Study On Theories And Methods Of Return Water Volume Prediction In Large Irrigation Area

The return water widely exists in the large irrigation area of our country. It is mainly composed of the abandoned water of field, return water of diversion ditch, drainage of ground water, precipitation, flash floods, industrial wastewater and domestic sewage. The return water volume of northwest china is nearly 200 billion m3, and it even accounting for 40% -60% of annual diversion. in some irrigation area. The study on return water has great significance not only to the irrigation area, but also to water resources management of whole district. However there is only little research of theories and methods regarding return water volume, and the research is not enough due to the composition and the spatial and temporal distribution of return water is very complex. Utilizing the approach combining the theoretical methods and application example, taking Qingtongxia irrigation area as example, the paper analyses the variation regularity of return water of irrigation area, ascertains the composition and main influencing factors of return water, classifies the return water into three different types: annual return water, monthly return water and daily return water, and researches the theory and method of predicting return water of irrigation area. Among the key findings:(1)The paper reveals the variation regularity of water and researches the reason of generating a great deal of return water in irrigated area by analyses of long historical observation data of Ningxia Qingtongxia irrigation area,,(2)The paper reveals the variation regularity of return water in multi-years and single-year, and ascertains the main influencing factors of return water using methods of the grey relational degree analysis and correlation analysis; they are diversion, precipitation and groundwater table.(3)The paper establishes the multiple stepwise regression methods of predicting annual return water of irrigated area. There are obvious correlation between return water and itseffective factors, but the modeling samples are few. The multiple regression model is simple in structure and clear in physical meaning, and no more requirement for sample. The predicting precision of annual return water model is less than 7%.(4)The paper finds that the monthly return water of irrigation area changes in a certain periodicity, however, the random variation of the change is large. It is difficult to simulate using traditional numerical analysis methods. The paper advances the neural network method of predicting monthly return water of irrigation area taking advantage of strong simulation capabilities and online learning ability of neural network. In addition, the papers compares the various neural network algorithm, and gives the suggestions for improving training method contraposing the disadvantage of BP neural network: slow training speed, more converging to the local minimum and over-training. The case analysis shows that improved method can overcome those three disadvantages and advance the training speed and precision.(5)It need a large number of training samples to ensure the neural network to achieve good extrapolation capability and predicting results. But in many irrigation area, there are shorter data sequences of return water and few training samples, can not meet the requirement of establishing neural network model. Therefore, this paper introduces the Support Vector Machine (SVM) method which is based on the principle of structure risk minimization and fit to establishing model with few samples, advances the SVM method of predicting monthly return water of irrigation area, discusses the training parameters methods of SVM method, and compares it with the predicting method based on neural network.The case application shows that the prediction ability of SVM model is better than neural network model.when the sample is enough.(6)The paper finds the nonstationarity of time-series and stationarity of first-order difference of return water of Qingtongxia irrigation area by studying the time series of daily return water of the irrigation area. The paper advances the time-series methods of predicting daily return water and discusses the information improvement methods of the time-series predicting methods of daily return water. The case application shows that the modle can simulate the change regularity of diary return water well, and the average relative error of the predicted results is 7.88%.(7)By carrying chaotic identification and phase space reconstruction on daily return water of irrigation area. The papers finds the chaotic character of daily return water of irrigation area, ascertains the time delay, embedment dimension and correlation dimension of phase spacereconstruction, then paper combines the phase space reconstruction theory and neural network methods, advances the chaos-neural network predicting method of daily return water, and compares it with the time-series predicting methods of daily return water.The cases appliation shows that the chaos-neural network modle is more accurate than the times series modle,but it is also more complex in modle model building and application.