Mining And Identifying Technology Of River Runoff Change Law

As a main link of water cycle, river runoff is the most important base for comprehensive development and utilization, scientific management and optimal operation. However, river runoff is affected by human activity and global climate change, so it is very important theoretical and practical sense for mining and identifying change law of the river runoff.In this paper, combined with the National Natural Science Foundation of China, the relation are analyzed between annual runoff and its factors, new methods are introduced for mining and identifying change law of river runoff, and similarity, periodicity and sequential patterns of runoff series are studied, which provide theoretical base for mining change law of river runoff. The analysis and calculation are conducted in combination with a real example, the watershed above the Lanzhou in the upper reaches of the Yellow River. The main research content and results are as follows:(1) Factors of river runoff change are interactivity and interdependency, and their mechanism of action is very complex. Limited by the present science-technology level, one or some factor is only analyzed, which is of one-sidedness. Moreover, qualitative discussion difficultly meets practical application need, so this paper analyses influenceand relation among factors, comprehensive effect of some factor, and quantitatively calculates some of factors.(2) Ant colony optimization (ACO) algorithm is firstly introduced the field of the runoff change analysis. Cluster analysis model is established based on ACO, by which some runoff factors are clustered. Therefore, runoff series is mined by means of similarity search.(3) Periodicity of runoff is calculated by maximum entropy spectrum analysis and Hilbert-Huang spectrum, the result shows some periodicities of runoff change and its cause in the upper reaches of the Yellow River. Hilbert-Huang transform is a new method for analyzing nonlinear and non-stationary data. This method is applied to mine and identify runoff series in this paper, by which runoff series is decomposed as various scale components and trend. These components are usually of clearer physical sense in comparison with classical method, so it provides a kind of effective way for analyzing runoff series.(4) This paper introduces a kind of model for forecasting runoff, life cycle model. Long-term trend of runoff change is forecast by it in the upper reaches of the Yellow River. This model considers that water resource is finite and runoff process is non-linear, and is a search for runoff forecasting.(5) Because runoff is affected by many factors, runoff process shows strong uncertainty and randomness. Forecasting model of river runoff is established based on grey theory. Moreover, three kinds of model is applied to forecast annual runoff in the upper reaches of the Yellow River, in which GM(1,1) is basic forecasting model, its forecasting result is not good, but may better reflect change trend of runoff. Grey topological model can realize waveform forecasting, however, forecasting precision is lower. Grey Markov chain model is based on the advantage of both GM(1,1) and Markov chain model, which may describe random fluctuating of runoff series, and improves the forecasting precision, so this is a kind of new and good search. It providesimportant reference value for real application.