Study On Process Mechanism Of Runoff Forecasting And System Integration Implementation

Runoff forecast is a basic problem in hydrology and water resources.Under the influence of climate change and human activities,the degree of uncertainty of rainfall,evaporation and underlying surface conditions is increased.Since the poor consistency,complexity,non-linearity and dynamic of runoff series,the traditional forecasting model method is challenged,and the operational innovation with strong adaptability is urgently needed.In view of the adaptability of uncertainty and dynamic changes in runoff forecasting process,it is more important to study the prediction model under the process mechanism based on the process and strengthening mechanism.Based on the characteristics of response to dynamic changes,focusing on rolling feedback,multi-scale nesting,interval,adaptive mechanism,from the analysis of runoff series characteristics to the integration and application of forecasting model methods and mechanisms,the dynamic mechanism of process is explored and designed,and the theme oriented and process oriented ru noff dynamic prediction model is realized with the help of integrated service platform.Based on the dynamic mechanism of runoff forecast and application-oriented,the following work had been carried out in this paper:(1)The variation characteristics of runoff series was analyzed and revealed for laying a foundation of runoff prediction.According to the trend of runoff evolution in Wei River Basin,the multi-dimensional analysis was carried out on the temporal and spatial scales.The accuracy and integrity of the runoff data wrer ensured by proofreading,"three characteristics" review and interpolation extension.P-III curve was used to calculate and fit the optimal parameters of annual runoff.The characteristics of runoff time series in Wei River Basin were analyzed from the aspects of annual variation,interannual variation,intergenerational variation,cycle identification and variation diagnosis.(2)The process mechanism of runoff forecast was put forward,and a new model was strengthened to guide and carry out runoff forecast with mechanism in the operational process of forecast.In view of the complex nonlinear,highly irregular and multi-scale variability of runoff time series,based on the dynamic process of runoff forecast process of "forecasting,decision-making,implementation,reforecasting,redecision-making and reimplementation",the influence of environment,demand and condition on runoff forecast was analyzed in depth,in order to innovate runoff forecast model,improve forecast method and improve the forecasting accuracy.Based on the dynamic system identification theory,feedback control principle and rolling optimization principle,in the process of runoff forecasting,the mechanism was adapted to the dynamic changes in the process of runoff prediction,and was integrated into the process of runoff forecast.The integrated mechanism covering rolling feedback,multi-scale nesting and interval adaptability was designed.(3)The system implementation of runoff forecast process mechanism based on integrated service platform was carried out.Based on the platform,supported by component technology and knowledge map technology,runoff forecasting models and methods were classified and granulated according to different time scales and applicable conditions.Java programming technology was used to realize component-based,and encapsulated into Web service components,which were published to the component library of runoff forecasting model.On the platform,according to the logic of knowledge graph,the application organization and operation oriented to the theme were completed,and the business application system of runoff forecast process mechanism was built.(4)The process mechanism of runoff forecast is integrated with the method of decomposition and integration model.Taking the decomposition integration model method as the object,this paper focused on the docking of the model method with the mechanism in the process and the adaptation to the dynamic changes.Based on the process operation of runoff forecast,the dynamic changes of factors were fully considered in the process,and the timely application of decomposition integration model method was guided by process mechanism.At the same time,the model method was also integrated into the process mechanism to illustrate the adaptive process mechanism model of runoff forecast.(5)An example was used to verify the prediction process and effectiveness of the runoff forecast model method under the process mechanism.data preprocessing technology was combined with artificial intelligence model,and then the monthly runoff forecasting model based on VMD-DNN(variational mode decomposition and deep neural network)and daily runoff forecasting model based on VMD-GBRT(variational mode decomposition and gradient boosted regression tree)were established respectively.Finally,taking Weihe River as an example,the forecast results were analyzed in scale.Compared with the traditional hydrological model method,the fast and timely effect of on-line evaluation of forecast indexes and the role of quantitative and qualitative analysis in the process of prediction were illustrated.(6)The integrated application of runoff forecast based on process mechanism was realized.In view of the whole process of runoff forecast,problems were found in the process and solutions were put forward.Through data integration,information integration,component integration,mechanism integration and application integration,around the rolling feedback mechanism,multi-scale nesting mechanism and interval adaptability mechanism,aiming at the dynamic changes,the business integration applications of runoff forecast based on decomposition integration artificial intelligence model,runoff forecast of different time scales and adaptive runoff forecast were carried out.Based on the dynamic characteristics of the system itself,various dynamic changes in the process of prediction were analyzed online.Through continuous feedback and adjustment,the adaptability was generated from the dynamic and the rationality was generated from the adaptability,which strengthens the decision support for the actual runoff forecast.