[IEEE 2014 Third International Conference on Agro-Geoinformatics - Beijing, China (2014.8.11-2014.8.14)] 2014 The Third International Conference on Agro-Geoinformatics - A design of spatial decision support system to enhance decision progress in agricultural actions
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A design of spatial decision support system toenhance decision progress in agricultural actionsMeng-Ying Li, Chih-Hong Sun and Min-Fang Lien
Taiwan GIS CenterTaipei, Taiwan
Tsun-Kuo ChangDepartment of Bioenvironmental Systems Engineering
National Taiwan UniversityTaipei, Taiwan
AbstractAgricultural actions usually involve a decision pro-cess collaborating different decision makers and officers. Dif-ferent decision process may encounter different difficulties de-pending on the type of actions. In order to make efficientprogress in actions, a support system for decision making shouldbe constructed to include not only supporting information forthe decision, but also tools for decision management. For suchdecision support and management system, enhancement in taskcommunication, data summarizing and subtask managementshould be the focus of the development. In this study, we usedMAKOCI (Multi-Agent Knowledge Oriented CyberInfrastruc-ture) as a geospatial platform to convey customized decisionknowledge, working procedures, and data processing capability,where ontology and multi-agent are implemented to facilitatemodularized application design. A case study on detection andresponses of agricultural heavy-metal contamination is given inthis study to exemplify the functions of the decision supportsystem designed. By the proposed design, the spatial decisionsupport system facilitates hierarchical task definition and cross-government communications, in addition to supporting spatialand non-spatial decisional information. The decision progress canconsequently be enhanced by monitoring, communication andmanagement of actions among partaking users of the system.
Agricultural actions, such as farmland contamination reme-diation or livestock epidemic prevention, usually involve adecision process that requires collaboration among differentdecision makers and officers. Such decision process may facedifficulties that hinder the progress of the action. For example,calls for decision usually come under stressful situations, andproblem definition are usually unclear. In addition, technicalchallenges, such as the lack of information to support decisionmaking, difficulty in decision communication, and difficulty inrelating pieces of decisional information, also build up barriersin decision making.
In order to make efficient progress in actions, a decisionmaking and implementation management system should beconstructed to include tools that provide not only requiredinformation according to the respective decision, but also amanagement system for the progress of action implementation.In particular, the tools should be able to enhance hierarchi-cal communication in task assigning and reporting, enhancecross-government communication in task collaboration, andconstruct mechanisms for intelligent data summarizing andsubtask management in order to achieve better decisionalresponses.
II. THE NEED OF A SUPPORTING SYSTEMDecision making for agricultural actions come in differ-
ent form, while the idea of making decisions constantlyinvolves the evaluation of alternatives regarding a wide rangeof considerations. The following are examples of challengesthat decision makers are faced when making decisions foragricultural actions:
Calls for decision usually come under stressful situations
Objectives for decisions are often conflicting  Decision making involves high complexity ,  Problem definition are usually unclear  Objectives are usually unclear  Consequences of decision cannot be assessed  Lack of information  Difficulty in implementing decisions  Difficulty in evaluating information sufficiency  Difficulty in decision communication  Difficulty in relating pieces of information  Unknown decision efficacy 
In a conventional form of spatial decision support system(SDSS), data sets are visualized for decision makers assummaries of decisional information. The type of difficul-ties resolved by the conventional decision support system,consequently, would be limited to visual judgment of thedata presented. For decision makers, the most difficult partfor decision making, including those listed above, essentiallyoriginate from the nature of the agricultural problem itself,which rely on humans experience to resolve the situation, anddata visualization may only contribute weakly and indirectlyto the entire decision process. In fact, the examples listedabove are identified as related to the lack of communication ortechnical development, and could be approached with propertechnology. To meet the end for enhancing the communicationin a decision-support system, consequently, we designed acollaboration mechanism with decision-support informationintegrated, such that the efficiency in decision making couldbe improved.
III. WEB APPLICATIONS AND THE MAKOCI PLATFORMThe implementation of collaborative decision making calls
for a communication tool to transport decision transactionsbetween decision makers. Considering the nature of collab-orative communication where users may not be using the
system at all times, the capability for data storage and internetcommunication are indispensable. A communication tool fordecision transactions, consequently, takes the form of mobileapps or web applications in most cases. In this study, wepropose the use of web applications to communicate amongdecision makers, in light of its compatibility to most internet-accessible facilities.
To facilitate the creation of web applications, we developedMAKOCI (Multi-Agent Knowledge Oriented CyberInfrastruc-ture)  as a geospatial platform that conveys decision knowl-edge, working procedures, and data processing capability todesignated users of a decision support system. The purposefor constructing the MAKOCI platform is to facilitate creationof customized decision-support system for decision makers,via the management of shared or leased GIS resources. Tomeet this end, a web page for the platform is designed toserve two purposes: (1) to match between the technologicalcapabilities of the industry and requirement between decisionmakers, and (2) to provide catalog service for registering web-serviced data, analytic/simulation models, or developed webapplications. As a conveyor of decision-support knowledge,MAKOCI implemented Web Ontology Language (OWL) , on the categorization of resources and users, as wellas computer-coded knowledge for workflows, models andrequirement-oriented application designs. Multi-agents  aredeployed on the basis of the ontology structure to allowautomatic data explanation and coded system reaction for theknowledge-based decision support.
IV. SOLUTIONS TO THE CHALLENGES
In this study, we implemented a decision management andsupporting framework for producing web applications, in orderto provide solutions to decision challenges. The frameworkconsists of the following components:
Clarification of requirements: to resolve the situation withstressful situations, unclear or conflicting problems andobjectives.
Management of decisions: to resolve the situation withcomplex-structured decision, and the difficulty in hierar-chical and cross-government communication.
Enhancement of information efficiency: to resolve thesituation with lack of information, information updates,relating pieces of information, implementation difficulty,information sufficiency, and decision consequence.
Implementation of the framework is described in the followingsubsections.
A. Solution for clarification of requirements
We propose to alleviate the stress in decision making byidentifying the workflow and tasks to be accomplished beforea recurrent emergency is encountered. Examples of such re-current emergency includes flood inundation, contamination orepidemic outbreaks, which are usually responded according toa standard operating procedure (SOP) that assigns governmentunits in charge and directs the actions to take under definedstages of the emergency. Problems and objectives would be
Fig. 1. The web portal for the MAKOCI platform
defined clearly in such SOP, while the people in charge ofthe tasks may or may not always be prepared for all typesof emergencies in charge when multiple tasks of variousemergencies are assigned to a single government unit.
The information science community has developed theoriesand instruments of ontology development to enable the system-atic organization of human intelligence and knowledge .By applying the instruments of ontology, the SOP as humanknowledge can be clearly defined as computer-readable infor-mation for further use in software development. For clarifyingthe requirements of decision responses, the first step wouldbe defining the functional requirements of the applicationbetween the decision makers and software developers. Toprovide a platform for defining the functional requirements,the main page of the MAKOCI platform was designed as amatching platform for decision makers and developers (Fig. 1).The functional requirements are posted by the decision makersto advertize the need of a custom-made application, and web-application developers would search over the database of therequirements to look for project development opportunities.
Further details of the functional requirements are definedbetween decision makers and developers after the developerscontact the decision makers, such that the SOP and detaileduser/function requirements are define. To ensure the require-ments are clearly defined, the computer-readable knowledge iscoded in the format of OWL file format  for the SOP ofan agricultural action across governments involved, to describethe subtasks for each partaking user.
B. Solution for management of decisions
With the SOP defined for decision makers in charge andtasks to be completed in response to the agricultural actions,we designed a decision-communication framework for thecollaboration and management of decisions. The decisioncommunication mechanism includes the following features for
a clear definition of tasks to be completed and progress ofcompletion:
A visual presentation of the decision relations, usually inthe form of a decision tree that describes the relation ofdecision makers under a hierarchical structure.
A list of events according to the monitoring data orwarning broadcasts that indicates the possible need ofagricultural actions.
A list of tasks to be completed for the agricultural actionsin response to the events indicated by monitored data orwarning broadcasts.
Percentage of tasks completed with respect to the numberof tasks to be completed.
A diagram representing the hierarchical relations of decisionmakers is presented upon login of each user, with percentageof completion indicated for each user to have an overviewof subtasks completed. Administrative assignment is codedas subtasks for administrators of all hierarchical levels, andsubtask reporting and communication is visible to the upperhierarchy to enhance the progress of each agricultural action.
C. Solution for enhancement of information efficiency
In this study, we propose to improving the informationefficiency by providing sources of concurrent data and ana-lytical/simulation model, as well as multi-agents to listen tothe status of the data that decides on which latest and qualityassured data data or models to access or invoke.
The listening and action mechanism is accomplished usingmulti-agent technology. The multi-agent technology is anarea in information science that has rapidly developed inrecent years. This technology enables information systems todynamically exchange and share information through agents,and accomplish tasks and goals in collaboration . Byusing the latest and most quality-assured data and models,a decision support system resolves the situation with the lackof information and updates of information, with informationsufficiency validated. In addition to the updating of data andmodels, multi-agents also provides mechanism to relate piecesof information to decision subtasks, invoke the simulation ofmodels and activate the transmission of data according to thestage of actions, that resolves difficulty in implementing thesubtasks and provide decision consequences in the form of theoutputs of simulation models or analytical functions.
In the decision support system framework designed, multi-agents are assigned to constantly monitor certain backgroundstatus, such as the contamination level, to decide whethera warning of agriculture incidence should be issued andprompt for users login. Spatial and non-spatial informationis supported according to the requirement of the subtasks ac-cording to the coded knowledge, via customized combinationof functions created on the MAKOCI platform.
V. CASE STUDY
We will demonstrate the requirement matching platform inthis section, followed by a decision management and supportsystem for remediation actions in response of heavy metal
Fig. 2. The web page for decision makers to post requirements on theMAKOCI platform
Fig. 3. The web page for developers to search and register data, model andapplications on the MAKOCI platform
contamination on farmlands, entitled Intelligent farmlandcontamination decision management and support system, toillustrate the communication and information support systemthat provides capability for hierarchical and cross-governmentcollaboration.
The requirement posting page is shown in Fig. 2. Thepage was designed to require only contact information of thedecision maker, and a brief description of the expected func-tions or outcomes that will help decision making. Optionally,categories or entries of the sources of data or models couldbe described to assist web-application developer in searchingrelated web-serviced data or simulation model. The require-ments are made visible to all qualified software developers,and functional requirements are negotiated between decisionmakers and application developers.
Fig. 4. The home page for the farmland contamination decision managementand support system. Titles of each text box indicated system features, usageand limitations, system developer, and knowledge provider
Fig. 5. The decision tree for the farmland contamination decision managementand support system, where yellow blocks indicate government units, andpurple blocks indicate officers in charge. In this example, two decision treesare involved: the decision tree for the Agriculture and Food Agency, and thedecision tree for Taichung City. The decision tree for Taichung City is shownin this figure.
Using the catalog service provided on the MAKOCI plat-form (Fig. 3), data and models could be included in thedevelopment of web application. All web applications areconstructed separately according to the theme of the decisionproblem. In this case study, the home page of the decisionsupport system is shown in Fig. 4, which consists of thetitle of the system, a login entry button, description of thecapability and limitation of the system, and info...