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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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Reservoir water release dynamic decision model based on spatial temporal pattern

Reservoir water release dynamic decision model based on spatial temporal pattern

The multi-purpose reservoir water release decision requires an expert to make a decision by assembling complex decision information that occurred in real time. The decision needs to consider adequate reservoir water balance in order to maintain reservoir multi-purpose function and provide enough space for incoming heavy rainfall and inflow. Crucially, the water release should not exceed the downstream maximum river level so that it will not cause flood. The rainfall and water level are fuzzy information, thus the decision model needs the ability to handle the fuzzy information. Moreover, the rainfalls that are recorded at different location take different time to reach into the reservoir. This situation shows that there is spatial temporal relationship hidden in between each gauging station and the reservoir. Thus, this study proposed dynamic reservoir water release decision model that utilize both spatial and temporal information in the input pattern. Based on the patterns, the model will suggest when the reservoir water should be released. The model adopts Adaptive Neuro-Fuzzy Inference System (ANFIS) in order to deal with the fuzzy information. The data used in this study was obtained from the Perlis Department of Irrigation and Drainage. The modified Sliding Window algorithm was used to construct the rainfall temporal pattern, while the spatial information was established by simulating the mapped rainfall and reservoir water level pattern. The model performance was measured based on the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Findings from this study shows that ANFIS produces the lowest RMSE and MAE when compare to Autoregressive Integrated Moving Average (ARIMA) and Backpropagation Neural Network (BPNN) model. The model can be used by the reservoir operator to assist their decision making and support the new reservoir operator in the absence of an experience reservoir operator.
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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

Reservoir water release decision is one of the challenging tasks for reservoir operators in order to determine the amount of water to be stored and to be released from a reservoir (Norwawi et al., 2005; Wurbs, 1993). The reservoir capacity needs to be maintained in order to prevent downstream floods and to reduce water shortage problems in the future. In both flood and drought situations, the decisions regarding water release are made in accordance with the available water, inflows, demands, time, previous releases, etc. (Jain & Singh, 2003). However, different reservoirs have different objectives and purposes, thus different operation rules are needed (Wan Ishak et al., 2012). Typically, reservoir water release decision is based on upstream inflow that is observed through the magnitude of the upstream rainfall and the river water level. The total volume of rainfall may come from several gauging stations and their distances to the reservoir are varied (Mokhtar et al., 2016). Thus, rainfall observed at those gauging stations may take different time to reach the reservoir. This situation shows that there is a spatial temporal relationship hidden between each gauging station and the reservoir. Currently, there are limited studies that focus on this situation, as most of the previous studies focused on temporal relationship and the total rainfall volume (Mohan & Revesz, 2012; Wan Ishak et al., 2012; Mokhtar et al., 2014). Thus, the spatial temporal relationship needs to be considered for modelling reservoir water release decision.
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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

Typically, in normal and conflicting seasons the reservoir water release decision is guided by the reservoir operation rule. The decision includes determining the quantities of water to be stored and to be released or withdrawn from a reservoir under various conditions [5]. In practice most of the reservoirs are operated based on the operator’s intui-tion and common sense enriched with experience [2]. The operation rules are obtained from the reservoir operation manual established when it was first operated. This rule gradually needs to be adapted to structural changes occur-ring to the reservoir such as due to sedimentation. Alternatively, the operation rule can be derived by modelling the reservoir operation [6].
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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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Stream ecosystem health response to coal seam gas water release

Stream ecosystem health response to coal seam gas water release

Changes to the magnitude of flow influence aquatic ecosystems directly, by altering habitat availability and connectivity (i.e. through interaction with water depth changes and flow velocity); and indirectly through the modification in stream geomorphology. Discharges of a given magnitude are responsible for the creation and modification of in-channel bars, influence sediment deposition and stream bed and bank scouring, and provide opportunity for lateral exchange of material and energy between channel and floodplain systems. Reduced small flow events can result in reductions in the frequency of filling or recharge of in-channel refugial pools. The same impact may apply if groundwater abstractions result in reduction to stream baseflow. The pools in ephemeral channels play an important ecological role. Changes to the quantity of water in pools may also be reflected by changes in the temperature and quality of the water (Hughes 2005), which reduce the capacity of these pools to function as refugia for biota.
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Stream ecosystem health response to coal seam gas water release

Stream ecosystem health response to coal seam gas water release

Control sites are upstream of the impact site used to set the monitoring objectives. These control sites represent the ecological health of streams prior to CSG water discharge. Control sites must approximate the selected test sites on broad geophysical characteristics such as stream order and altitude. Selected sites are designed to be as similar to the impact sites as possible (Downes et al. 2002), including the effects from sources of contaminants other than the CSG water. Comparison of health of control sites with that of reference sites will indicate how degraded the starting point is for that specific stream. It would account for natural spatial variation in the control zone. The level of replication permits analyses of differences between test and control sites. Repeated sampling of the same site will not achieve these aims due to problems associated with pseudo-replication and confusion of temporal versus spatial variation (Marshall & Choy 1999).
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Dinamic of Bacteria Desnitrificants and Nitrificants in the Rizospheric of Wheat with Slow Release of Fertilizer, Irrigated with Waste or Well Water

Dinamic of Bacteria Desnitrificants and Nitrificants in the Rizospheric of Wheat with Slow Release of Fertilizer, Irrigated with Waste or Well Water

luation of sampling interaction with the other factors evaluated: type of water, fertilizers and soil type (vs. rhi- zosphere. Rhizospheric not) for any of the populations of bacteria quantified. Figure 1 shows the temporal changes of the populations of denitrificant bacteria of non rhizospheric soil in relation to plant age with waste water and well water. In general terms, there was an increase in denitrificant bacteria populations, when the plan age in- creased, indepently of type of water. The average effect of the types of water, soil and fertilizers (as well as their interactions between them) was not significant (p > 0.05) on the development of the denitrificant bacteria popula- tions, evaluated MPN. In general terms, The populations denitrificant were higher in non rhizospheric in relation to those found in rhizospheric soil, independently of the type de water. In average, denitrificant from non rhizo- spheric soil populations were higher in well water than in waste water. Mean while, the opposite trend was obser- ved in rhizospheric soil (Table 4).
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The socioecohydrology of rainwater harvesting in India: understanding water
storage and release dynamics across spatial scales

The socioecohydrology of rainwater harvesting in India: understanding water storage and release dynamics across spatial scales

The temporal pattern of net GE, estimated using Eq. (2), is presented in Fig. 7 together with trends in tank water lev- els and daily precipitation. GE rates across the monsoon sea- son appear to be driven by a combination of both tank water levels and the occurrence and magnitude of rainfall events. Tank 2, for example, has relatively lower recharge rates (pos- itive values in Fig. 7) in the earlier part of the season, with values decreasing with the occurrence of each major rain- fall event, and then increasing incrementally over time until the next rainfall. The last period of significant rainfall oc- curs in mid-December, and shortly after this time, recharge magnitudes for Tank 2 reach a peak, and then slowly de- crease with decreasing tank water levels. A similar pattern can be seen for Tank 4, where the peak recharge value oc- curs during the mid-December period, followed by a steady decline in recharge magnitudes as tank water levels decrease. In contrast, Tanks 1 and 3 appear to be less impacted by rain- fall events; for these tanks, recharge magnitudes begin to de- crease with decreases in tank water levels much earlier in the season, after the last major rainfall (64 mm) on 17 Novem- ber. In the last few weeks of the monsoon season, Tanks 2–4 all switch over to a groundwater inflow regime (negative GE values). Lower recharge rates as well as these switches to groundwater inflow towards the end of the season may be due to tank water levels consistently having greater declines compared to the surrounding aquifer, resulting in decreases and potential reversals of hydraulic head gradients. This pe- riod is also, however, punctuated by some distinct, very high groundwater outflow events that may correspond to observed groundwater pumping in the vicinity, highlighting a potential direct human influence to tank recharge rates.
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Evaluation of Hydrophilic Polymers and their Combinations in Formulation of Sustained-Release Matrix Tablets of Water-Soluble Drug

Evaluation of Hydrophilic Polymers and their Combinations in Formulation of Sustained-Release Matrix Tablets of Water-Soluble Drug

Aim of the work: The present study was aimed to investigate matrix-forming property of gum olibanum for sustained-release tablets of tramadol, a freely water-soluble drug. The possible synergistic effect of gum olibanum with hydroxypropyl methylcellulose (HPMC) and xanthan gum, and widely used pharmaceutical excipients on tablet properties was investigated. Methods: Matrix tablets were prepared containing polymers, either alone or in combinations, by wet granulation. Tablets were evaluated for their physico-mechanical properties, drug release and hydration properties. Results: Tablets swelling increased till ~3-4 h this was followed by a decline phase. Drug release from the matrix tablets was dependent on the concentration of gum olibanum. Formulation with drug: gum olibanum in 1:2 released 94.37±2.37% drug in 6 h. The drug release data was analyzed by model dependent and model independent equations. Combination of gum olibanum with HPMC or xanthan gum matrices significantly (p<0.05) modulated drug release, which was reflected by the mean dissolution times (MDTs). Incorporation of lactose induced faster drug release compared to dicalcium phosphate and microcrystalline cellulose. Drug release largely followed first-order kinetics and non-Fickian type of diffusion.
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Influence of Soil Water Content and Soil Amendments on Trace Metal Release and Seedling Growth in Serpentine Soil

Influence of Soil Water Content and Soil Amendments on Trace Metal Release and Seedling Growth in Serpentine Soil

Soil moisture regimes have a direct effect on many physi- cochemical and microbiological qualities of soils, including organic matter content, pH, and electrical conductivity (Van den Berg and Loch 2000; Angle et al. 2003; Gray and Mclaren 2006; Zheng and Zhang 2011) and are indirectly involved in the fractionation of metals and their bioavailability. Zheng and Zhang (2011) demonstrated the effect of hydrological regimes such as field capacity, flooding, and wetting-drying cycles for transformation rates of trace metals among different fractions of paddy soils. The flooding condition of soil contributes to higher transformation rates of trace metals towards stable frac- tions (i.e., Fe–Mn oxide and organic matter bound) than soil incubated under 75% of field capacity and soils which are kept under wetting – drying cycles. Trace metal transformation into stable fractions under flooded conditions is governed by in- creased pH levels, metal ion precipitation with sulfides, and increased concentration of ion oxides which are induced by the saturated water level (Zheng and Zhang 2011). Therefore, current soil water status such as field capacity and wilting point as well as the dynamics of soil moisture content should be taken into account in order to assess the release and bio- availability of trace metals from certain soil types.
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Press Release FOR IMMEDIATE RELEASE

Press Release FOR IMMEDIATE RELEASE

We believe these non-GAAP measures and ratios, when taken together with the corresponding GAAP measures and ratios, provide meaningful supplemental information regarding our performance. Our management uses, and believes that investors benefit from referring to, these non-GAAP measures and ratios in assessing our current operating results and related trends, and when planning and forecasting future periods. However, these non-GAAP measures and ratios should be considered in addition to, not as a substitute for or preferable to, ratios prepared in accordance with GAAP. In the financial tables below, we have provided a reconciliation of, where applicable, the most comparable GAAP financial measures and ratios to the non-GAAP financial measures and ratios used in this press release, or a reconciliation of the non-GAAP calculation of the financial measure.
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The scaling of carbon dioxide release and respiratory water loss in flying fruit flies (Drosophila spp )

The scaling of carbon dioxide release and respiratory water loss in flying fruit flies (Drosophila spp )

3.06±0.52 mg, respectively (means ± S . D .). Some of the force measurements and respirometry data presented here have been published previously in an analysis of muscle efficiency and aerodynamic flight performance (Lehmann and Dickinson, 1997, 1998). Unless stated otherwise, all reported values represent means ± S . D . Throughout the paper, we performed reduced major axis regression (model II) on species mean values as part of the statistical data analysis. Since the species are separated by millions of generations, we assume that each species can be treated as statistically independent. However, water loss rates and other physiological characters may evolve within a few tens of generations under laboratory conditions (Gibbs et al., 1997), which would justify a statistical analysis on the whole data set. Treating all tested animals as one large population, the statistical analysis produced exponents similar to those obtained using species mean values, but at P values consistently below 0.005 (except for Fig. 6C, P=0.29). This result gives confidence in the general conclusions we draw from our statistical analysis on four species mean values, despite the relatively high P values.
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Release Notes for Cisco VPN Client, Release and Release

Release Notes for Cisco VPN Client, Release and Release

When installing the Windows MSI installation package, the user must manually uninstall the previous VPN Client if it is older than Release 4.7. The Release 5.0 MSI installer does not detect older versions, and the installer attempts to install before aborting gracefully. Once a version 4.7 MSI package has been installed, future client versions can detect the existing release 5.0.x installation and automatically begin the uninstallation process.

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Electromotive Force Generation with Hydrogen Release by Salt Water Flow under a Transverse Magnetic Field

Electromotive Force Generation with Hydrogen Release by Salt Water Flow under a Transverse Magnetic Field

2Na   2e   2H O  2NaOH  H g (8) The reaction in (8) implies production of sodium hy- droxide and hydrogen gas at the positively charged elec- trode. Therefore, also confiding on the experimental evidence in ref. [2], we may argue that it is possible to device an e.m.f. generator by letting salt water run in a pipe with average velocity A under an orthogonal

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Release Notes for Patch Release #2614

Release Notes for Patch Release #2614

User wasnt able to edit existing social account with Internet Explorer 11.. This was fixed by completely searching account array, to be able to edit social accounts again.[r]

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Release of Arsenic from Arsenic Removal Water Filter Sludge in Soil and Its Uptake by Red Amaranth

Release of Arsenic from Arsenic Removal Water Filter Sludge in Soil and Its Uptake by Red Amaranth

wooden hammer and mixed thoroughly to make the sam- ples homogeneous. For the purpose of pot culture, the soil samples were sieved through a 5 mm sieve. Moist soil equivalent to 4 kg dry mass was placed in earthen pots (30 cm height and 25 cm diameter) after mixing with arsenic removal water filter sludge at levels of 0, 1.5 and 2.5 t·ha −1 which provided 0, 0.41 and 0.68 mg As

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Release

Release

The words hit like a slap and Alice let the pen fall to the pad.. Alice knew Shirley could sense the change in her.[r]

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SNMP Informant Release Notes Release

SNMP Informant Release Notes Release

──────────────────────────────────────────────────────────────────────────────────── Release 2010.1 ID Product Summary 1 SI-CUSTOM Added new custom agent which is available with Premium Licenses that allow the user to define their own performance, registry, and process queries. 2 SI-PDH-FOREFRONT Added new agent to support Microsoft Forefront performance counters. 3 SI-PDH-HYPERV Added new agent to support Microsoft Hyper-V performance counters. 4 SI-PDH-WSUS Added new agent to support Microsoft WSUS performance counters.
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TapAnalytics Release Notes for external release

TapAnalytics Release Notes for external release

Hello  and  welcome  to  the  latest  version  TapAnalytics  –  Release  5.5!    This  release  contains   the  long-­‐awaited  ‘Importer’  which  enables  Users  to  set  up  their  own  automated  datafeeds   as  well  as  a  huge  number  of  new  User  Experience  improvements  that  make  working  with   TapAnalytics  even  more  of  a  breeze  than  it  already  was!    Enjoy!  

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