The Construction Of DSS In Water Quality Management Of Intensive Litopenaeus Vannamei Shrimp Tanks

The intensification of aquaculture has brought substantial economic benefits, aswell as promoted waste production and disease outbreak. In recent years, deterioratingwater quality has caused massive financial losses to farmers, and has become one ofthe major bottlenecks to production output. For aquaculture enterprises, consideringthe innumerable and complicated variations in water quality, monitoring programsand the reliable estimation of water quality play important roles in culturemanagement to provide a thorough understanding of the degree of contamination andto limit its effect. The data were collected and analyzed, which was the presentcontext of water quality management. Traditional approaches for water qualitymanagement do not provide a comprehensive view of overall water quality. By thetime we’d scoped out the problem, it was too late. It requests that the water qualitymanagement must change the pattern of the traditional management and make thealternative into the way of the real-time monitoring, current situation evaluation andin time alarming.In the present study, the water quality of intensive culture tanks of L. vannameiwas analyzed. Firstly, on the basis of analyzing the weekly values of water qualityvariables measured in the shrimp farm, the intensive culture water quality assessmentmodel was established. It was a practical tool for fast and easy data interpretation, andits application in monitoring the quality of the water sources is recommended for themanagement of shrimp farming or other production activities. Secondly, The ANN model is built for forecasting of shrimp water quality in intensive culture tanks. Themodel can describe complex nonlinear effects between water quality variables andwater quality. Thirdly, the early warning model was established. The present studycombined single-factor warning model with multi-factor warning models. Finally, thedecision support system was achieved. The main results are as follows:1. The water characteristics of the intensive shrimp cultureThe present study investigated the characteristics of water quality parametersrelated to shrimp water quality. Eleven different water quality parameters wereanalyzed during the experimental period. A stepwise multiple regression model wasused to identify the significant correlation among water quality parameters. The resultshowed that the correlation between the water quality parameter and the otherparameters was studied quantitatively the quantitative formula was fitted. In addition,compared with the multiple linear regression model, stepwise multiple regressionmodel could identify the main variables and interference variables, improved thecredibility and reliability of model.2. Water quality assessment model for intensive shrimp tanksIn the present study, the water quality of intensive culture tanks of L. vannameiwas evaluated using factor analysis model. According to the weekly values of waterquality variables measured in the shrimp farm and fisheries water quality standards athome and abroad, the water quality criteria of Litopenaeus vannamei for intensiveshrimp tanks were determined. The source identification indicated that the variablesresponsible for water quality deterioration in the intensive culture shrimp tanks weremainly related to organic matter, natural condition, and nutrient. The nine waterquality variables remained were chosen and the final equation waszF=W₁*F₁-W₂*_F2-¨¨-W_n*F_n.In summary, the model was a practical toolfor fast and easy data interpretation, and its application in monitoring the quality of the water sources is recommended for the management of shrimp farming or otherproduction activities.3. Water quality forecasting model for intensive shrimp tanksWe used a backpropagation neural network （BP-NN） model to predict the waterquality in intensive Litopenaeus vannamei shrimp tanks. It was developed usingmeasured water quality data that were generated over120days with weeklymonitoring in four different shrimp tanks. Nine parameters were selected as inputvariables: water temperature, pH, total ammonia nitrogen, nitrite nitrogen, nitratenitrogen, dissolved inorganic phosphorus, chlorophyll-a, chemical oxygen demand,and five-day biochemical oxygen demand. The model has a tan-sigmoid transferfunction for the hidden layer and a linear transfer function for the output layer. TheLevenberg–Marquardt algorithm was used to overcome the shortcomings of thetraditional BP algorithm; that is, low computational power and getting stuck in localminima. The number of hidden layer nodes was optimized by a trial and errorapproach, and seven optimal neuron nodes were identified. The computed results forwater quality show good agreement with the experimental values. The correlationcoefficient of the data set is0.9921. The simulation results reveal that the BP-NNmodel efficiently predicts the water quality in intensive shrimp tanks.4. Water quality early warning model for intensive shrimp tanksThe single-factor and multi-factor early warning model were established on thebasis of the characteristics of water quality parameters. After the level of warning hasbeen identified, the single-factor early warning model was established. The modelbased on the water quality criteria of Litopenaeus vannamei for intensive shrimp tanks.In addition, we were modeling the single-factor early warning model based on thestepwise multiple regression. The multi-factor early warning model consisted of twoparts: status early-warning and trending early-warning. The status early-warning established on the basis of the water quality assessment model. The final equationwas=W₁×F₁-W₂×_F2-¨¨-W_n×F_n. The trending early-warning established onthe basis of the water quality forecasting model. All available variables were selectedas input variables. The model has a tan-sigmoid transfer function for the hidden layerand a linear transfer function for the output layer. The Levenberg-Marquardtalgorithm was used to overcome the limitations of the traditional BP algorithm. Thenumber of hidden layer nodes was optimized by trial and error. The computed resultsfor water quality were in good agreement with the experimental values. Thecorrelation coefficient （R2） of the data set was0.991. The simulation results revealthat the BP-NN model efficiently predicts the water quality in intensive shrimp tanks.5. The application of decision support system in water quality managementBased on the above models, the decision support system in water qualitymanagement was designd and established. The paper used supplement decisionsupport which based on model. After the framework and running structure weredetermined, we show several examples of how the DSS is used to water qualityassessment and alarming.