The Study On Data Mining And Applicability Of Model For Predicting Surface Water Quality

In recent years, as China's industrialization and urbanization process accelerated and the environmental pollution increased seriously, emergent environmental pollutions which happen frequently have made an important impact on human's health, ecological security, production and daily life. The drinking water sources in the main stream of Songhua River, which is the main source of drinking water in Heilongjiang Province, have been polluted seriously and it has a significant effect on people's production and life and it need urgently to adopt modern means to achieve the scientific management of the Songhua River Basin.This paper has developed the water quality prediction model based on the Songhua River Basin and this study has a value of theoretical research and practical applications.The water quality data of this paper is collated by using the on-line monitoring system and it will serve as the data source of the training set in the water quality prediction model. In order to ensure the prediction with high prediction accuracy, it needs to preterit the water quality data of the raw water and form to the data of the training set that meets the requirement. This paper separates the data into terms by month, retreats the data by applying cluster analysis method and rejects the abnormal data, so that the effective data can be distributed evenly and improve the prediction accuracy. In addition, it can select the optimal influencing factor by applying the cluster analysis method according to the data distribution between the several influencing factors and the forecasting object and the choice of the factor has a high value to improve the prediction accuracy of the prediction model. This paper also studies the effect to the prediction accuracy after the data pretreatment by the cluster analysis method.This paper separates the water pollution into two types: conventional pollution and sudden pollution. It introduces the artificial neural network technology and applies the MATLAB software to set up the conventional water quality prediction model in the study of the conventional water quality prediction. In the model, the prediction position is in the Sifangtai Station in Harbin, and it takes COD Mn—the main index in the Songhua River Basin water pollution—as the prediction object, determines the CODMn,the water temperature and other four parameter of that day as the influencing factor by the cluster analysis method, and adopts the daily water quality detection data in Sifangtai monitoring station from 1997 to 1999 as the sample set to predict the Codman in the next three days based on the six influencing factors of that day. The prediction model needs to be updating and maintaining continuously for better forecasting effect. In order to improve the precision of the prediction, this paper sets the cluster center point as the initial weight to train the model, and applies the predict model both before applying and after applying to water quality prediction. Then we consider the effect of the application through the comparing research.In the study of the prediction effect in different water quality stages in the same river, it divides the Songhua River into plentiful period, freezing period and other period, and makes researches on the prediction effect in different periods. The results show that the prediction effect varies in different periods, best in the plentiful period, worst in the freezing period and between them in the rest.This paper studies the cross prediction research of the regional water quality prediction model by using the Songhua River prediction model and Yuqiao Reservoir model to predict the water quality in the two places. The prediction mechanism has some commonness for the same object when using different prediction models to forecast the water quality in different rivers and the changes of the water quality in the future has similarities. In some special circumstances, it has a reference role to understand the changes of the water quality in future by using other river prediction models to forecast the water quality in this river. The comparison shows that a prediction model of some river has a better result when predicting the same river and a worse result in other rivers, so using the prediction model of this river forecasts the water quality as possible in practice.Comparing the prediction effect in the Songhua River model and Yuqiao model,the conclusion is that for the rivers with different characteristics, if the water quality changes in the external factors, the conventional water quality prediction model of the river with fast water flow and water update forecasts poor results;conversely,the results will be better.This paper studies the embedded integration technology of the conventional water quality prediction model and unexpected water quality prediction model, compares with the three ways of the current GIS and application analysis model integration, selects the mode of combination of the prediction model and Misuses ActiveX automation technology to solve the multi-objective optimization of the MATLAB and VB integrated, thus realizes the integration of data input and output functions of the water quality model on the GIS platform by using MATLAB function, to forecast the water quality of all the circumstances.This paper introduces the functions of the conventional prediction model in the research of the application of the conventional water quality prediction model, which is applied to forecast the CODMn in Sifangtai monitoring station in Songhua River in August, 2006, studies the prediction effect of the model. The average prediction error is 4.79% within the allowable range, so this model has a guiding significance in the actual water quality management. On the research of the embedded integration of the system, SMS model is chosen as emergent water quality prediction model. And system calls SMS model to form the visual pollution transport model in the end and analyze the surface water quality, velocity and flow patterns and so on.The study on data mining and applicability for prediction model of surface water quality provides the scientific basis for the guiding of the water plant production, for the scientific management and decision making of the surface water environment.