water quality model parameter automatic calibration method based on a PIKAIA genetic algorithm and an OpenMP shared memory model

Abstract

The invention discloses a water quality model automatic calibration parallel computing method based on a PIKAIA genetic algorithm and an OpenMP shared memory model, and belongs to the field of water quality model parameter calibration. The invention relates to a water quality model parameter automatic calibration parallel computing method, in particular to a water quality model parameter automaticcalibration parallel computing method combining a PIKAIA genetic algorithm and an OpenMP shared memory model. The method comprises the following steps: determining a simulation object according to afield investigation result, constructing a water quality parameter optimization automatic calibration parallel computing model by utilizing a PIKAIA genetic algorithm and an OpenMP shared memory model, automatically calibrating the value of a selected parameter to be calibrated, finally selecting an index, and verifying whether the simulation value is consistent with the change trend of an actualmeasurement value or not. By adopting the PIKAIA genetic algorithm and the OpenMP shared memory model at the same time, the advantages of the PIKAIA genetic algorithm and the OpenMP shared memory model are fully utilized, and the water quality model automatic calibration parallel computing method which is short in optimization time and high in adaptability is obtained.

Description

technical field [0001] The invention belongs to the field of water quality model parameter calibration, and specifically relates to a method for automatic calibration and parallel calculation of water quality model parameters combined with a PIKAIA genetic algorithm and an OpenMP shared memory model. Background technique [0002] Computer numerical models have been widely used in water bodies such as rivers, lakes, and reservoirs. Among them, water quality models are often used as one of the important tools for rivers, lakes and reservoirs management. In practical applications, various water quality models require a lot of parameter adjustment ("parameter adjustment") in advance, that is, to continuously adjust various parameters so that the calculated values ​​of the model are most consistent with the measured values, and the most common method of adjusting parameters is It is a manual trial and error method, which is time-consuming and difficult to reach the optimal value,...

AI Technical Summary

Problems solved by technology

In practical applications, various water quality models require a lot of parameter adjustment ("parameter adjustment") in advance, that is, to continuously adjust various parameters so that the calculated values ​​of the model are most consistent with the measured values, and the most common method of adjusting parameters is It is a manual trial and error method, which is time-consuming and difficult to achieve the optimal value, and has many disadvantages

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