Optimization method of reservoir dispatching rules based on machine learning fusion of multi-source remote sensing data

Abstract

The invention provides a reservoir dispatching rule optimization method based on machine learning fusion of multi-source remote sensing data, including: collecting short series of runoff observation data of the reservoir, and extracting precipitation series, meteorological data and land water storage series of the watershed where the reservoir is located; according to the short series A series of runoff observation data and meteorological data, establish a hydrological model of the watershed where the reservoir is located and initially simulate the runoff; build a long-term short-term memory neural network model and use it to correct the simulated runoff, and obtain the corrected simulated runoff series; the collected long-term meteorological The data is input into the hydrological model and the corrected simulated runoff system to simulate the long series of inflow runoff process of the reservoir; a multi-objective optimization dispatching model is constructed based on the obtained long series of inflow runoff of the reservoir, and the optimized dispatching rules are solved by genetic algorithm. The invention integrates multi-source remote sensing data for simulating long series of runoff processes, and provides a reference basis for reservoir scheduling and water resource planning.

Description

technical field [0001] The invention belongs to the technical field of reservoir scheduling, and in particular relates to a method for optimizing reservoir scheduling rules based on machine learning fusion of multi-source remote sensing data. Background technique [0002] Hydrological and meteorological data is the basic basis for project planning, design, construction and operation management, and is also an important data for evaluating the flood control risk of water conservancy projects in the basin. However, some reservoirs in my country only have a small amount of measured hydrometeorological monitoring data. Therefore, how to invert a long series of runoff processes and guide the operation and scheduling of reservoirs is a major challenge for hydrologists. [0003] In recent years, satellite telemetry technology and data inversion algorithms have developed rapidly. Precipitation quantitative observation products based on satellite remote sensing inversion have wider c...

AI Technical Summary

Problems solved by technology

[0004] However, the hydrological model is suitable for simulating the runoff process in the natural state. Engineering measures such as dams, reservoirs, agricultural irrigation, water diversion, and inter-basin water transfer often destroy the consistency of the underlying surface, resulting in large errors in the hydrological model of the basin. Restricts the accuracy of hydrological simulation

The existing literature fails to make full use of satellite telemetry meteorological information, fails to consider the error caused by human activity interference on runoff simulation, and cannot solve the long series of runoff simulation problems in areas with scarce data, and is difficult to be used in the application practice of optimizing reservoir dispatching rules

Images