Development And Realization Of Real - Time Monitoring And Forecasting System For Water Quality Of Dianchi Lake Based On GIS And

In recent decades, the rapid development of economy and continuous expansion of the city in Kunming, which largely increased the ecological pressure on Dianchi Lake Basin. The reasons of deterioration of the water environment in Dianchi Lake in addition to are related to the objective of factor in economic development, but also with unscientific monitoring and management of the water environment. The traditional water quality monitoring primarily rely on manual water sampling and laboratory analysis which need high cost and cannot achieve real-time monitoring of water quality indicators. This methods are difficult to predict the dynamic changes in water quality. Water quality monitoring results stored in tables is not conducive to data management. It underuse the powerful spatial analysis and a graphical interface of the GIS. The GIS uses maps and charts scientifically, comprehensively, intuitively and real-time to reflect the water quality.Development of wireless sensor networks(WSN) technology provided technical support for real-time online monitoring of water quality indicators in Dianchi Lake. Integrated with sensors of water quality indicators as the sensing unit can obtain quantitative composition of water quality indicators in Dianchi Lake. The GPS module of data communication part can achieve spatial and temporal management of each node, because it can obtain the location of nodes and implement time synchronization between nodes to nodes through its timing capabilities. The GPRS module sends information to the monitoring center, a two-way data communication, each node can only transmit the monitoring data to the monitoring center, the monitoring center can also change the characteristics of each node through remote commands, such as sampling cycle. Geographic Information Systems(GIS) has a rich graphical interface, query and statistics, management and sharing of spatial data, spatial analysis and so on. When monitor water quality in Dianchi Lake, we can take advantage of GIS technology to manage and reflect the state of water quality. Therefore, this paper combine GIS and WSN to achieve real-time online monitoring the water quality in Dianchi Lake.By analyzing the characteristics of the major open-source GIS software, this paper use DotSpatial provided by MapWindow as the development kit of the system of real-time monitoring and forecasting water quality in Dianchi Lake. In the Microsoft Visual Studio development environment, using C # language to secondary develop the open source components DotSpatial, and combined with geographic information technology, multi-functional wireless sensor network technology, GPRS wireless communication technology, GPS global satellite positioning technology, MongoDB database technology and MATLAB simulation to design and implement the system of real-time monitoring and forecasting water quality in Dianchi Lake. The system can real-time monitor water quality indicators such as chlorophyll a, dissolved oxygen(DO), PH value, conductivity(EC), turbidity, temperature. It also can forecast the change trends of water bloom in Dianchi Lake by put the monitoring data of the water quality indicators into the prediction model. The water bloom prediction model of the system is the use of gray metabolic GM(1,1) algorithm for preliminary forecast of various water quality indicators, and then use the BP neural network sequence to nonlinear compensation sequence, in order to establish GM(1,1)-BP neural network water bloom prediction model. In this model, it effectively combines the information required less of gray modeling and the advantages of nonlinear predictive neural networks, which overcomes shortcomings such as the low accuracy of GM(1,1) and training data required of BP neural network. The model achieve the Short-term prediction of the outbreak of water bloom in Dianchi Lake. Combined with GIS spatial analysis functions to achieve temporal and spatial expression of water quality monitoring and prediction, which provides data and technical support for monitoring water pollution emergencies, forecasting, early warning, assessment and emergency response.