- freely available
Water 2014, 6(10), 3136-3151; https://doi.org/10.3390/w6103136
2. Architecture and Function Module
2.1. Generic Architecture
- GUI: To ensure that the technical tools of a GIS-based DSS are user-friendly, a graphical user interface (GUI) is used . The GUI is the front-end tool for data preprocessing and the visualization tool for analyzing the final results. It can assist with problem formulation, data input, changing driver and parameter values to specify their inputs, and it can provide tools for the analysis and visualization of the model outputs. It is easy to provide access to different policy options and external factors and to visualize model output and indicators. The GUI can directly communicate between the database, knowledge base, model base, and user.
- Database: The database of an RHD-DSS holds all required data for river health diagnosis. The database is composed of four sub-databases: a basic sub-database, a diagnostic indicator sub-database, a parameter sub-database, and a result sub-database. The basic sub-database involves topographic data (e.g., river setup, riparian characteristics, cross sections, floodplains), hydrologic data (e.g., precipitation, discharge), water quality data (e.g., dissolved oxygen, chemical oxygen demand, biochemical oxygen demand, total nitrogen, total phosphorus), and infrastructure data (e.g., levee, weir, dam). The diagnostic indicator sub-database contains status indicator data and cause indicator data. In the parameter sub-database, there are threshold values of each indicator, built-in weight coefficients, external weight coefficients, and comprehensive weight coefficients. What is more, the results of status assessment and cause diagnosis are stored in the result sub-database.
- Model base: The model base is used to store all necessary models capable of assessing health status, identify causal factors as well as support the process of decision-making. In terms of the models, analytic hierarchy process (AHP), order degree (OD), partial least square (PLS), value importance project (VIP), and the if-then-else rule are involved. They can be classified into three categories: status assessment, cause diagnosis, and decision-making; accordingly, these three types of models are stored in a status assessment sub-model base and lead to diagnosis sub-model base and decision making sub-model base, respectively. The model base can be communicated with the user, database, and knowledge base through the GUI.
- Knowledge base: The knowledge base is the base through which the human expertise and heuristic knowledge, including concept knowledge, expert knowledge, and inference knowledge, are efficiently stored and accessed. By coupling knowledge with models and tools, decision making for river management is facilitated. The knowledge base draws inferences from the data presented through the GUI, consults the model base, and assists river managers in selecting a series of suitable restoration options for a given location.
2.2. Functional Components
- User registration and initialization module. The user registration and initialization module can be used to create and allocate authority to different users. By means of the initialization tool, a user is allowed to select a specific river and view its related maps and information shown in the GUI.
- Database management module. The database management module enables the user to create a new database and append, save, delete, and browse the data using “New”, “Append”, “Save”, “Delete”, and “Browse” buttons, respectively, in the user-friendly dialog interface of Database Management. Additionally, the monitored, sampled or collected data at different spatial scales (e.g., reaches, sections, local regions, and watersheds) and temporal scales (e.g., yearly, seasonally or monthly) are entered and stored through this module.
- Indicator management module: This module makes it possible to identify and generate diagnostic indicator sets. Meanwhile, it is also equipped to calculate the values of indicators by extracting data from the database. This module has four functions: choosing status assessment indicators, choosing cause diagnosis indicators, calculating values of indicators, and browsing criteria of indicators.
- Model base management module: The models in the model base execute specialized operations, which include models for operational, strategic or tactic decision support. All models are run and controlled by the model base management module, which allows for browsing stored models, modifying models, and appending models.
- Knowledge management module: The task of the knowledge management module is to provide knowledge about river health and expert experience with river restoration. Moreover, in this module, there is an extensive port to allow users to append knowledge and expert experience.
- Assessment and diagnosis module: The aims of the assessment and diagnosis module are to assess the health status of a particular river system, analyze the main causal factors, and make reasonable decisions for river restoration and management. When the map, data, parameters, and models are pre-processed, this module allows a user to select a river, create indicator sets, assess health status, diagnose causal factors, and make restoration decisions. Additionally, this module makes it possible to communicate among database, model base, and knowledge base.
- Result visualization module. The results of a diagnosis may be viewed in the form of a thematic map, tables, or graphs through result visualization module. In particular, the software has a built-in feature of geospatial display of data and results using thematic maps in different colors. Additionally, map elements such as legend, scale, and compass can be selectively added to thematic maps.
3. Methodology and Approaches for Design and Implementation
3.1. GUI Design
3.2. Database Design
3.3. Model Base Design and Read
|Field Name||Data Type||Field Name||Data Type|
|Model ID||Integer||Model Name||String|
|Model Type||Integer||Model File Name||String|
3.4. Assessment and Diagnosis Procedure Design
|Step1: Input initial information||Initial investigated data, digital map, river type (e.g., mountain river, hilly river, and floodplain) and key service function (e.g., irrigation, drinking, hydropower, recreation, navigation, waterway) may be inputted and selected.|
|Step2: Set status assessment indicators||All or some of 13 built-in status assessment indicators (Table 4) may be set.|
|Step3: Assign weights||One of three optional weight assigning methods, namely, Built-in method, User-defined method, and Compound method, is exerted.|
|Step4: Run status assessment models||Order degree (OD) model is called from the model base and compounded with data and weights. Then health index (HI) may be obtained so that health grade can be determined by comparing HI with interval of health criteria.|
|Step5: Set causal diagnosis indicators||Any of 21 built-in impacting indicators may be checked on the tab dialogs of rainfall, pollution, water resource development, human occupation, hydraulic infrastructure, urbanization, and management activity.|
|Step6: Run causal diagnosis models||A partial least square regression (PLS) model is called from the model base, and along with appropriate data, a regression equation between HI and impacting factors can be derived. Then a value importance project (VIP) of each impacting factor can be obtained and the main causal factors identified.|
|Step7: Make restoration decision||According to the main causal factors and relevant expert knowledge, a restoration suggestion may be made.|
3.5. Map Management Design
4. Application of RHD-DSS
4.1. Study River
|Year||X1 (mm)||X2 (m3)||X3 (m3)||X4 (m3)||X5 (108 Yuan)||X6 (%)|
4.3. Results and Discussion
- The integrated system facilitates effective usage of spatially explicit data to evaluate health status, and analyze the most important causal factors, and help make restoration decisions at reach and regional segment scales.
- This decision support system contributes to river information technology by providing a novel, efficient, and cost-effective approach for comprehensively mapping river health; information that is not readily apparent due to the complex interaction of river characteristics and their relative contribution to its health.
- RHD-DSS also provides histograms that facilitate the study of the primary causes of river health decline.
- A simple user-friendly interface provides easy access to the components of the system by maintaining complex data and control transfer operations in the background.
Conflicts of Interest
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