The alternative with the highest global weight is selected as the best one Islam and Abdullah, But the AHP has some inherent drawbacks: It requires a large number of pair-wise comparisons, especially in the presence of a large number of criteria, and the exhaustive pair-wise comparison is tedious and time consuming when there are many alternatives to be considered Hotman, ; Islam and Abdullah, ; Liebowitz, The aim of this study is to present a simple analysis approach called comprehensive entropy weight observability-controllability risk analysis CEWORA , based on information entropy theory IET and experimental psychology principles EPP.
A case study of water resource planning in the Yellow River Basin, China, was performed. In the rest of this paper, we first introduce the general concept of IET, then present the method of CEWORA and apply it to the decision of alternatives and measures in the case study. General concept of information entropy. The concept of entropy originates from thermodynamics and represents the heat energy that cannot be used to generate work. It is a ratio of the variation of heat to the variation of temperature.
In , Shannon and Weaver introduced the entropy concept into information theory and measured the amount of information with it.
Information entropy is a measure of the disorder in a system and can be used to measure the degree of disorder of unpredictable, unstructured and complex systems Mays et al. Information entropy has been applied extensively to the fields of engineering, society and economy. The concept of information entropy as defined by Shannon and Weaver is:.
Models for Identifying and Evaluating Alternatives | SpringerLink
The unit of information entropy varies with the logarithms' base: the unit of base 2 is bits, base 10 is decibels and base natural-logarithm e is napiers Amorocho and Espildora, The base 2 logarithm is considered in this study. According to Eq. However, measurements are usually discrete, representing data sets that are limited in time and space in the case of laboratory or field data Mays et al.
Under this condition, suppose that a system has n kinds of states; the discrete equation is given by:. Comprehensive entropy weight observability-controllability risk analysis. The approach is carried out based on the following steps:. Each alternative includes l indexes. The rth alternative with the j'th index has a value of k Then the evaluation entropy value of the j'th index is defined as:.
The evaluation entropy indicates the important degree of index. The smaller the value of e , the greater the information content provided by the j'th index Ding and Shi, The EW calculated by Eq. It results from the uncertainty of the observability-controllability objectives and results, and the limitation of capacity of executants. According to the observability-controllability model of periphery COMP presented by Li and Wei , a certain relationship exists between the system inner state and the system input and output.
In terms of the feedback information from the system, the controller could make corresponding responses and take some measures to control the system inputs, outputs and states, based on the changing relationship between the system and the environment, in order to promote the coordinated and stable de velopment of the system. This process continues until the termination condition or the system objective requirement is met. However, due to the insufficient information, the uncertainties of observability-controllability objectives and their background and the limitation of controller's capacity, the expected values may not be achieved, which will result in risk.
Thus the risk exists objectively between observability and controllability of the system: the higher the degree of observability-controllability of the system, the larger the amount of information, and the smaller the uncertainty and the risk, and vice versa. Even though the expected condition is the same, the risk varies with the observability-controllability controller, objective, background, mode and guidance basis.
Therefore, the observability-controllability risk has a statistical significance. It can be defined based on the dispersion degree of the stochastic variable in statistics, which is shown as follows:. The flow chart of the approach is shown in Fig. The greater the G is, the greater the R is. Under the permitted value range of risk, the alternative with the largest risk value has the greatest benefit in general.
Therefore, optimal decision results can be achieved based on the alternative with the greatest value of R oc or G bc under the permitted condition of risk. Statement of the problem. The river is the second-longest 5 km in China and its basin covers an area of km 2. The total average yield is about With the development of economy and society, the water requirements in the Yellow River Basin YRB have increased rapidly.
The Yellow River has one of the highest water resource exploitation intensities in the world. For example, the total amount of water consumption by industry, agriculture and domestic uses was The intensity of demand for water resources and the alteration of natural conditions have resulted in the occurrence of water resource problems in the YRB, such as water resource shortages, flooding, and deterioration of ecological function. The key factor leading to the problems mentioned above is the disharmony between the surface water-soil-environment system and the social-econom c system in YRB.
The exploitation and utilisation of water resources should be consistent with the natural laws of water resources.
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Holman addressed the direct and indirect impacts of climate change together with socio-economic changes on groundwater recharge. Scibek and Allen incorporated climate and groundwater models to evaluate the impacts of climate change on groundwater recharge and levels in an unconfined aquifer near Grand Forks in south central British Columbia, Canada. More recharge to this unconfined aquifer was found from spring to summer based on future climate projections. Hsu et al. Their regression results showed that groundwater levels would decrease decreased groundwater availability so that conflicts of water supply and demand would be aggravated.
Tapoglou et al.
Surface water and groundwater are inextricably linked; understanding of their interactions is essential for developing effective conjunctive water resources management strategies, especially for adaptation to future climate change Sophocleous, ; Allen et al. Some researchers have conducted studies related to surface-water and groundwater interactions under climate change. For example, Eckhardt and Ulbrich investigated the impacts of climate change on streamflow and groundwater recharge using a conceptual eco-hydrologic model based on a revised SWAT.
Scibek et al. Under future climate scenarios, differences of aquifer water levels would vary from less than 0. More studies can be found in Hatch et al. Interannual variability of climate such as temperature and precipitation is vital to assess the climate change impacts and develop corresponding adaptation strategies effectively Andersson et al. The variability between years can result in direct or indirect effects on hydrological, ecological, and biogeochemical processes Fatichi et al.
Since the research by Rind et al.
Most of his models showed a reduced variability of temperature in winter in the extratropical Northern Hemisphere and the high-latitude Southern Ocean, and a slight increase of temperature variability over land in low latitudes and northern mid-latitudes in summer. They also pointed out an increased interannual variability of precipitation in most areas, especially in the regions with a reduced mean precipitation. Consistent conclusions were drawn in the studies by Giorgi and Bi Their results further demonstrated the previous findings that interannual variability of precipitation would increase in all seasons, while interannual variability of temperature would increase in summer and decrease in winter.
Andersson et al. The Rossby Centre Regional Climate Model RCA3 was employed for future climate projections and a river basin hydrological model HBV was used for identification of hydrological responses to climate change. There would also be an increase of interannual variability of dry season streamflow. In assessment of interannual variability of precipitation, selecting the suitable correction methods such as scaling or bias correction methods is also crucial since it will significantly affect the associated uncertainty estimation Johnson and Sharma, This should be paid more attention to in future climate variability and impacts assessment studies.
Optimization models and methods are effective tools for allocating water resources and providing decision supports. A number of optimization management models have been proposed for conjunctive use of surface water and groundwater Sethi et al. These models are mainly for the purposes of cropping patterns planning and irrigation water management Singh, Azaiez and Hariga presented a single-period planning model for conjunctive use of surface water and groundwater for a multi-reservoir system, with stochastic inflow to the main reservoir and irrigation water demand.
Barlow et al. Tradeoffs between groundwater withdrawals and streamflow depletion were analyzed.
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Karamouz et al. Management objectives of minimization of irrigation water supply shortages and pumping costs, and control of average groundwater table fluctuations were considered. Rao et al. Syaukat and Fox presented an integrated surface water and groundwater management model to meet urban water demand in the Jakarta region, Indonesia. Khare et al.
Net benefits from cropping activities were maximized considering water demand and availability.
An increase of groundwater development was suggested to handle the surface water shortage problems. More recently, Cheng et al. Yang et al. The model integrated a multi-objective genetic algorithm, constrained differential dynamic programming, and groundwater simulation model named ISOQUAD into a general framework.
Montazar et al.
Application of their model to an agricultural water system in Iran demonstrated the feasibility of conjunctive use and effectiveness in enhancing the total benefits. Safavi et al. The method incorporated an artificial neural network to simulate the variations of groundwater levels, and then used a genetic algorithm to solve the simulation-based optimization model. Chang et al. Safavi and Esmikhani presented a simulation-optimization model for conjunctive use of surface water and groundwater in the Zayandehrood river basin in Iran. Surrogate models were developed by using support vector machines to replace surface water and groundwater simulation models in the optimization management model with the objective of minimizing water shortages for satisfying irrigation demands, subject to a series of water-related constraints such as controlling cumulative water-table drawdown and maximizing irrigation system's capacity.
Systems analysis methods are highly desirable to handle water use conflicts among different parts of water management systems Wu et al. The abovementioned optimization methods and models for conjunctive surface water and groundwater management didn't consider the impacts of future climate change. This lack hampered their applicability to generate effective water management strategies in future changing climatic conditions since climate change is inevitable. Recently, many researchers attempted to incorporate climate change impacts into the planning and management issues in conjunctive water use Hoekema and Sridhar, ; Pingale et al.
Simple statistical techniques were used to downscale the outputs such as precipitation rates from the GCM for providing the inputs for RGWM.