Establishing method of uncertainty mid-term and long-term hydrological forecasting model

Abstract

The invention discloses an establishing method of an uncertainty mid-term and long-term hydrological forecasting model, comprising the following steps: using a wavelet analysis (WA), an artificial neural network (ANN) and a hydrological frequency analysis (HFA) in combination to establish the uncertainty mid-term and long-term hydrological forecasting model; dividing the original sequence into two sections of a main sequence and a random sequence on the basis that WA is used to reveal multiple time dimension variation characteristics of the hydrological sequences, adopting ANN for analogue forecasting on the main sequence and hydrological frequency analysis on the random sequence and overlapping the results of the two sections to be a final forecasting value. The model is used for the mid-term and long-term hydrological forecasting in the Yellow River estuary area, and compared with the traditional method, the results show that the model can reveal the time and frequency structures and variation characteristics of the sequences, has high forecast value result precision and acceptance rate, can quantitatively analyze and describe the impact of hydrological uncertain factors on the forecasting result and can obtain the analogue forecasting value of different frequencies to corresponding hydrological sequences.

Description

technical field [0001] The invention relates to a modeling method, in particular to a method for establishing an uncertain medium and long-term hydrological forecast model. Background technique [0002] It is of great significance to realize the rational utilization and management of water resources by studying the hydrological characteristics and changing laws of river basins and regions, and carrying out hydrological forecasting, especially medium- and long-term hydrological forecasting, so as to grasp the future hydrological situation. In today's deteriorating water resources situation, the importance of medium and long-term hydrological forecasting is more prominent. At present, there are many forecast models in practical application, but there are still many problems, and there are few related research reports in recent years, and the research lags behind the actual production needs. Therefore, it is still of great theoretical and practical significance to study medium...

AI Technical Summary

Problems solved by technology

However, due to the influence of many uncertain factors in the hydrological cycle process, the composition and change characteristics of the hydrological time series are complex and changeable. In addition, the climate conditions are constantly changing, and at the same time, the conditions of the underlying surface are also undergoing huge changes due to the influence of human activities. , the characteristics of hydrological sequence changes are more complex and changeable

Therefore, it is still very difficult to carry out medium and long-term hydrological forecasting

[0004] From a comprehensive analysis, it can be seen that the current traditional forecasting methods mainly have the following two problems: First, each method has its own limitations, so a single method cannot achieve good forecasting purposes, and it is necessary to explore the advantages and disadvantages of multiple methods. joint use

Although ANN has many advantages over traditional time series analysis methods, it is similar to a "black box model". Many parameters cannot be determined, and the simulated hydrological sequence structure cannot be known in detail, and the calculation results cannot be explained; and the factors that affect the topology of ANN There are many, and the imperfect development of parameter optimization theory also restricts the advantages of the ANN model, making it encounter certain difficulties in application and promotion. At present, the neural network is only a purely technical research method; second, the current forecasting method is very poor. It is unreasonable to consider less the impact of hydrological uncertainties on the forecast results, and still use the deterministic model as the main one, and the forecast results obtained are a single definite value

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