International Workshop on Non-structural Measures for Water Management Problems Flood Control Management System for Reservoirs as Non-structural Measures By Cheng Chuntian Department of Civil Engineering, Dalian University of Technology Dalian,116024, P.R.China ctcheng@dlut.edu.cn K.W.Chau Department of Civil and Structural Engineering, Hong Kong Polytechnic University Hung Hom, Kowloon, Hong Kong Ou Chunping Intelligent Engineering Lab, Institute of Software, Chinese Academy of Sciences Box 8718, 100080, Beijing, P.R.China Abstract: Flood control operation of reservoirs can play a major role in alleviating flood losses. Flood control management system for reservoirs, an important non-structural measure, will benefit to make full use of the flood control capacity of the existing reservoir projects. The paper is a summary of the national programming about the flood control management system for reservoirs in China. The background, objectives, main challenges, contents and key technologies of the programming are introduced. The issues of the integration method and technology of the flood control management system are addressed. Emphasis is concentrated on object-oriented design of system, integration of models and methods, as well as database development. Keywords: flood control; integrated management; object-oriented; database; non-structural measure Introduction Flood disaster is one of the most damaging natural disasters in China, with annual average losses more than 200 billion yuan(1 US$ equal to 8.3 CNY$) in recent years. As major structural measures to defend against floods, more than total number of 86,000 reservoirs have been established in the past fifty years. These reservoirs have great role on mitigating flood losses together with other flood protection measures. A typical example is the flood control operation of Gezhouba Reservoir, Geheyan reservoir and Danjiangkou Reservoir for 1998 floods of the Yangtze River. The three large-scale reservoirs are at the main stem and tributaries of the upper and middle reaches of the Yangtze River. Their joint operation with other flood control projects have avoided the use of division flood regions and decreased the losses with up to more than tens of billion yuan for the 1998 floods. The flood control operation of the four large-scale reservoirs at the main stem of the Liaohe River for 1995 floods is another example. Dahuofang reservoir, Qinhe Reservoir, Chaihe Reservoir and Guanyinge Reservoir had reduced the losses by 14.3 billion yuan for 1995 floods with magnitude in more than 100 years return periods. After 1995 floods in the Liaohe River and 1998 floods in the Yangtze River, the governments from national to local have realized that the flood control operation of reservoirs can play a major role in alleviating flood losses but there are some problems in flood control management for 109 London, Canada, October 2001 reservoirs. Most of the existing flood control management systems for reservoir were established for special purposes and are lack of data share and communication with governments, it is very difficult to for decision- making departments to get real- time information in short time. In order to make full use of the flood control capacity of existing reservoir projects and to improve the national level of the flood control operation for reservoirs, the National Flood Control and Drought Defying Chief Headquarters of China has commissioned Dalian University of Technology (DUT), Hohai University, and Wuhan Hydroelectric University to develop an integrated management system for flood control of reservoirs (IMSFCR) since 1998, with a duration of five years. The objectives are to establish a standardized flood control software system of multi- reservoir, integrated real-time data acquisition and processing, precipitation analysis, flood forecasting, reservoir system analysis, information query, multi- media tutorial and some of the recent methodologies of flood control based on large scale database management system. This paper addresses the issues of the software integration of flood control management system for reservoirs. Emphasis is concentrated on object-oriented design of system, database development as well as integration of models and methods. The main challenges, structure framework and custom designed functions of IMSFCR are briefly described. Main Challenges The main challenges in developing IMSFCR lie in dealing with the complexity of typical systems, the interface integration and standardization of software system. It is because China is a country with a vast territory and there exist substantial differences in flood conditions determined by variations such as physical geography, hydrological and meteorological characteristics at different locations. Owing to these variations, methods employed in the reservoir flood control system are determined to a large extent by the purposes and the scope of the project. In addition, the data available has an effect on the choice of models. Hence, extensive model libraries will be established, which may take a lot of time and effort. Advances in computer technology have made it possible to simulate the flood control management processes in an integrated and comprehensive framework. The flood control management system for reservoirs involves directly with real-time data acquisition and processing, precipitation analysis, flood forecasting, reservoir system analysis, information query, as well as multi- media tutorial. Large-scale database management system (DBMS) is the basis of the integrated system. A distributed model requires the analyst to acquire, maintain, and extensively utilize a referenced database. It is greatly different from the traditional text files systems. Database sources including input, output, calculation, query and temporary procedure should be used in all operations. Another question, which arises here, is whether it is necessary to replace all the existing software. A lot of mathematical models, including professional models and general algorithms, are coded with a variety of programming languages such as Fortran, C++, Powerbuilder, and so on. The user interface of the old software may be under traditional database system or in previous versions. The choice in reusing or rewriting them under the new environment, mainly depends on the quantity, complexity and quality of the existing software as well as on the availability of time and resources. Furthermore, the programs must meet a minimum standard of quality as far as reliability, efficiency and maintainability are concerned. We chose to reengineer and redevelop all existing systems in order to satisfy the common rules and to meet the requirements of reliability, efficiency and maintainability as far as possible. To speed up the development procedure, the functions of old systems are refereed and made some translation from one language to another. 110 International Workshop on Non-structural Measures for Water Management Problems Selection of prototype system In China, there are more than 5,000 rivers with area over 100km2 . There are about 86,000 reservoirs, and among them, more than 3,000 have storage capacity over 100 million cubic meters. These reservoirs are classified as medium to large types that are the essential objects of development for the national software project of reservoir flood control. 20 reservoirs were chosen as prototype systems in the first trial project from the whole country in 1998. 40 reservoirs were chosen in the second batch in 1999 while 60 reservoirs were selected in the third batch in 2000. Among them, the developing group s of DUT chose 10 reservoirs as prototype systems in 1998, 14 ones in 1999, and 20 ones in 2000. From these prototype systems, we aim to find the common features and to distinguish the differences among them. Furthermore, we are planning to develop a general software system of reservoir flood control. On the other hand, the calibration and validation of these common rules are also one of main objectives. Structural framework of system IMSFCR adopts client/server structure based on large-scale database. The databases are divided into network databases and special databases. Network databases consist of real- time data library, history records library and results library. Special databases include flood forecast library and flood operation library. Network databases are shared resources involving original records and public information. On the other hand, special databases are kept private to facilitate simulation and analysis of flood forecasting and flood control operation by both technical and non-technical personnel before formal or official results have been generated. Most data are temporary in nature and are only valid in user’s machines. For non-technical users, it is especially important that they can operate and learn from the system, without the threat that their inadvertent action may erase some important and raw data. For technical users, they can simulate any alternatives in an efficient way. This layout of databases enhances data security as well as improves flexibility of the system application. The structural framework of the reservoir flood control system is shown in Figure 1. Custom designed functions The reservoir flood control system includes six custom designed functions, i.e, “information query”, “data processing”, “flood forecast”, “flood operation”, “result post-processing” and “help”. Figure 2 shows the main function tree of the reservoir flood control system. “Information query” provides several basic information inquiry for brief introduction of reservoirs, dynamic real-time flood situation, flood forecasting results, flood operation alternative results, historical records, operation rules, law and policy related to flood control operation, and so on. Query can be activated through access to the databases via internet/intranet. The query results may be shown in various formats such as tables, graphic, maps, videos, and text. 111 London, Canada, October 2001 Reservoir flood operation system Forecast Operation Post-processing Database Operatio Historica Network Real- Special Database Help Results Preprocessing Forecast Information Figure 1. Structural framework of the reservoir flood control operation system Flood Control System of Reservoirs Information Preprocessing Forecast Operation Introduction Data input Model choice Real-time Data processing Initial condition Historical Real-time monitoring Real-time forecast Simulation forecast Revised forecast Historical analysis Parameter calibration Initial condition Automatic manner Interactive manner Evaluation Policy Rules Postprocessing Results project Graphic result Table result Report forms Selection Figure 2. Main function tree of the system 112 Help About system Multimedia International Workshop on Non-structural Measures for Water Management Problems “Data preprocessing” provides the data input and modification requirements on historical records, real- time flood data processing, and real- time flood monitoring. Large amounts of historical flood records are very valuable to flood forecast and flood control operations. Since data input is a time consuming task, various choices are allowed in the manner of input, such as electronic formats developed for special tables, database translation connections from a database into another, and special file formats similar to “excel”. General index and guidance are established to facilitate the data input. All imported data, such as historical records, historical files, and model parameters, can be reviewed and checked through “information query”. Constrained by the time interval stipulated in the flood forecast model, some of the original data about rainfall, level and discharge must be processed in order to satisfy the requirements of the real-time system. In addition, employing the real- time monitoring of flood event, the user can dynamically perceive rainfall spatial- temporal distribution of the whole basin as well as level process of some control sections. When alarming accumulated rainfalls or water levels arise, the system will prompt the user to perform flood forecast and flood control operations. “Flood forecast” includes the choice of flood forecasting models, initial condition set and modification on antecedent soil moisture, real-time forecast, simulation forecast, revised forecast, historical flood analysis, and model parameter calibration and validation. The system has integrated most of the commonly used flood forecasting models in China, such as the famous Xinanjiang model developed by Zhao(1992), typical rainfall-runoff models, unit hydrograph model(Linsley et al.,1975; Hoggan, 1997).When a model is chosen, the system will set it as the default in the next time. Flood forecast is classified into “real-time” and “simulation” depending on the situation when real- time and simulation data are used respectively. Forecast results are dynamically analyzed and shown with tables and graphics. Some characteristic values of the flood , including total rainfall, total runoff, pure precipitation rate, peak inflow, peak time, the largest flood inflow volume , occurrence time and corresponding frequency during the given time interval, are displayed. When there is a large discrepancy between real data and prediction, revised methods can be evoked. Historical flood analysis can be performed from the similar historical floods based on pattern recognition. With the accumulation of more hydrological data, the user can recalibrate and revalidate the model parameters, and choose another appropriate model. “Flood control operation” can deal with both single reservoir and multi-reservoirs system. Reservoir flood control operation is operated in real time, which often differs very much from other operations for planning purposes. The crucial difference between them is that decision making of flood control is usually effective only for the current period or for the following periods. Constrained by the updating forecasting inflow information at the current period, decisions need to be made on a daily or even hourly basis during flood events. Multi-criteria decision analysis has been shifting from optimization methods to more interactive decision support tools (Bender and Simonovic, 2000). This system unitizes the recent fuzzy optimal model for the flood control system developed by Cheng (1999) and Cheng and Chau( 2001). The main features are the ability to quickly generate, select and evaluate alternatives and the flexibility of these models to allow for the transient change of practical flood conditions and to mimic the intuitions and experience of operators. “Automatically generated alternatives” uses knowledge-based rules gleaned from flood control authorities (experts) and from analyzing a mass of recorded historical data (Cheng and Chau, 2001). After these automatically generated alternatives are evaluated, “interactively produced alternatives” may be activated to produce more alternatives. Through fuzzy optimal model, one of the satisfied solutions is obtained. 113 London, Canada, October 2001 “Results post-processing” completes the task management for real- time information, flood forecast, flood control operation, and report outputs. Tables and graphics are direct and distinct demonstrations for problem and are easy to be understood by users and decision- makers. The numerous tables and graphics are designed for the special tasks and can be printed as report outputs. In addition, the formal results of flood forecast and flood control operation are only accessible by the authorized users and institutions. “Help” provides support and guide for users. The user can learn to use the system through multimedia tutorials. Database development The database system is the basis of IMSFCR. Sybase and SQLServer are adopted as the DBMS of reservoir flood control system. The two DBMS can ensure high data integrity, recovery, and concurrency control. They support the high- level query language SQL and enable users to perform sophisticated data retrievals. Most of the structural definitions of Sybase about data properties is the same as those of SQLServer. One of the two DBMS can be chosen according to the scale of the application system and the economic condition of the user. It is not necessary to modify the programming source codes. The design of the relational tables has a significant effect on the programming source codes and the operation efficiency of the flood control system. The software system based on the database is completely different from traditional files such as in HEC1-HEC5 packages(Hoggan, 1997). All preprocessing, calculation, query and post-processing are based on database and necessitates the access to the data in the related tables. An optimized database design can render the system easier to expand and minimize adjustment to the programming source codes, as well as improve the efficiency. The main works to achieve database optimization are to define the types of data queries and requests, and to normalize the database relationships. The optimization products are table structures where the table names, the name and data types of its fields, and the integrity constraints are defined. More than 400 tables have been designed for the reservoir flood control system. These tables form parts of the common rules for the national flood control system. The database can be utilized to generate all the standard reports required by the user. Another major objective is to provide the public with flood control information. The increasing use of the Internet makes the World Wide Web an attractive vehicle for the dissemination of such information. The flood control information about reservoirs can be accessed using Internet/Intranet. Design of object-oriented system During recent years, object-oriented framework has become popular technology as effected by object-oriented software development(Whitten et al., 2001). The technology has been applied to water resources system development (Beclkhouche et al., 1999), as well as in other fields (Cheng, 2000; Mattsson and Bosch, 2000). Object-oriented technology bears the promise of reduced development effort through large-scale reuse and increased quality (Moser and Nierstrasz, 1996). For the national flood control system, reliability, efficiency, maintainability and reuse are the important objectives of the normalized and standardized flood control system for reservoirs. The fourth generation languages (4GL), such as PowerBuilder 6.0, VC6.0 and VB6.0, are object-oriented software development and are easier to be used. Most of components in IMSFCR are developed by using PowerBuilder 6.0, some by VC6.0 and integrated by ActiveX. 114 International Workshop on Non-structural Measures for Water Management Problems Good quality object-oriented software system depends on the workflow analysis. The detailed design of object involves layout of conceptual design, logical design, physical design and implementation (Bekhouche et al, 2000). In other words, the whole system will be divided into a mass of single objects. The attributes and behaviors of each object will be analyzed and defined. A series of object-oriented models capturing attributes and methods of objects are established based on the analysis of objects. The whole system is in fact the implementation of objectoriented models based on the logical and physical relationships of objects. Figure 2 is the main function tree of the system, which also shows the logical and physical relationships of main objects. The main interface of flood control system for reservoirs is an integrated object consists of six integrated objects of second level function components. Each object of second level function components is also an integrated object consists of lower level objects. The lowest level component is a series of single object-oriented models. Figure 3 shows the framework of objectoriented models. In Figure 3, rectangular boxes represent integrated objects; rectangular boxes with shadow represent single objects. Adopting these object-oriented models, the interrelations that exist among these components are established, specifying the behavior of the entire structure and each component. The object-oriented framework technology has the following appealing traits: (1) easy development by team groups thus speeding up the development, (2) large-scale reuse because of relatively independent encapsulation of the object, (3) improved quality such as reliability, efficiency and maintainability because of high portability. Integration of mathematical models and methods The two essential components are flood forecast and flood control operation. Numerous models related to these two parts are integrated into this system. Most of the common methods and models are coded with general functions through conversion from previously developed software as far as possible. Only small proportions of them are developed recently under the new environment. Interactive contents related to these functions such as initial conditions are provided and executed through graphic user interface. Most of the mathematical models and methods are embedded in methods of object through the call for mathematical model or method functions. The forecasting component consists of precipitation-runoff model libraries and flood routing libraries. The precipitation-runoff model libraries have integrated five models up to now: rainfall-runoff relationships (including P+Pa~R and P~Pa~R, flow forecast using unit graph model), Xinanjiang model (Zhao,1992), Dahuofang Model( Liu 1985). The flood routing libraries provide for Muskingum methods (Muskingum and Muskingum-Cunge), coefficients methods and direct additional method after consideration of the time lag. Real-time revised forecast results are useful in improving the forecasting quality. An effective method for all cases is still under development because the flow routing time is very short for most reservoirs. Two particular methods and technologies are used in this system. The first method is dynamically adjusting the model parameter through calibration and validation of model from those classified floods based on pattern recognition or from recent floods. Figure 4 represents a case application of the real-time revised forecast results through the above methods. Another method is interactively modifying the forecasting results by hand with reference to those similar historical floods. These two methods are effective for some reservoirs only. 115 London, Canada, October 2001 First Level Object Main interface of system Second Level Object Component 1 Second Level Object Component 2 Second Level Object …… Second Level Object Component n2-1 Nth Level Object Component 1 Nth Level Object …… Nth Level Object Component nn Object 1 Object 2 Object … Object m-1 Second Level Object Component n2 Object m Figure 3 Framework of object-oriented models Calibration and validation of model parameters are also a time consuming process. A new technology, i.e., genetic algorithms, is developed to automatically calibrate and validate model parameters. In recent years, genetic algorithms have become one of the most widely used techniques for solving a number of hydrology and water resources problems (Wang, 1991; Ritzel et al, 1994; McKinney and Lin, 1994; Dandy et al, 1994; Cieniawski et al, 1995; Franchini and Galeati, 1997;See and Openshaw, 1999; Wardlaw and Sharif, 1999) because of their advantages over classical optimization methods. They have been used successfully as an optimization tool in runoff model calibration (Wang,1991; Franchini and Galeati, 1997). A new genetic algorithm, which combines a fuzzy optimal model with a genetic algorithm, is developed to solve the multiple objective rainfall-runoff model parameters calibration problem (Cheng et al.,2001). The parameter calibration includes optimization of multiple objectives: (1) peak discharge, (2) peak time and (3) total runoff volume.. Furthermore, the graphic user interfaces related to the genetic algorithm are designed and developed. Users can easily set the ranges of different parameters, choose real and binary codes, and decide the approaches of crossover and mutation. 116 Observed 1500 Common method Clustering 1000 Recent floods 0 500 discharge(m3/s) 2000 International Workshop on Non-structural Measures for Water Management Problems 2000-6-10 08 2000-6-12 05 2000-6-14 02 2000-6-15 23 2000-6-17 20 2000-6-19 17 time Figure 4. A case study about the real-time revised forecast After entering the initial conditions and evoking the “calibrate” button, the model parameters will be automatically calibrated. The results will be shown in both graphics and tables. When a group of satisfied parameters is available, choosing the “replace” button will update the old parameters of the model. Figure 5 and Figure 6 show the part results of a case study about the parameter calibration and validation based on our genetic algorithm. 500 1000 1500 2000 2500 Observed Simulated 0 discharge (m3/s) 3000 3500 As mentioned above, flood control is operated in real-time. The decision making process is very complex and related to all the parties interested in the problem under consideration. Traditional techniques often simplify the problem by transforming the multi-objectives into a singleobjective, which are difficult to be related to intuitions and experience of operators. 1041 1081 1121 1161 routing period (3hr) 1201 1241 Figure 5 The simulated and observed hydrographs during calibration 117 1281 3000 Observed 1000 2000 Simulated 0 discharge (m3/s) 4000 London, Canada, October 2001       routing period (3hr) Figure 6 The simulated and observed hydrographs during validation Fuzzy optimal models are easy to deal with the complexity of a typical system with uncertainty and to mimic intuitions and experience of operators (Cheng, 1999; Cheng and Chau, 2001). Similar works can be found in Russell and Campbell (1996), Fontane et al.(1997), Despic and Simonovic(2000), and Bender and Simonovic(2000). These models (Cheng, 1999; Cheng and Chau, 2001) have been integrated into this system. When a new flooding event is imported, the system will automatically generate a series of alternatives based on the rules in the knowledge base and gives the evaluation of various alternatives (Cheng and Chau, 2001). Furthermore, users can interactively propose new alternatives and evaluate all alternatives. Finally, one alternative can be selected according to the evaluation. Conclusion The flood control management system for reservoirs has a significant role in alleviating flood losses. The establishment of IMSFCR will speed up the development of the national flood control system for reservoirs, make full use of the flood control capacity of existing reservoir projects and improve the national standard of the flood control operation for reservoirs. Largescale database is the basis of IMFCR. IMFCR integrates descriptive knowledge (e.g., data and information), the procedure knowledge (methods and algorithms) and reasoning knowledge (rules), with robust and effective management capacity. Acknowledgments This research was supported by “The Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of Ministry of Education, P.R.C” (No.200026), “Fok Ying Tung Education Foundation, P.R.C”(No.71072), the National Natural Science Foundation of China (No. 60073037), and the Research Grants Council of Hong Kong (PolyU5084/97E). 118 International Workshop on Non-structural Measures for Water Management Problems References B.Beclkhouche, I.Demtchouk, and L.J.Steinberg(1999). “Design of object-oriented water quality software system”. Journal of Water Resources Planning and Management, 125(5), 289-296 Brian J. Ritzel, J.Wayland Eheart, and S. Ranjithan(1994). “Using genetic algorithms to solve a multiple objective groundwater pollution containment problem”. Water Resources Research, 30(5), 1589-1603. Cheng Chuntian(1999). “Fuzzy optimal model for the flood control system of upper and middle reaches of the Yangtze River”. Hydrological Sciences Journal, 44(4),573~582 Cheng Chuntian(1999). “Fuzzy optimal decisions for multi-objective flood control system of cascade reservoirs”. Journal of Hydrodynamics ,Series B, 11(3), 6~13 Cheng Chuntian, K.W.Chau(2001). “Fuzzy iteration methodology for reservoir flood control operation”, Journal of the American Water Resources Association, 37(5), 1381~1388 Cheng Chuntian, Ou Chunping, K.W.Chau(2001). "Combining a fuzzy optimal model with a genetic algorithm to solve multiobjective rainfall-runoff model calibration", Jour nal of Hydrology (in review) Daene C. McKinney, and Min-Der Lin(1994). “Genetic algorithm solution of groundwater management models”. Water Resources Research. 30(6), 1897-1906 Daniel H.Hoggan(1997). “Floodplain hydrology and hydraulics”. The McGraw-Hill Companies, Inc. E.C.Cheng(2000). “A object-oriented organization model to support dynamic role-based access control in electric commerce”. Decision Support Systems, 29, 357~369 Graeme C. Dandy, Angus R. Simpson, and Laurence J. Murphy(1996). “An improved genetic algorithm for pipe network optimization”. Water Resources Research, 32(2), 449-458. Linda See, and Stan Openshaw(1999). “Applying soft computing approaches to river level forecasting”. Hydrological Science Journal. 44(5), 763-778. Liu(1985). “Dahuofeng rainfall-runoff model ”. The selected papers on hydrological forecasting conference. The hydroelectric publisher of China, Beijing, China, 35~48 Jeffrey L.Whitten, Lonnie D.Bentley and Kevin C.Dittman(2001). System analysis and design methods. McGraw-Hill Companies, Inc. Ozren Despic, S.P.Simonovic(2000). “Aggregation operators for soft decision making in water resources”. Fuzzy Sets and Systems, 115, 11~33 Q. J. Wang(1991). “The genetic algorithm and its application to calibrating conceptua l rainfallrunoff models ”. Water Resources Research. 27(9), 2467-2471. 119 London, Canada, October 2001 Ray K.Linsley,JR., M.A.Kohler and J.L.H.Paulhus(1975). “Applied Hydrology”. Tata McGrawHill Publishing Company Limited, New Delphi-110015 Robin Wardlaw, and Mohd Sharif(1999). “Evaluation of genetic algorithms for optimal reservoir system operation”. Journal of Water Resources Planning and Management, 125(1), 25-33 Marco Franchini, and Giorgio Galeati(1997). “Comparing several genetic algorithm schemes for the calibration of conceptual rainfall-runoff models ”. Hydrological Science Journal. 42(3), 357378. Michael J.Bender, S.P.Simonovic(2000). “A fuzzy compromise approach to water resource systems planning under uncertainty”. Fuzzy Sets and Systems, 115, 35~44 M.Mattsson, and Jan Bosch(2000). “Stability assessment of evolving industrial object-oriented frameworks”. Journal of Software Maintenance: Research and Practice, 12, 79~102 Moser S, Nierstrasz O(1996). “The effect of object-oriented frameworks on developer productivity”. IEEE computer, 29(9), 45~51 Scott E. Cieniawski, J. Wayland Eheart, and S. Ranjithan(1995). “Using genetic algorithms to solve a multiobjective groundwater monitoring problem”. Water Resources Research. 31(2), 399-409. Zhao R.J(1992). “The Xinanjiang model applied in China”. Journal of Hydrology, 135, 371~381 120