Top PDF Reservoir water release decision modelling

Reservoir water release decision modelling

Reservoir water release decision modelling

The sliding window technique has been successfully employed on reservoir water release data, to extract the changes of the reservoir water level that lead to the[r]

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Modeling reservoir water release decision using adaptive neuro fuzzy inference system

Modeling reservoir water release decision using adaptive neuro fuzzy inference system

et al., 2015). However, this technique suffers from poor interpretability, since it is difficult for humans to explain the practicality and logical meaning behind the learned weights of the model (Jothiprakash & Kote, 2011; Kajornrit et al., 2013). This problem can be solved by the Adaptive Neuro Fuzzy Inference System (ANFIS). ANFIS is a hybrid intelligent system which combines the fuzzy processing of Fuzzy Logic (FL) and the learning capability of ANN. The next section presents several related studies on ANFIS and its application on modeling reservoir operations, followed by the methodology, results and discussion. The last section presents the paper’s conclusions.
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Derivation of Multipurpose Single Reservoir Release Policies with Fuzzy Constraints

Derivation of Multipurpose Single Reservoir Release Policies with Fuzzy Constraints

not representative, modeling inaccuracy: parameter as- sumptions, randomness of natural phenomena, climate change, extreme events, operational variability, future socio-economics objectives, maintenance. Reservoir sto- rages are uncertain due to variation in inflows and some time vague due to poor operation. Stochastic crop net irrigation requirement (NIR) contributes uncertainty in irrigation demands. NIR is influenced by temperature, precipitation and the rate of crop development. Further irrigation demands, although driven by weather condi- tions to a large extent, are also impacted by crop type, market conditions, period of planting and harvest. Re- lease and storage targets for a reservoir operation are usually decided based on factors defining the functional requirement of the reservoir systems. These include, for example, downstream water demand; flood control re- quirement; expected damage; political and social impacts of reservoir operation. The decision maker considers all these issues, and applies personal judgment to decide on the target value, and thus the target becomes practically subjective in nature. Uncertainty also occurs from im- precise knowledge of current or future demands placed on the system. For example, reservoirs that are used to generate peaking hydroelectric power are subject to the whims of electricity pricing, consumer demand. Uncer- tainty is involved in objectives in the sense that the val- ues and targets are usually subjective, and the relative emphases on different objectives change with time. In many cases, fuzzy logic may provide the most appropri- ate methodological tool for modeling reservoir operation. In this study, the applicability of the reservoir operation model is improved by incorporating the uncertainties in model parameters and representing those as fuzzy sets instead of single values. The degree of satisfaction    of a certain value of the parameter within the fuzzy set is represented by a membership function. In present study while modeling of reservoir operation with fuzzy logic, the following steps are followed, i.e. fuzzification of in- puts, where the crisp parameters such as reservoir stor- ages, releases, irrigation demands, power demands and storages of the reservoir are transformed into fuzzy pa- rameters. In present section, three FLP models are pre- sented which considers uncertainty in various parameters gradually. In model I, the technological coefficients are taken as a crisp numbers while the resources are charac- terized by uncertainties. In model II, the technological coefficients are fuzzy numbers and resources are crisp in nature. In model III, FLP formulation considers both technological coefficients and resources are character- ized by uncertainty. In this section, formulation of these three FLP models is explained.
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Modelling Reservoir Water Release Decision Using Temporal Data Mining and Neural Network

Modelling Reservoir Water Release Decision Using Temporal Data Mining and Neural Network

Abstract—During emergencies such as flood and drought seasons, reservoir acts as a defence mechanism to reduce the risk of flooding and maintaining water supply. During this period, decision regarding the water release is very crucial. During flood season, early water release decision should be established to prepare the reservoir for incoming in-flow. While during drought season, reservoir water level should be maintained in order to sustain the supply and other usages. Reservoir operation during these two seasons cause conflicting decision as incoming inflow is hardly predicted. Modeling the reservoir water release decision can be one of the solutions to this problem. The modeling is based on reservoir’s operator previous experiences when dealing with such situations. These experiences provide valuable information on the decision when the reservoir water should be released. Temporal data mining technique has been applied to extract temporal pattern from the reservoir operational record and neural network has been applied as the modeling tool. The neural network model was developed to classify the data that in turn can be used to aid the reservoir water release decision. In this study neural network model 8-23-2 has produced the acceptable performance during training, validation and testing.
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Integrated hydrological modelling of small  and medium sized water storages with application to the upper Fengman Reservoir Basin of China

Integrated hydrological modelling of small and medium sized water storages with application to the upper Fengman Reservoir Basin of China

the regulation and utilisation of water resources (Gross and Moglen, 2007; Lopez-Moreno et al., 2009) on one hand; on the other hand, they represent a major type of human inter- ferences to hydrologic processes. Different approaches have been developed to account for the interferences in hydro- logical simulations. For example, water storage simulation models have been developed for small-sized river catchments (Jayatilaka et al., 2003; Saxton and Willey, 2004), which sug- gest that those models could be used as a useful tool for op- timizing the usage of limited water resources in similar re- gions with a small amount of water storage. However, the impact of water storage on hydrological processes has not been well addressed in general, largely due to the fact that the popular watershed simulation models lack sufficient data and relationships to simulate the effects of water storages. To address this issue, this paper presents an improved version of SWAT2005 (Soil and Water Assessment Tool, version 2005) using Landsat, a satellite-based dataset, an empirical storage classification method and some empirical relationships to es- timate water storage and release from the various sizes of flow detention and regulation facilities.
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Flooded with water deficits : optimal timing of an environmental flow release, using flow forecasts in the operational management of a reservoir

Flooded with water deficits : optimal timing of an environmental flow release, using flow forecasts in the operational management of a reservoir

Flow forecasting can be developed with three different procedures: (1) recorded rainfall in the basin in combination with upstream water level observations; (2) recorded rainfall used with a rainfall-runoff model; or (3) rainfall prediction by a weather model, together with a rainfall- runoff model (Carlos et al., 2006 citing Anderson et al., 2002; Koussis et al., 2003; Collischonn et al., 2005). The first two procedures have been used during the last 50 years, based on simple conceptual or stochastic modelling of hydrological variables. The third procedure may extend the lead time of a flow forecast longer than the response time to rainfall within a catchment. Other models to predict a river flow do exist, but they are more based on available historic data than on the simulation of the natural processes. Examples are artificial neural networks or autoregressive methods, that base their flow forecast on large amounts of data for the specific case. The correct use of these data can provide predictions of upcoming flow. If extra historical information or information about actual water levels or rainfall is added, the accuracy is increased.
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Forecasting model for the change in stage of reservoir water level

Forecasting model for the change in stage of reservoir water level

Reservoir is one of major structural approaches for flood mitigation. During floods, early reservoir water release is one of the actions taken by the reservoir operator to accommodate incoming heavy rainfall. Late water release might give negative effect to the reservoir structure and cause flood at downstream area. However, current rainfall may not directly influence the change of reservoir water level. The delay may occur as the streamflow that carries the water might take some time to reach the reservoir. This study is aimed to develop a forecasting model for the change in stage of reservoir water level. The model considers the changes of reservoir water level and its stage as the input and the future change in stage of reservoir water level as the output. In this study, the Timah Tasoh reservoir operational data was obtained from the Perlis Department of Irrigation and Drainage (DID). The reservoir water level was categorised into stages based on DID manual. A modified sliding window algorithm has been deployed to segment the data into temporal patterns. Based on the patterns, three models were developed: the reservoir water level model, the change of reservoir water level and stage of reservoir water level model, and the combination of the change of reservoir water level and stage of reservoir water level model. All models were simulated using neural network and their performances were compared using on mean square error (MSE) and percentage of correctness. The result shows that the change of reservoir water level and stage of reservoir water model produces the lowest MSE and the highest percentage of correctness when compared to the other two models. The findings also show that a delay of two previous days has affected the change in stage of reservoir water level. The model can be applied to support early reservoir water release decision making. Thus, reduce the impact of flood at the downstream area.
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Modelling of human expert decision making in reservoir operation

Modelling of human expert decision making in reservoir operation

In this study, a total of 3041 daily data from Jan 1999 – April 2007 were retrieved from the Timah Tasoh reservoir operation log book. Operation of Timah Tasoh reservoir was influenced by upstream rainfall which was manually recorded through 5 upstream gauging stations. Rainfall observed from these stations will eventually increase the reservoir water level. In this study the current water level (t), tomorrow water level (t+1), and the changes of water level at t, t-1, …, t-w were used as the input data or the premises, while the gate opening/closing at t is used as the target or the expected outcome. The constant t and w represent time and days of delays (which later represented as window size).
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Intelligent Decision Support Model Based on Neural Network to Support Reservoir Water Release Decision

Intelligent Decision Support Model Based on Neural Network to Support Reservoir Water Release Decision

Abstract. Reservoir is one of the emergency environments that required fast an accurate decision to reduce flood risk during heavy rainfall and contain water during less rainfall. Typically, during heavy rainfall, the water level increase very fast, thus decision of the water release is timely and crucial task. In this paper, intelligent decision support model based on neural network (NN) is pro- posed. The proposed model consists of situation assessment, forecasting and decision models. Situation assessment utilized temporal data mining technique to extract relevant data and attribute from the reservoir operation record. The forecasting model utilize NN to perform forecasting of the reservoir water level, while in the decision model, NN is applied to perform classification of the cur- rent and changes of reservoir water level. The simulations have shown that the performances of NN for both forecasting and decision models are acceptably good.
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Reservoir water release dynamic decision model based on spatial temporal pattern

Reservoir water release dynamic decision model based on spatial temporal pattern

Beach, L.R, & Mitchell, T.R. (1978). A Contingency Model for the Selection of Decision Strategies. The Academy of Management Review, Vol. 3, No. 3 (Jul., 1978), pp. 439-449, Retrieved from: http://www.jstor.org/stable/257535 Becerra-Fernandez, I., Xia, W., Gudi, A., & Rocha, J. (2008, May). Task characteristics, knowledge sharing and integration, and emergency management performance: research agenda and challenges. In Proceedings of

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RESERVOIR CFD MODELLING WHAT ARE THE BENEFITS OF USING A 3D MODEL?

RESERVOIR CFD MODELLING WHAT ARE THE BENEFITS OF USING A 3D MODEL?

In terms of option comparison both the 2D and 3D model gave similar results. In both cases the output of the modelling has shown that the installation of baffles minimises ‘dead areas’ and reduces the mean water age in the reservoir. However a detailed comparison of the model results shows differences bet ween the two simulations. At this stage only the velocity results from the two models were compared. Although the 2D model can simulate constituent fate and transport to estimate the age of water this was not undertaken for this study. The 2D and 3D model runs gave very similar results for the scenario without baffle in terms of velocity range. The two models show that the long strip located on the eastern side of the reservoir presents significant velocities, especially around the inlet and outlet pipes whereas the main chamber of the reservoir shows lo w velocities. The 3D model predicts the apparition of circular streamlines at the inlet of the reservoir and in the main chamber. The 2D model shows some turbulence in the streamlines at the inlet of the reservoir but the mixing effect in the main chamber is not depicted.
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The effect of desiccation on water management and compartmentalisation in scorpions: the hepatopancreas as a water reservoir

The effect of desiccation on water management and compartmentalisation in scorpions: the hepatopancreas as a water reservoir

2003). When pooling total body and hepatopancreas water contents of the two studied scorpion families (Fig.·5), it seems that they adopt different strategies in avoiding desiccation. Scorpionids exhibit a rapid depletion of body water stores, which reflects their higher WLR in comparison with those of buthids (Gefen and Ar, 2004), but appear to store more bulk water in their bodies when fully hydrated (Fig.·5). The changes in hepatopancreas lipid content during desiccation also appear to be phylogenetically related, with only the two Scorpionidae exhibiting a significant increase in lipid fraction following desiccation (Table·3). Glycogen water binding capacity is estimated to be 3–5 times its own mass (Schmidt-Nielsen, 1997). However, glycogen-bound water is only available to the organism as glycogen is catabolised. Therefore, high WLR may necessitate glycogen catabolism not only for meeting energetic needs and production of metabolic water, but also for making bulk water available for maintaining homeostasis during prolonged desiccation. This could be accompanied by lipogenesis from a carbohydrate source, as has been suggested for several Drosophila species exposed to desiccating conditions (Marron et al., 2003).
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Is the groundwater reservoir linear? Learning from data in hydrological modelling

Is the groundwater reservoir linear? Learning from data in hydrological modelling

The paper contains the results of the tests of this methodol- ogy on several catchments in Luxembourg, representative of the variability of conditions of this area. With respect to the parameterization of the reservoir that simulates the slow hy- drograph component, we determined that in general a linear model can well represented well the groundwater behaviour of the catchments. The non-linearity observed in the storage- discharge relation derived from data alone, appeared to be explained as a bias produced from groundwater recharge. This result is obviously influenced by the specific test con- ditions, including model structure, calibration methodology, data quality and availability, as well as all the subjective as- sessments made by the user (e.g. choice of a specific model to represent the groundwater behaviour). Hence, further re- search is needed to allow a more reliable quantification of the impact of groundwater recharge on flow recession.
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Modelling foam improved oil recovery within a heterogeneous reservoir

Modelling foam improved oil recovery within a heterogeneous reservoir

The displacement of foam within a heterogeneous reservoir during foam improved oil recovery is described with the pressure-driven growth model. The pressure-driven growth model has previously been used to study foam motion for homogeneous cases. Here the foam model is modified in such a way that it includes terms for variable permeability. This model gives the evolution of the foam motion over time and the shape of the foam front, a wet foam zone between liquid-filled and gas-filled zones. The foam front shape for a heterogeneous or stratified reservoir develops concave and convex regions. For shapes such as these, the numerical solution of pressure- driven growth requires special numerical techniques, particularly in the case where concavities arise. We also present some analysis of the level of heterogeneity and how it affects the displacement, the shape of the front developing a set of concave corners. In addition to this we consider a heterogeneous and isotropic reservoir, in which case the foam front can sustain concavities, without these concavities having the same tendency to develop into corners.
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Computational Modelling of Fuel Motion and Its Interaction with the Reservoir Structure

Computational Modelling of Fuel Motion and Its Interaction with the Reservoir Structure

One of the vehicle components where two physi- cal systems interact is the fuel-tank. In previous simulations the model of the tank was very simplified, since the fluid motion’s influence on the tank’s behav- iour was not considered. Only the fuel mass was taken into account, which was distributed at discrete nodes along the tank walls to account for the fuel inertia forces. The fuel motion during a vehicle crash, which has a large influence on tank deformation and its supporting ele- ments, was thus completely neglected. Such simplifications were necessary due to the limitations of the simulation software used. However, some recent re- leases of simulation software already allow for an effec- tive solution of several physical systems simultaneously. In this paper different computational models that allow for consideration of the fluid motion and its influence on the structure are described and evaluated with the soft- ware LS-DYNA [10]. LS-DYNA is based on the finite- element method and it was originally designed for solv- ing structural dynamic problems. Therefore, its ability to model structural responses in general is well defined. However, the modelling of a coupled fluid-structure in- teraction is still quite challenging. A comparison of the methods and their applicability is illustrated on a practi- cal example, describing the fuel motion in a reservoir with simple geometry. The computational results are fur- ther compared with the experimental measurements [11].
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Modelling the water energy nexus: should variability in water supply impact on decision making for future energy supply options?

Modelling the water energy nexus: should variability in water supply impact on decision making for future energy supply options?

Water Cost scenario – Fig. 3), wet-cooled coal power plants are the preferred choice due to their lower investment costs and higher net generation efficiencies. However, when con- sideration is given to the regional variability in water supply (the Water Cost scenario in Fig. 4), dry-cooling is the pre- ferred option for new coal power plants, particularly in the Waterberg region where the remaining economically viable coal reserves are located. New dry-cooled capacity of ap- proximately 40 GW is commissioned by 2050 and includes the replacement of the existing stock of 37 GW which will mostly be retired by then. This SATIM-W result indicates that Eskom’s dry cooling policy is really in the economic interests of the country, even though it increases the cost of electricity from coal power plants. This has a significant impact in terms of future water use efficiency in the power sector, which could either reach a peak of 1.65 L kWh −1 by 2050 based on the No Water Cost scenario, or 0.5 L kWh −1 based on the Water Cost scenario. In absolute terms, the inclusion of regional water supply cost cuts the cumula- tive (2010–2050) water consumption for the power sector by 9338 million m 3 (77 %) with just a modest increase (0.84 %) in the system cost.
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Decision-focussed resource modelling for design decision support

Decision-focussed resource modelling for design decision support

A decision-focussed resource model differs from existing resource models in that it is designed and developed to support decision making, and therefore it provides not only the information about resources but also provides evaluation on the resource capability and fitness to design tasks, so that designers can make informed and rationalised decisions on resource allocation, levelling and configuration etc. Most existing research on resource definitions, models and systems are built upon the understanding of processes and activities. The transformation model shown in Figure 1(a) has been widely adopted in generic operations management [8]. All the inputs and some of the outputs (for example produced goods) are considered as resources. Typically those inputs which are used up in creating goods and services are defined as transformed resources, and those play a part in the creation process but not used up as transforming resources. This transformation model was expanded by Duffy and applied to design activities as illustrated in Figure 1(b) [9]. This latter model takes an activity as its central interest rather than a transformation process. An activity is taken to be a physical or cognitive action that creates an outcome, and the author explicitly stated that “an activity is carried out by a resource of some kind”. Compared with the transformation model, the activity model distinguishes resources and goals from inputs and outputs of a design activity. Resources were defined as means to carry out the activity while the other inputs provide the conditions or elements upon which the means act. That is, the resources facilitate the activity whereas the inputs and goals are used in the activity. This to some extend reflects the concepts of transformed and transforming resources in the transformation model. The performance of resources to undertake activities was further investigated through the definition of an E 2 model, i.e. efficiency and effectiveness [7]. In Haffey’s work, efficiency was defined as the relationship (often expressed as a ratio) between what has been gained (outputs minus inputs) and the level of resources used, as shown in Figure 1(c). On the other hand, the effectiveness is determined by the relationship between outputs and goals, as shown in Figure 1(d).
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Abstract: After water injection, water content of reservoir rises gradually, heterogeneity and high water-

Abstract: After water injection, water content of reservoir rises gradually, heterogeneity and high water-

Xingnan Development area reservoir layer has Saertu, Putaohua oil layer.Overall Saertu oil layer of Xingnan development area of pure oil reservoir sediment is mostly argillaceous sandstone and argillaceous siltstone and silty mudstone, poor permeability, as for putaohua reservoir because deposit is the more channel sand body, the lithology has better properties.

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OPERATING DIFFICULTIES OF SMALL WATER RESERVOIR LOCATED IN WASILKOW

OPERATING DIFFICULTIES OF SMALL WATER RESERVOIR LOCATED IN WASILKOW

When considering the issue of the functioning of small water reservoirs, the attempt to assess changes in trophy of small retention reservoir located in Wasilkow, Podlasie, before and after remediation, was carried out. Water samples tests were carried out once a month from April 2007 to March 2008, from April 2009 to March 2010 (be- fore remediation), and from April 2013 to March 2014 (after removal of silt). Prior to works related to the reservoir remediation, a gradual increase in the number of tested contaminants and disturbances in the seasonal occurrence of nitrogen and phosphorus compounds were observed. Advanced eutrophic processes in Wasilków reservoir oc- cur probably due to the supply of large amounts of humic and biogenic substances from the catchment, because a significant percentage of its area is covered by forests and agricultural lands. The development of the trophic status of the reservoir is largely influenced by the amount of phosphorus and total nitrogen supplied to the reservoir; the least affected by chlorophyll “a”. Comparing the analyses performed in 2007/2008 and 2009/2010, a slight, but growing trend of average trophic levels of water in the basin Wasilków was found. Studies conducted in 2013/2014 revealed a significant decrease in the concentrations of all analyzed pollutants, and hence lower TSI values. It can be concluded that the reclamation associated with the removal of sediments brought the expected results.
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The water footprint related to reservoir operation on a global scale

The water footprint related to reservoir operation on a global scale

The WRD database was used as main source of reservoir data, but the information in this database is not consistent. For each reservoir, the reservoir purposes are provided and for some reservoir purposes the production data is also provided. However, in some cases production figures are provided for a purpose, while that purpose is not listed as a purpose in the database. For some reservoirs, the data provided by the WRD database does not match with data provided by the GRanD database. The connection between the WRD database and the GRanD database is made based on dam name and the country where the dam is located. However, it is possible that within a single country there are 2 reservoirs with the same name. This will result in the wrong climatological data for that reservoir. Because not all production or price data was available for each reservoir purpose, estimations and assumptions were made (paragraph 2.2). These estimations or assumptions can result of over or underestimations. Within this study, one national price was used for both electricity and residential water supply. In most cases this was the average price for that country (Danilenko, et al., 2014; Eurostat, 2015). Because these prices varie within a country, the economic value can be over- or underestimated for reservoirs on a local scale. The economic value of flood storage in reservoirs is based on the economic value of flood storage in the United States. This can result in an over- or underestimation of the economic value of flood prevention for other countries. The drink water abstraction from reservoirs is estimated based on the reservoir volume and climate class. However, it is possible that in reality the abstraction is higher or lower than estimated. Secondly, the abstraction can vary annually (Zhoa & Liu, 2015).
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