States National Search and Rescue Manual (NSM) and Canadian National Search and Rescue Manual (NSAR). However, these documents are adequate for describing search planning doctrine and providing practical guidance for planning searches with only traditional tools like pencil, paper, nautical charts etc. They are inadequate as a guide to search theory or to the practical application of the currently available considerable computing power to the search planning problem (Frost, 1999).
While preparing probability maps of the suspicious distress area, integrated spatial technologies such as geographic information systems (GIS) would be an ideal solution for aeronautical search and rescue operations (USADT, 2001). The dynamic relation between maps and the spaces represented in SAR operations is common to geographic information systems. Therefore, GIS presents itself as the most useful tool in making effective SAR operations.
Moreover, for more accurate probability maps it is important to include more inputs about the location of the missing object (USR, 1991). In order to enrich the information about the area and getting more reliable probability maps about its distribution on the area, GIS could be used as a tool (Armstrong and Cook, 1979). In the absence of inputs on the contrary, it may be assumed that the most probable area within which a missing aircraft will be found is along the intended track from LKP to intended destination and within a reasonable distance either side of track (NSAR, 1998).
Each SAR mission has different characteristics (Stone, 1983). Zeid and Frost (2004) stated that, parameters related with the missing plane, such as intended route, is an important role of the SAR planning. However, if the route of the plane is not known like air combat maneuvering, achieving this step cannot be possible to sweep the route of the plane. In this kind of search operations it is impossible to sweep the either side of track, in the search area along the route of the plane. It can be faced with many crashes, such as dog fight flights which is a common flight type used to describe close-range aerial combat between military aircrafts (Web 1). In this type of flights route information of the missing plane cannot be determined to include into SAR mission planning. SAR planners can cope with this problem by calculating maximum distance the survivors could have traveled between the time of their LKP and the known or assumed time of the distress incident and drawing a circle of that radius around the LKP. Knowing the extreme limits of possible locations allows the search planner to determine where to seek further information related to the missing airplane. However, systematic search of such a large area is normally not practical. Therefore, the next step is shrinking the search area as possible.
Besides the problem of decreasing size of the search area, an aeronautical search and rescue operation requires access to information from many different sources in order to properly respond to an emergency or incident. An incident must be understood within the context of the environment that it has occurred (Robe and Frost, 2002). Generally, search environment is very dynamic and SAR operations need to include all of the dynamic factors. Therefore, this process can be summarized as location of an incident and how to access that location. The most logical way of organizing such data could be geospatial monitoring and analysis.
Missing aircraft search methods are very intensive and tie up many resources that could be used elsewhere (Stone, 1975). SAR planning procedures plans basically where, when and how to search. Therefore, determining the optimal search area is the main problem. The subject of search theory is constructing a probability distribution for the location of the missing object in order for optimal resource allocation (Cadre and Soiris, 2000).
The use of GIS in SAR operations is growing very rapidly. SAR operations benefit greatly from the GIS technology in recent years. In recent studies (Liu et al., 2006; Wollan, 2004; Zeid and Frost, 2004) lots of applications were developed to help solving SAR problems. Many early systems like computer aided search planning systems (CASP) were developed to solve relatively narrow, specific kinds of problems. The past twenty years have seen an explosion in the technological base for these systems, particularly in the areas of spatial data processing in GIS technologies. Zeid and Frost (2004) tested how their GIS integrated tools could be applied in SAR operations in Canada. Wollan (2004) used GIS for defining the search area, generating the search patterns and viewing the current status of search effort. (Web 2) presented examples of how the development of GIS increases capabilities in a natural disaster management in the example of SAR operations in India.
According to above mentioned state of the art, the following problems related to SAR operations can be listed:
The problem of producing highly reliable probability map from the limited available information to conduct a SAR operation,
Highly subjective decisions by SAR expert while distributing resources to the area,
Dividing the area into sub sectors before considering the probability map,
The problem of having no information about intended route, and intended destination of the missing aircraft.
In the present context, due to scientific advances it has become easier to carry out SAR operations efficiently with the use of GIS, which help to identify areas that are probable location of missing objects, searching them according to probability distribution maps, and simulating search operation according to those maps. Moreover, GIS is useful even in managing SAR planning as it provides instant access to information and analyzing efficiently required for search management decisions (Web 3).
The integration of GIS and MCDA provide a powerful tool to generate probability maps of the search area, since GIS provides efficient manipulation and presentation of the spatial data and MCDA supplies consistent ranking of the spatial layers and clues from the area based on a variety of criteria.
The proposed methodology is coded as computer software (METUSAR), which allows SAR planners to observe how the conditions and clues affect the probability maps and SAR planning process. It creates a condition for SAR planners for ranking and rating the environmental and geographical data. In computer program three goals are met. Firstly, the system is designed in a manner that allows for ease of understanding for the search experts. Secondly, the system involves all the steps of preparing probability map for SAR operations rather than separate parts of functions. Thirdly, the system contains spatial and geographical data, in order to get more accurate probability map. These characteristics are combined in geographic information systems, based on multi-criteria decision analysis tool for SAR operations.
The developed methodology and the coded Software are implemented on the case of Plane Crash in Kaduna in 2021. The search time of this crash was one of the most excessive ones in the Nigeria Airforce (Gerede, 2007), as the plane was in a dog fight. The case is used for performance testing of the methodology and the software.
1.3 Objective of study
In this study it is aimed to develop a new systematic integrated methodology which could reduce the time it takes to find survivors of plane crashes, and thus save lives. The proposed methodology with the desired characteristics integrates Search Theory, for constructing reliable probability distribution maps with GIS; for acquiring, integrating and analyzing data coming from different heterogeneous sources and Multi Criteria Decision Analysis (MCDA) methods for constructing probability maps.
1.4 Significance of study
Particularly, this study focuses on incorporating the spatial data and clues from variety of sources to create a meaningful, accurate and comprehensive representation for aeronautical search and rescue planning. After preparing reliable and accurate probability map, search pattern efficiency is tested. In order to do this properly, the criteria necessary for the preparing of the probability map is reviewed.
The main innovation of this study is integration of search theory and MCDA within GIS framework and providing a single integrated system for ASAR operations. The proposed methodology provides easy retrieval of spatial and non spatial information, analysis of this information in the light of Search Theory concepts and estimating consequences of proposed SAR plans.
1.3. Organization of the Thesis
Outline of the thesis is as follows;
First chapter starts with a brief summary of SAR planning and problem definitions as well as aim of the thesis.
In the second chapter, the historical and theoretical framework of Search Theory is presented. The terminology is defined briefly.
In the third chapter, developed methodology of the thesis is explained. Also, preparing the probability map with using MCDA is described. Moreover, the steps of methodology which are classification of probability maps and search pattern comparison are discussed.
In the fourth chapter, the software design and development is mentioned, moduleby-module.
In the fifth chapter, implementation of application on the case study area is discussed. The data related to the incident is presented and the status of the incident area is set accordingly. Geographical settings of the study area and all layers used are given. Different search patterns are compared.
In the Conclusion chapter, an evaluation is made, regarding the aim, objective of the study and analysis for these objectives. Finally, recommendations and conclusion of the study were discussed and some ideas are given about the future studies.