Selection of Water Transmission Method and Analysis of Pipe Network Zoning in Municipal Water Supply and Drainage Design

With the acceleration of urbanization,the demand for water supply and drainage pipe networks has increased significantly.In the planning of urban construction,it is necessary to optimize the design of the water supply and drainage system pipe network to effectively save energy while providing residents with more accessible water resources.Therefore,the municipal water supply and drainage system and the water transmission methods should be designed according to the geographical conditions of the city.In this paper,we mainly analyze the design of municipal water supply and drainage systems and the selection of water transmission methods.Besides,the optimization of the water supply and drainage network zoning process and pipe network maintenance is also discussed,so as to provide a reference for municipal water supply and drainage work.

Integrated assessment of sea water quality based on BP artificial neural network

In order to carry out an integrated assessment of sea water quality objectively, this paper based on the concept and principle of artificial neural network, generated appropriate training samples for BP artificial neural network model through the method of producing samples to the concentration of various pollution index of sea water quality from the viewpoint of threshold, established the BP artificial neural network model of sea water quality assessment using multi-layer neural network with error back-propagation algorithm. This model was used to assess water environment and obtain sea water quality categories of offshore area in Bohai Bay through calculating. The calculations shown that pollution index in river＇s wet season was higher than that in dry season from 2004 to 2007, and the pollution was particularly serious in 2005 and 2006, but a little better in 2007. The assessed results of cases shown that the model was reasonable in design and higher in generalization, meanwhile, it was common, objective and practical to sea water quality assessment.

Application of Dynamic Programming Method in Optimization of Water Distribution Pipe Network

With a water-supply network by dynamic programming. The minimal as an example, the network was optimized annual discounted costs were taken as an objective function and node pressure etc. as constraint conditions. The alternative pipe diameters were optimized as per enumeration method and the group allowing objective function with the least values would be the optimized one. It is proved the optimized pipe network reduced by 11.49% in terms of cost and the optimized ben- efits proved much significant.

Water quality forecast through application of BP neural network at Yuqiao reservoir

This paper deals with the study of a water quality forecast model through application of BP neural network technique and GUI (Graphical User Interfaces) function of MATLAB at Yuqiao reservoir in Tianjin. To overcome the shortcomings of traditional BP algorithm as being slow to converge and easy to reach extreme minimum value,the model adopts LM (Leven-berg-Marquardt) algorithm to achieve a higher speed and a lower error rate. When factors affecting the study object are identified,the reservoir's 2005 measured values are used as sample data to test the model. The number of neurons and the type of transfer functions in the hidden layer of the neural network are changed from time to time to achieve the best forecast results. Through simulation testing the model shows high efficiency in forecasting the water quality of the reservoir.

Artificial neural network modeling of water quality of the Yangtze River system:a case study in reaches crossing the city of Chongqing

An effective approach for describing complicated water quality processes is very important for river water quality management. We built two artificial neural network(ANN) models,a feed-forward back-propagation(BP) model and a radial basis function(RBF) model,to simulate the water quality of the Yangtze and Jialing Rivers in reaches crossing the city of Chongqing,P. R. China. Our models used the historical monitoring data of biological oxygen demand,dissolved oxygen,ammonia,oil and volatile phenolic compounds. Comparison with the one-dimensional traditional water quality model suggest that both BP and RBF models are superior; their higher accuracy and better goodness-of-fit indicate that the ANN calculation of water quality agrees better with measurement. It is demonstrated that ANN modeling can be a tool for estimating the water quality of the Yangtze River. Of the two ANN models,the RBF model calculates with a smaller mean error,but a larger root mean square error. More effort to identify out the causes of these differences would help optimize the structures of neural network water-quality models.

Application of Artificial Neural Networks for the Prediction of Water Quality Variables in the Nile Delta

The quality of a water body is usually characterized by sets of physical, chemical, and biological parameters, which are mutually interrelated. Since August 1997, monthly records of 33 parameters, monitored at 102 locations on the Nile Delta drainage system, are stored in a National Database operated by the Drainage Research Institute (DRI). Correlation patterns may be found between water quantity and water quality parameters at the same location, or among water quality parameters within a monitoring location or among locations. Serial correlation is also detected in water quality variables. Through the investigation of the level of information redundancy, assessment and redesign of water quality monitoring network aim to improve the overall network efficiency and cost effectiveness. In this study, the potential of the Artificial Neural Network (ANN) on simulating interrelation between water quality parameters is examined. Several ANN inputs, structures and training possibilities are assessed and the best ANN model and modeling procedure is selected. The prediction capabilities of the ANN are compared with the linear regression models with autocorrelated residuals, usually used for this purpose. It is concluded that the ANN models are more accurate than the linear regression models having the same inputs and output.

Optimal operation of water distribution networks under local pipe failures

The optimal operation of water distribution networks under local pipe failures, such as water main breaks, was proposed. Based on a hydraulic analysis and a simulation of water distribution networks, a macroscopic model for a network under a local pipe failure was established by the statistical regression. After the operation objectives under a local pipe failure were determined, the optimal operation model was developed and solved by the genetic algorithm. The program was developed and examined by a city distribution network. The optimal operation alternative shows that the electricity cost is saved approximately 11%, the income of the water corporation is increased approximately 5%, and the pressure in the water distribution network is distributed evenly to ensure the network safe operation. Therefore, the proposed method for optimal operation under local pipe failure is feasible and cost-effective.

Comparison of Using an Electronic System and Conventional Monitoring Method for Monitoring the Quality of Drinking Water and Defects Discovery in Rural Area Water Distribution Network of Abarkouh, Iran

The used water for human consumption must be free of microorganisms and chemicals that cause risk in the human health. In this study, water quality of 18 rural area of Abarkouh was determined and compared the conventional monitoring method (According to ISIR (Institute of Standards and Industrial Research of Iran), 1053 and 4208) and use of electronic system method (Patent in industrial property general office of Iran, 77815). Free chlorine monitoring and pH test done by health workers in the conventional method and the results will be sent to the Health Network monthly. Sampling for microbiological testing is done monthly based on population (According to ISIR, 4208). On the electronic system, the procedure is also done by health workers, but the result will be sent to the receiver device by using a cell phone. According to the chlorine test results if the free chlorine residual reported zero, microbiological sampling was done by a health expert. Finally, the number of chlorine test and microbiological sampling and the results of these experiments collected in the both methods and recorded in SPSS 22 then were analyzed by using chi-square test and Fisher exact test. The result of microbiological experiments shows that the sampling rate decreased 29% in using of electronic system method in comparison to the conventional monitoring method while the number of microbial defect detection increased 19% in drinking water networks monitoring by electronic system. Using of electronic system monitoring can reduce the rate and cost of microbiological sampling and its experiments and increase accuracy of these tests, in this way it will increase the quality and safety of drinking water in distribution network in small and dispersed rural communities.

Application of a Neural Network Technique for Prediction of the Water Quality Index in the Dong Nai River, Vietnam

Recent trends in environmental management of water resource have enlarged the demand for predicting techniques that can provide reliable, efficient and accurate water quality. In this case study, the authors applied the Artificial Neural Networks （ANN） to estimate the water quality index on the Dong Nai River flowing through Dong Nai and Binh Duong provinces. The information and data including 10 water quality parameters of the Dong Nai River at 23 monitoring stations were collected during the recorded time period from 2010 to 2014 to build water quality prediction models. The results of the study demonstrated that the Water Quality Index （WQI） forecasted with GRNN was very significant and had high correlation coefficient （R2 = 0.974 and p = 0.0） compared to the real values of the WQI. Moreover, the ANN models provided better predicted values than the multiple regression models did.

Water Management for Improvement of Rice Yield,Appearance Quality and Palatability with High Temperature During Ripening Period

To clarify the optimal water management in large-scale fields under high temperatures at the ripening period,effective water managements during this period for improvement of yield,appearance quality and palatability were investigated.Compared with intermittent irrigation and flooded irrigation,the soil temperature with saturated irrigation remained low throughout the day,and the decrease rate of the bleeding rate of hills was the lowest.These results suggested that the saturated irrigation maintained root activity.For the three irrigation types,the number of spikelets per m2 and 1000-grain weight were similar,however,saturated irrigation resulted in significantly higher rice yield due to improvement in the percentage of ripened grains.The saturated irrigation produced a high percentage of perfect rice grains and thicker brown rice grain,furthermore,the palatability of cooked rice was excellent because protein content and hardness/adhesion ratio were both low.Thus,under high-temperature ripening conditions,soil temperature was lowered and root activity was maintained when applying saturated irrigation after heading time.The results indicated that saturated irrigation is an effective countermeasure against high-temperature ripening damage.

Environmental Anthropological Study of Watershed Management‐Water Quality Conservation of Forest as a Catchment Area in the Southern Part of Australia

Authors have conducted an experiment of irradiation using sound waves (frequency) including ultrasonic waves into water such as drinking water, sea water and forest water and wastewater so far. As a result, almost the same effect of improvement of water quality was confirmed for each sound wave. Then, an environmental anthropological study of watershed management based on the sound was carried out assuming that a water quality management using the sound could be possible. The Goulburn River basin in the southern part of Australia in which indigenous peoples (Yorta Yorta) have been concerned with the management for a long time so far was selected as an objective drainage basin this time. As a result, a couple of environmental anthropological perspectives on watershed management were proposed.

Biological Evaluation Based Quality Management of Water Resources in Small Xingkai Lake

Taking the water ecosystem of Small Xingkai Lake as research object,through the survey and analysis of aquatic organism indicators,this paper established an ecosystem integrity evaluation system with the biological integrity as the criterion layer. Using the index of biological integrity and comprehensive health index,it evaluated the health status of water ecosystem of Small Xingkai Lake.

Development and application of a GIS-based artificial neural network system for water quality prediction: a case study at the Lake Champlain area

Artificial Neural Network(ANN)models have been extensively applied in the prediction of water resource variables,and Geographical Information System(GIS)includes powerful functions to visualize spatial data.In order to provide an efficient tool for environmental assessment and management that combines the advantages of these two modules,a GIS-based ANN water quality prediction system was developed in the present study.The ANN module and ArcGIS Engine module,along with a dynamic database,were imbedded in the system,which integrates water quality prediction via the ANN model and spatial presentation of the model results.The structure of the ANN model could be modified through the graphical user interface to optimize the model performance.The developed system was applied to a real case study for the prediction of the total phosphorus concentration in the Lake Champlain area.The prediction results were verified with the monitoring data,and the performance of the developed model was further evaluated through graphical techniques and quantitative statistical methods.Overall,the developed system provided satisfactory prediction results,and spatial distribution maps of the predicted results were obtained,which coincided with the monitored values.The developed GIS-based ANN water quality prediction system could serve as an efficient tool for engineers and decision makers.

Fish Production, Water Quality and Bacteriological Parameters of Koi Carp Ponds Under Live-food and Manure Based Management Regimes

To test the effectiveness of introducing live zooplankton against direct manuring in ornamental fish ponds upon their survival and production, larvae of koi carp, Cyprinus carpio L., were cultured for 11 weeks in earthen ponds maintained according to four management regimes： （1） live zooplankton fed to carp larvae （LF）; （2） direct fertilization with poultry manure （PM）; （3） direct fertilization with cowdung （CD）; and （4） a control treatment （C）. There were three replicates for each treatment. The growth of heterotrophic bacteria and pathogenic microorganisms like Aeromonas sp. and Pseudomonas sp. were also examined in response to pond management. Values of dissolved oxygen were significantly higher （P〈0.05） in the water of LF ponds, compared to other treatments, while the PM and CD treatments recorded were significantly higher （P〈0.05） values of PO4 - P, NH4 - N, NO3 - N, NO2 - N, specific conductivity, alkalinity, and BOD, compared to the LF and C treatments. The percentages of organic carbon and total nitrogen in the bottom sediments were higher in the PM and CD treatments compared to LF （P〈0.05）. Average counts of heterotrophic bacteria in the water of PM and CD ponds were significantly higher than other treatments （P〈0.05）. The development of Aeromonas sp. and Pseudomonas sp. were significantly higher （P〈0.05） in the PM and CD treatments. Weight gain of koi carp stocked in LF was significantly higher （P〈0.05） than that of fish in the other treatments. There was a significant difference in the survival rate of koi carp among the treatments ranging from 67.21% in C to 90.11% in LF. The results suggest that raising koi carp larvae in ponds and feeding them exogenously with zooplankton would support high rates of survival and production through maintenance of better water quality and greater abundance of zooplankton in the system. Significantly lower abundance of Aeromonas sp. and Pseudomonas sp. in the LF treatment considerably lowered any possibility of occurrence of bacterial disease.

Combined Method of Chaotic Theory and Neural Networks for Water Quality Prediction

Chaos theory was introduced for water quality, prediction, and the model of water quality prediction was established by combining phase space reconstruction theory and BP neural network forecasting method. Through the phase space reconstruction, the one-dimensional water quality time series were mapped to be multi-dimensional sequence, which enriched the spatial information of water quality change and expanded mapping region of training samples of BP neural network. Established model of combining chaos theory and BP neural network were applied to forecast turbidity time series of a certain reservoir. Contrast to BP neural network method, the relative error and the mean squared error of the combined method had all varying degrees of lower. Results indicated the neural network model with chaos theory had the higher prediction accuracy, at the same time, it had better fault-tolerant capability and generalization performance .

An Analysis of the Value of Additional Information Provided by Water Quality Measurement Network

European Community policy concerning water is placing increasing demands on the acquisition of information about the quality of aquatic environments. The cost of this information has led to a reflection on the rationalization of monitoring networks and, therefore, on the economic value of information produced by these networks. The aim of this article is to contribute to this reflection. To do so, we used the Bayesian framework to define the value of additional information in relation to the following three parameters: initial assumptions (prior probabilities) on the states of nature, costs linked to a poor decision (error costs) and accuracy of additional information. We then analyzed the impact of these parameters on this value, particularly the combined role of prior probabilities and error costs that increased or decreased the value of information depending on the initial uncertainty level. We then illustrated the results using a case study of a stream in the Bas-Rhin department in France.

A GIS-Based DSS for Management of Water Distribution Networks (Rafah City as Case Study)

The water problem in Gaza Strip in Palestine is complicated;in reason of consumed water exceed the renewable water and high population density. The water situation is very important for mangers that are responsible of distribution, operation and maintenance of water networks to build an effective and complete comprehensive management of water distribution networks system. The main aim of this research is to study and evaluate the existing operation & maintenance system which is applied in Rafah area in Gaza strip which selected as pilot study area. The proposed system deals with a huge quantity of data which is needed to propose an effective system that can save time, cost, and minimize the mistakes in managing operation & maintenance systems for water distribution networks, Geographic Information System Technology is selected as one of tool decision which deals with such quantity of data and can be linked with other software which can achieve the aim of this study. The research depended on the use of Arc GIS 9.3 datasets and Water CAD/WaterGEMS hydraulic modeling based on Decision Support System for proposed water distribution plan systems, proposed operation & maintenance systems, and use pipe condition index to assist operation & maintenance manager to take the best decision which saving time, effort, and cost.

Impact of the Condition of Drinking Water Supply Networks on the Quality of Water Intended for Consumption: The Case of the Network in the Commune of Daloa (Central West of Côte d’Ivoire)

The potability of drinking water depends not only on the source and the treatment system, but also on the quality of the waterworks. In fact, the quality of drinking water is considerably degraded by the dilapidated state and lack of maintenance of drinking water networks. In Côte d’Ivoire, the majority of drinking water networks in the various towns are ageing. In Daloa, despite the efforts made by the company in charge of water treatment and distribution to make the water drinkable, the water at consumers’ taps is often colored, has an unpleasant aftertaste and settles after collection. As a result, people are concerned about the potability of tap water, and some are turning to alternative sources of drinking water of unknown quality. In order to determine the factors responsible for the deterioration in water color and taste, as well as the sectors of the network most affected, a diagnosis of the network’s equipment was carried out. Water samples taken from the network were analyzed for color and turbidity. The diagnosis revealed that most of the equipment (suction pads, valves, drains and fire hydrants) is outdated and irregularly maintained. Analyses show that the water is more colored in cast-iron and PVC pipes than in asbestos cement pipes. Coloration values in the network range from 0 to 27 UVC for asbestos cement pipes, from 15 to 56 UCV for ductile iron pipes, and from 11 to 102 UCV for PVC pipes. On the over hand, turbidity values vary from 8.02 to 3.32 NTU for ductile cast iron pipes, 8.51 to 16.98 NTU for asbestos cement pipes and 0.9 to 6.98 NTU for PVC pipes. Old cast-iron pipes release ferric ions on contact with water, degrading their color. Old cast-iron pipes release ferric ions into the water, degrading its color. The high color values observed in the vicinity of drains are thought to be due to irregular maintenance of the network. In fact, after network maintenance, a reduction rate ranging from 2% to 73% is observed for turbidity, while for color, the rate varies from 5% to 72%. In short, the network’s obsolescence and irregular maintenance contribute significantly to the deterioration of water quality.

Research on the Present Situation and Improvement Strategy of Water Conservancy Project Construction Quality Management based on the Quality Efficiency Equilibrium Theory

In this paper, we conduct research on the present situation and improvement strategy of the water conservancy project construction quality management based on quality efficiency equilibrium theory. Water conservancy engineering construction enterprises should refer to oneself circumstance, and the characteristics of the water conservancy project, determine the quality objectives of specific quality assurance plan and the related measures, clear implementation content, methods and effects. Strictly control the factors influencing the water conservancy engineering quality of many aspects, in the process of the water conservancy project construction, to establish a meet the requirements of the technical process and quality standards, operation procedures. Establish the strict appraisal system, continuous improvement and improve the level of construction technique and technology. Establish strict quality assurance system and quality responsibility system, each division, subdivisional work will comprehensively implement in place management to ensure the engineering quality.