COMPARATIVE STUDY OF DROUGHT PREDICTION TECHNIQUES FOR RESERVOIR OPERATION1

ABSTRACT: Predicting the likelihood of a drought markedly enhances the efficiency of reservoir operations. This study applies the kriging method and time series analysis to predict inflows to Shihmen Reservoir in northern Taiwan. A subsequent reservoir operation simulation is employed to determine the drought lead time (DLT), the time before the onset of a drought. A more efficient reservoir operational strategy can be established with the aid of DLT and the probability of successful drought prediction (P s). Simulation results of reservoir operation over a period of three decades demonstrate that, at one month DLT, the kriging approach achieves 0.86 of P s for moderate droughts and 0.94 of P s for severe droughts. The kriging approach generally outperformed the time series approach in terms of DLT, P s of drought prediction, and the number of correctly predicted drought events.     

A Drinking Water Crisis in Lake Taihu, China: Linkage to Climatic Variability and Lake Management

In late May, 2007, a drinking water crisis took place in Wuxi, Jiangsu Province, China, following a massive bloom of the toxin producing cyanobacteria Microcystis spp. in Lake Taihu, China’s third largest freshwater lake. Taihu was the city’s sole water supply, leaving approximately two million people without drinking water for at least a week. This cyanobacterial bloom event began two months earlier than previously documented for Microcystis blooms in Taihu. This was attributed to an unusually warm spring. The prevailing wind direction during this period caused the bloom to accumulate at the shoreline near the intake of the water plant. Water was diverted from the nearby Yangtze River in an effort to flush the lake of the bloom. However, this management action was counterproductive, because it produced a current which transported the bloom into the intake, exacerbating the drinking water contamination problem. The severity of this microcystin toxin containing bloom and the ensuing drinking water crisis were attributable to excessive nutrient enrichment; however, a multi-annual warming trend extended the bloom period and amplified its severity, and this was made worse by unanticipated negative impacts of water management. Long-term management must therefore consider both the human and climatic factors controlling these blooms and their impacts on water supply in this and other large lakes threatened by accelerating eutrophication.     

Factors Influencing Prymnesium parvum Population Dynamics During Bloom Initiation: Results from In-lake Mesocosm Experiments1

Roelke, Daniel L., Leslie Schwierzke, Bryan W. Brooks, James P. Grover, Reagan M. Errera, Theodore W. Valenti, Jr., and James L. Pinckney, 2010. Factors Influencing Prymnesium parvum Population Dynamics During Bloom Initiation: Results from In-Lake Mesocosm Experiments. Journal of the American Water Resources Association (JAWRA) 46(1):76-91. DOI: 10.1111/j.1752-1688.2009.00392.x Abstract: The alga Prymnesium parvum forms large fish-killing blooms in many Texas lakes. In some of these lakes, however, P. parvum occurs but does not develop blooms. In this study, we investigated factors that may influence bloom initiation by conducting a series of in-lake experiments involving mixing of waters from Lake Whitney, which has a history of P. parvum blooms, with waters from Lake Waco where no blooms have occurred. In all experiments, the addition of Lake Waco waters resulted in a poorer performance of P. parvum. Various experimental treatments and field data show that differences in grazing, pathogens, nutrients, and salts between the two lakes were not likely factors that contributed to this observation. Industrial and agricultural contaminants, allelochemicals and algicidal chemicals were not measured as a part of this research. However, anthropogenic contaminants other than nutrients were not observed at levels exceeding water quality standards in Lake Waco in recent years. On the other hand, nuisance cyanobacteria are common in Lake Waco, where Microcystis sp. and Anabaena sp. were abundant during the initiation of our experiments, both taxa are known to produce chemicals with allelopathic properties. In addition, the emergent field of algal-heterotrophic bacteria interactions suggests that chemicals produced by heterotrophic bacteria should not be overlooked. Further research focusing on the chemical interactions between cyanobacteria and P. parvum, as well as the potential role of algicidal bacteria, in the initiation of P. parvum blooms is necessary, as it may be important to the management of these blooms.     

Predicting Carbon Monoxide Concentrations in the Air of Pardis City,Iran, Using an Artificial Neural Network

To date, several methods have been proposed to explain the complex process of air pollution prediction. One of these methods uses neural networks. Artificial neural networks (ANN) are a branch of artificial intelligence, and because of their nonlinear mathematical structures and ability to provide acceptable forecasts, they have gained popularity among researchers. The goal of our study as documented in this article was to compare the abilities of two different ANNs, the multilayer perceptron (MLP) and radial basis function (RBF) neural networks, to predict carbon monoxide (CO) concentrations in the air of Pardis City, Iran. For the study, we used data collected hourly on temperature, wind speed, and humidity as inputs to train the networks. The MLP neural network had two hidden layers that contained 13 neurons in the first layer and 25 neurons in the second layer and reached a mean bias error (MBE) of 0.06. The coefficient of determination (R²), index of agreement (IA), and the Nash–Scutcliffe efficiency (E) between the observed and predicted data using the MLP neural network were 0.96, 0.9057, and 0.957, respectively. The RBF neural network with a hidden layer containing 130 neurons reached an MBE of 0.04. The R², IA, and E between the observed and predicted data using the RBF neural network were 0.981, 0.954, and 0.979, respectively. The results provided by the RBF neural network had greater acceptable accuracy than was the case with the MLP neural network. Finally, the results of a sensitivity analysis using the MLP neural network indicated that temperature is the primary factor in the prediction of CO concentrations and that wind speed and humidity are factors of second and third importance when forecasting CO levels.     

Current Status of Mathematical Models for Population Dynamics of Prymnesium parvum in a Texas Reservoir1

Grover, James P., Jason W. Baker, Daniel L. Roelke, and Bryan W. Brooks, 2010. Current Status of Mathematical Models for Population Dynamics of Prymnesium parvum in a Texas Reservoir. Journal of the American Water Resources Association (JAWRA) 46(1):92-107. DOI: 10.1111/j.1752-1688.2009.00393.x Abstract: Blooms of the harmful alga Prymnesium parvum have apparently increased in frequency in inland waters of the United States, especially in western Texas. A suite of mathematical models was developed based on a chemostat (or continuously stirred tank reactor) framework, and calibrated with data from Lake Granbury, Texas. Inputs included data on flows, salinity, irradiance, temperature, zooplankton grazing, and nutrients. Parameterization incorporated recent laboratory studies relating the specific growth rate of P. parvum to such factors. Models differed in the number of algal populations competing with P. parvum, and whether competition occurred only by consumption of shared nutrients, or additionally through production of an allelopathic chemical by one of the populations, parameterized as cyanobacteria. Uncalibrated models did not reproduce the observed seasonal dynamics of P. parvum in Lake Granbury, which displayed a maximum population in late February during a prolonged bloom in cooler weather, and reduced abundance in summer. Sensitivity analyses suggested two modifications leading to predictions that better resembled observations. The first modification greatly reduces the optimal temperature for growth of P. parvum, an approach that disagrees with laboratory experiments indicating a strong potential for growth at temperatures above 20°C. The second modification increases the growth rate of P. parvum at all temperatures, in models including cyanobacterial allelopathy. Despite these adjustments, calibrated models did not faithfully simulate all features of the seasonal dynamics of P. parvum.     

A HYPOTHESIS ABOUT FACTORS THAT AFFECT MAXIMUM SUMMER STREAM TEMPERATURES ACROSS MONTANE LANDSCAPES1

ABSTRACT: Temperature is an important variable structuring lotic biotas, but little is known about how montane landscapes function to determine stream temperatures. We developed an a priori hypothesis that was used to predict how watershed elements would interact to affect stream temperatures. The hypothesis was tested in a series of path analyses using temperature data from 26 sites on second‐order to fourth‐order streams across a fifth‐order Rocky Mountain watershed. Based on the performance of the first hypothesis, two revised versions of the hypothesis were developed and tested that proved to be more accurate than the original hypothesis. The most plausible of the revised hypotheses accounted for 82 percent of the variation in maximum stream temperature, had a predicted data structure that did not deviate from the empirical data structure, and was the most parsimonious. The final working hypothesis suggested that stream temperature maxima were directly controlled by a large negative effect from mean basin elevation (direct effect = ‐0.57, p < 0.01) and smaller effects from riparian tree abundance (direct effect = ‐0.28, p = 0.03), and cattle density (direct effect = 0.24, p = 0.05). Watershed slope, valley constraint, and the abundance of grass across a watershed also affected temperature maxima, but these effects were indirect and mediated through cattle density and riparian trees. Three variables included in the a priori hypothesis ‐ watershed aspect, stream width, and watershed size ‐ had negligible effects on maximum stream temperatures and were omitted from the final working hypothesis.     

BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: Water scarcity in the Sevier River Basin in south‐central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day‐to‐day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin‐scale and could be valuable to water users in semi‐arid areas to more efficiently utilize and manage scarce water resources.     

Associations between Water Physicochemistry and Prymnesium parvum Presence,Abundance, and Toxicity in West Texas Reservoirs

Toxic blooms of golden alga (Prymnesium parvum) have caused substantial ecological and economic harm in freshwater and marine systems throughout the world. In North America, toxic blooms have impacted freshwater systems including large reservoirs. Management of water chemistry is one proposed option for golden alga control in these systems. The main objective of this study was to assess physicochemical characteristics of water that influence golden alga presence, abundance, and toxicity in the Upper Colorado River basin (UCR) in Texas. The UCR contains reservoirs that have experienced repeated blooms and other reservoirs where golden alga is present but has not been toxic. We quantified golden alga abundance (hemocytometer counts), ichthyotoxicity (bioassay), and water chemistry (surface grab samples) at three impacted reservoirs on the Colorado River; two reference reservoirs on the Concho River; and three sites at the confluence of these rivers. Sampling occurred monthly from January 2010 to July 2011. Impacted sites were characterized by higher specific conductance, calcium and magnesium hardness, and fluoride than reference and confluence sites. At impacted sites, golden alga abundance and toxicity were positively associated with salinity‐related variables and blooms peaked at ~10°C and generally did not occur above 20°C. Overall, these findings suggest management of land and water use to reduce hardness or salinity could produce unfavorable conditions for golden alga.     

Dynamics of fisheries that affect the population growth rate coefficient

Conventional surplus production models indicate that destruction of fish populations by overfishing is difficult, if not impossible, but catastrophic declines in abundance of exploited populations are common. Surplus production models also do not predict large continuing fluctuations in yield, but large fluctuations in yield are common. Conventional surplus production models assume that fisheries do not impact the population's capacity to increase, but changes in age structure or a decrease in age-specific fecundity resulting from fishing can decrease the coefficient of increase. A surplus production model is developed in which fishing reduces the capacity of a population to increase; the model is applied to describe the fluctuations observed in yield of lake herring (Coregonus artedii) from the upper Great Lakes. The fisheries of the Great Lakes were decimated by the combined effects of heavy fishing and a changing environment. For some species, yield increased to high levels and then the fisheries collapsed; for other species, yield and effort fluctuated greatly.     

Effective Control of Aquatic Invasive Species in Tropical Australia

Often ecologists and natural resource managers can easily access data on invasive species occurrence across a region. Yet, collecting species abundance data over a large area is arguably more important for decision making, but inherently costly, so methods which can provide robust information at low-cost are particularly valuable. Studies of species distribution often use occurrence data to build models of the environmental niche. Environmental suitability derived from such models may be used to predict the potential distributions of species. The ability of such models to predict spatial patterns in abundance have recently been demonstrated. Here we tested the relationship of environmental suitability with local abundance of an aquatic invasive species, olive hymenachne (Hymenachne amplexicaulis) in the Wet Tropics of Australia. Ordinary least squares and quantile regressions revealed a positive relationship between environmental suitability and local abundance of olive hymenachne. We expand on this and use the relationship between environmental suitability and local abundance to quantify the effectiveness of management (reduction in local abundance) under four different management investments. We show that the upper limit of abundance can be used to evaluate management effectiveness based on varying investments, and that ongoing management is the most effective at reducing local abundance. We discuss implications of this in addressing important problems in invasive species management.     

Temperatures and Locations Used by Hibernating Bats,Including <Emphasis Type="Italic">Myotis sodalis</Emphasis> (Indiana Bat), in a Limestone Mine: Implications for Conservation and Management

Understanding temperatures used by hibernating bats will aid conservation and management efforts for many species. A limestone mine with 71 km of passages, used as a hibernaculum by approximately 30,000 bats, was visited four times during a 6-year period. The mine had been surveyed and mapped; therefore, bats could be precisely located and temperatures (T _s) of the entire hibernaculum ceiling accurately mapped. It was predicted that bats should hibernate between 5 and 10°C to (1) use temperatures that allow a near minimal metabolic rate, (2) maximize the duration of hibernation bouts, (3) avoid more frequent and prolonged arousal at higher temperatures, (4) avoid cold and freezing temperatures that require an increase in metabolism and a decrease in duration of hibernation bouts or that could cause death, and (5) balance benefits of a reduced metabolic rate and costs of metabolic depression. The distribution of each species was not random for location (P < 0.000) or T _s (P < 0.000). Myotis sodalis (Indiana bat) was most restricted in areas occupied, hibernating in thermally stable yet cold areas ( = 8.4 ± 1.7°C); 99% associated with cement block walls and sheltered alcoves, which perhaps dampened air movement and temperature fluctuations. Myotis lucifugus (little brown myotis) hibernated in colder, more variable areas ( = 7.2 ± 2.6°C). Myotis septentrionalis (northern myotis), Pipistrellus subflavus (eastern pipistrelle), and Eptesicus fuscus (big brown bat) typically hibernated in warm, thermally stable areas ( = 9.1 ± 0.2°C, = 9.6 ± 1.9°C, and = 9.5 ± 1.5°C, respectively). These data do not indicate that hibernacula for M. sodalis, an endangered species, should be manipulated to cool below 5°C.     

The Effects of Three Chemical Algaecides on Cell Numbers and Toxin Content of the Cyanobacteria Microcystis aeruginosa and Anabaenopsis sp.

Toxic cyanobacteria blooms are a growing concern for public health and safety, due in part to the production of the hepatotoxin microcystin by certain species, including Microcystis aeruginosa. Management strategies for controlling cyanobacteria blooms include algaecide treatments, often with copper sulfate, and more recently oxidizers such as sodium percarbonate that produce hydrogen peroxide. This study assessed the effects of two copper-containing algaecides and one sodium percarbonate-containing algaecide on mitigating cell numbers and toxin content of cultured M. aeruginosa and summer (July) bloom samples of Anabaenopsis sp. in a brackish stormwater detention pond. Monitoring of the bloom revealed that Anabaenopsis sp. was associated with elevated levels of orthophosphate compared to nitrogen (dissolved inorganic nitrogen to phosphorus ratios were 0.19–1.80), and the bloom decline (September–October) was likely due to lower autumn water temperatures combined with potential grazing by the dinoflagellate Protoperidinium quinquecorne. Laboratory-based algaecide experiments included three dose levels, and cyanobacteria cell numbers and microcystin concentrations (particulate and dissolved) were evaluated over 7 d. Following exposure, copper-containing treatments generally had lower cell numbers than either sodium percarbonate-containing or control (no algaecide) treatments. Addition of algaecides did not reduce overall microcystin levels, and a release of toxin from the particulate to dissolved phase was observed in most treatments. These findings indicate that algaecide applications may visibly control cyanobacteria bloom densities, but not necessarily toxin concentrations, and have implications for public health and safety.     

Linking Excess Nutrients,Light, and Fine Bedded Sediments to Impacts on Faunal Assemblages in Headwater Agricultural Streams1

Griffith, Michael B., F. Bernard Daniel, Matthew A. Morrison, Michael E. Troyer, James M. Lazorchak, and Joseph P. Schubauer‐Berigan, 2009. Linking Excess Nutrients, Light, and Fine Bedded Sediments to Impacts on Faunal Assemblages in Headwater Agricultural Streams. Journal of the American Water Resources Association (JAWRA) 45(6):1475‐1492. Abstract: Biological impairments in streams are typically defined by regulatory agencies in terms of altered invertebrate or fish assemblages. While nutrients, canopy cover, and sediment fines contribute to these impairments, these stressors are often defined, at least in part, by their impacts on periphyton. Path analysis can extend these assessments to impacts on invertebrates and fish by characterizing the direct and indirect relationships among variables along defined model pathways. With data from headwater tributaries in the Little Miami River, Ohio, we tested models of the impacts of nutrients [total nitrogen (TN), total phosphorus (TP), and the nitrogen to phosphorus (N/P) ratio], the percentage of (%) open canopy, and the % sand and fines on three periphyton metrics [periphytic ash‐free dry mass (AFDM), the percent abundance of cyanobacteria (% cyanobacteria), and the percent abundance of Chlorophyta (% Chlorophyta)] and, in turn, on selected invertebrate or fish metrics. Our objective was to develop and evaluate a statistical model that assesses the direct and indirect impacts of excess nutrients on macroinvertebrate and fish in these streams and demonstrate how this approach might be applicable elsewhere. The results suggest indirect pathways for the influences of nutrients, canopy cover, and fine bedded sediments on invertebrates or fish that are mediated by their influences on periphyton. This is in addition to any direct impacts of these stressors on the invertebrate and fish metrics. In most models, all three periphyton metrics increased with % open canopy. Periphytic AFDM increased with TN, while % cyanobacteria decreased. The % cyanobacteria also decreased with % sand and fines, but % Chlorophyta increased. The metrics, percent abundance of (%) three most dominant (macroinvertebrate) taxa, % Trichoptera, and % herbivorous fish all increased with periphytic AFDM, while % climbers, % swimmers, and %Lepomis cyanellus Rafinesque decreased. Lepomis cyanellus is an indicator species, because it is generally common in these streams and relatively tolerant to various common environmental stressors. The % three most dominant macroinvertebrate taxa increased while % Hydropsychidae (Trichoptera) and %L. cyanellus decreased with % cyanobacteria. The % Trichoptera and %L. cyanellus increased with % Chlorophyta. Some macroinvertebrate metrics, such as the % burrowers and number of burrower taxa, did not have any statistically significant relationships with the periphyton metrics but did exhibit a direct pathway with % sand and fines. These analyses illustrate how path analysis can be used to estimate the relationships among the variables in a conceptual model, modify the model, assess the relative importance of different paths, and explore responses resulting from stressors with interacting and indirect impacts.     

Impingement losses of white perch at Hudson River power plants: Magnitude and biological significance

We performed a quantitative assessment of the impact of impingement at power plants on the Hudson River white perch population We estimated that impingement reduces the abundance of each white perch year class by at least 10% and probably by 15–20% or more after 2–3 years of vulnerability to power plants We attempted to detect effects of impingement on average year-class abundance of white perch from a time series of abundance indices derived from impingement data We found, however, that neither impingement collection rates observed at Hudson River power plants nor beach seine data provide a reliable index of year-class strength in white perch. Even if a reliable index were developed, natural fluctuations in year-class strength are great enough that a short-term monitoring program would be inadequate for detecting even a large reduction in average year-class strength. We performed a multipopulation analysis using simple food chain and food web models The results suggest that any long-term decline in white perch abundance caused by impingement should be accompanied by an increase in the abundance of one or more competing fish species and by an increase in the biomass of adult white perch relative to young-of-the-year.We conclude that 1) at present, assessments of population-level impact of impingement should focus on short-term effects, 2) research is needed to develop a reliable index of year-class strength for use in long-term monitoring programs, 3) identification and quantification of natural environmental factors influencing year-class strength are needed to improve our ability to predict and detect changes in abundance, and 4) it would be useful in designing monitoring programs to focus on detecting patterns of change among populations and age groups rather than solely on declines in abundance of individual populationsResearch sponsored by the Office of Nuclear Regulatory Research, US Nuclear Regulatory Commission under Interagency Agreement No. 40-550-75 with the US Department of Energy under Contract W-7405-eng-26 with Union Carbide Corporation. Publication No. 2030, Environmental Sciences Division, ORNL.     

Environmental Control and Limnological Impacts of a Large Recurrent Spring Bloom in Lake Washington,USA

A series of statistical analyses were used to identify temporal and spatial patterns in the phytoplankton and nutrient dynamics of Lake Washington, an mesotrophic lake in Washington State (USA). These analyses were based on fortnightly or monthly samples of water temperature, Secchi transparency, ammonium (NH₄), nitrate (NO₃), inorganic phosphorus (IP), total nitrogen (TN), total phosphorus (TP), dissolved oxygen (DO), pH and chlorophyll a (chl a) collected during 1995–2000 from 12 stations. Lake Washington has a very consistent and pronounced annual spring diatom bloom which occurs from March to May. During this bloom, epilimnetic chl a concentrations peak on average at 10 μg/L, which is 3 times higher than chl a concentrations typically seen during summer stratified conditions. The spring bloom on average comprised 62% diatoms, 21% chlorophytes and 8% cyanobacteria. During summer stratification, diatoms comprised 26% of the phytoplankton community, chlorophytes 37% and cyanobacteria 25%. Cryptophytes comprised approximately 8% of the community throughout the year. Overall, 6 phytoplankton genera (i.e., Aulacoseira, Fragilaria, Cryptomonas, Asterionella, Stephanodiscus, and Ankistrodesmus) cumulatively accounted for over 50% of the community. These analyses also suggest that the phytoplankton community strongly influences the seasonality of NO₃, IP, DO, pH and water clarity. According to a MANOVA, seasonal fluctuations explained 40% of the total variability for the major parameters, spatial heterogeneity explained 10% of variability, and the seasonal-spatial interaction explained 10% of variability. Distinctive patterns were identified between offshore and inshore sampling stations. The results of our analyses also suggest that spatial variability was substantial, but much smaller than temporal variability.     

Spatiotemporal Associations of Reservoir Nutrient Characteristics and the Invasive,Harmful Alga Prymnesium parvum in West Texas

Golden alga (Prymnesium parvum) is a harmful alga that has caused ecological and economic harm in freshwater and marine systems worldwide. In inland systems of North America, toxic blooms have nearly eliminated fish populations in some systems. Modifying nutrient profiles through alterations to land or water use may be a viable alternative for golden alga control in reservoirs. The main objective of this study was to improve our understanding of the nutrient dynamics that influence golden alga bloom formation and toxicity in west Texas reservoirs. We examined eight sites in the Upper Colorado River basin, Texas: three impacted reservoirs that have experienced repeated golden alga blooms; two reference reservoirs where golden alga is present but nontoxic; and three confluence sites downstream of the impacted and reference sites. Total, inorganic, and organic nitrogen and phosphorus and their ratios were quantified monthly along with golden alga abundance and ichthyotoxicity between December 2010 and July 2011. Blooms persisted for several months at the impacted sites, which were characterized by high organic nitrogen and low inorganic nitrogen. At impacted sites, abundance was positively associated with inorganic phosphorus and bloom termination coincided with increases in inorganic nitrogen and decreases in inorganic phosphorus in late spring. Management of both inorganic and organic forms of nutrients may create conditions in reservoirs unfavorable to golden alga.     

香溪河库湾浮游细菌和病毒计数方法比较研究

液氮速冻后转入-80℃保存是流式细胞仪计数浮游细菌及病毒的样品保存之标准条件。以荧光显微镜计数法为参照,利用流式细胞仪对温和冷冻保存(-20℃冷冻2周后转入-80℃保存)的香溪河库湾水样分别计数了浮游细菌和浮游病毒丰度。结果显示,利用流式细胞仪计数香溪河库湾水样中的浮游细菌丰度与荧光显微镜计数法等效(P>0.05);流式细胞仪计数浮游病毒丰度高于荧光显微镜计数结果2.0±0.1倍(Z=-4.704,P<0.01),且具有强正相关(r=0.979,P<0.01)。计数结果间的差异与其他环境中样品标准保存方法下计数结果相近,表明温和冷冻保藏条件对流式细胞仪计数香溪河库湾水样中浮游细菌和浮游病毒丰度结果无明显影响。     

Non-linear autoregressive neural network approach for inside air temperature prediction of a pillar cooler

The volcanic plate made pillar cooler system is designed for outdoor spaces as a heat exchanging medium and reduces the outcoming air temperature which flows through the exhaust port. This paper proposes the use of artificial neural networks (ANNs) to predict inside air temperature of a pillar cooler. For this purpose, at first, three statistically significant factors (outside temperature, airing and watering) influencing the inside air temperature of the pillar cooler are identified as input parameters for predicting the output (inside air temperature) and then an ANN was employed to predict the output. In addition, 70%, 15% and 15% data was chosen from a previously obtained data set during the field trial of the pillar cooler for training, testing and validation, respectively, and then an ANN was employed to predict inside air temperature. The training (0.99918), testing (0.99799) and validation errors (0.99432) obtained from the model indicate that the artificial neural network model (NARX) may be used to predict inside air temperature of pillar cooler. This study reveals that, an ANN approach can be used successfully for predicting the performance of pillar cooler.     

Flood Forecasting Using Neural Computing Techniques and Conceptual Class Segregation

Various neural networks models are developed and applied for flood forecasting at Sangye station (no. 1) of the Bocheong Stream catchment, which is one of the International Hydrological Program's representative catchments, Republic of Korea. The neural networks models (NNMs) are multilayer perceptron‐neural networks model (MLP‐NNM), generalized regression neural networks model (GRNNM), and Kohonen self‐organizing feature maps neural networks model (KSOFM‐NNM). Data used for model training and testing are divided into two groups: such as floods and typhoon events. Single conventional application and class segregation implementation are applied to evaluate the neural networks models. KSOFM‐NNM forecasts flood discharge more accurately than do MLP‐NNM and GRNNM for the testing data of Methods I and II for single conventional application and class segregation implementation. This study shows that class segregation can capture the dynamics of different physical processes and overcome the difficulties using single conventional application of neural networks models.     

Mini Review: Probes for Detecting Prymnesium parvum and Preliminary Results From Gene Expression Studies1

Medlin, Linda K., Sonja Diercks, and Sara Beszteri, 2010. Mini Review: Probes for Detecting Prymnesium parvum and Preliminary Results From Gene Expression Studies. Journal of the American Water Resources Association (JAWRA) 46(1):144-152. DOI: 10.1111/j.1752-1688.2009.00398.x Abstract: Prymnesium parvum is common in brackish and marine coastal waters within temperate zones, world-wide. P. parvum forms recurrent blooms causing fish kills in many parts of the world. Harmful blooms are formed in nutrient rich, low salinity lakes, ponds, river systems, or estuaries. Probes made to this species and to the genus Prymnesium have been tested in dot blot and fluorescent in situ hybridization (FISH) and used with a solid-phase cytometer using a tyramide signal amplification (TSA) enhanced FISH hybridization to provide a means to identify the cells before blooms develop with automated counting. Field counts from light microscopy have been compared with solid-phase cytometer counts. Additional detection systems, such as biosensors and microarrays, have also been developed to identify rapidly this species without resorting to electron microscopy. Advantages and disadvantages of each detection system are discussed. A study of the genes expressed by P. parvum under environmental conditions that can induce blooms or stress was undertaken to try to understand the ecology behind toxic blooms.