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Drought disaster weather prediction method based on semi-supervised ensemble learning

An integrated learning and weather prediction technology, applied in climate sustainability, instrumentation, design optimization/simulation, etc., can solve problems such as low confidence level, poor generalization performance, and slow training speed, so as to improve training efficiency and improve The effect on generalization performance

Pending Publication Date: 2022-08-02
SHENYANG POLYTECHNIC UNIV
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Problems solved by technology

[0005] The present invention proposes a drought disaster weather prediction method based on semi-supervised integrated learning, and its purpose is to solve the problems of low confidence level, slow training speed and poor generalization performance in the current drought disaster weather prediction based on semi-supervised integrated learning

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  • Drought disaster weather prediction method based on semi-supervised ensemble learning
  • Drought disaster weather prediction method based on semi-supervised ensemble learning
  • Drought disaster weather prediction method based on semi-supervised ensemble learning

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Embodiment Construction

[0035] A method for forecasting drought disaster weather based on semi-supervised ensemble learning, characterized by comprising the following steps:

[0036]Step (1): establish a data sample set for predicting drought disaster weather, specifically including WS10M_MIN minimum wind speed of 10 meters (m / s), QV2M 2 meters of relative humidity (g / kg), T2M_RANGE 2 meters of temperature range ( ℃), WS10M wind speed 10 meters (m / s), T2M 2 meters temperature (℃), WS50M_MIN 50 meters minimum wind speed (m / s), T2M_MAX 2 meters maximum temperature (℃), WS50M 50 meters wind speed (m / s), TS Earth skin temperature (°C), WS50M_RANGE wind speed range at 50 meters (m / s), WS50M_MAX maximum wind speed at 50 meters (m / s), WS10M_MAX maximum wind speed at 10 meters (m / s) ), WS10M_RANGE wind speed range at 10 meters (m / s), PS surface pressure (kPa), T2MDEW 2 meters dew point / freezing point (℃), T2M_MIN 2 meters minimum temperature (℃), T2MWET 2 meters wet bulb temperature (℃) ), PRECTOT precipita...

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Abstract

The invention relates to the technical field of semi-supervised ensemble learning, in particular to a drought disaster weather prediction method based on semi-supervised ensemble learning, and the method comprises the steps: constructing a dynamic selection sample data algorithm in a self-training-based semi-supervised learning algorithm; according to the method, the sample data can be dynamically selected according to the confidence level of the model for predicting the unlabeled data in the sample training process, and the model training efficiency is effectively improved while high accuracy is ensured. According to the selective integrated learning pruning algorithm based on target maximization cuckoo optimization, the precision of the base learners and the difference between the base learners can be met to the maximum extent, multi-angle selective integration is adopted, the generalization performance is better, the algorithm operation efficiency is higher, and the model precision is higher. The method can be used for predicting drought disaster weather in the meteorological field.

Description

technical field [0001] The invention relates to the field of semi-supervised and integrated learning, in particular to a method for predicting drought disaster weather based on semi-supervised integrated learning. Background technique [0002] In meteorological disasters, dry weather has a serious impact on the natural environment, human life and social economy. Effective prediction of meteorological drought plays an important role in the natural resource conditions of the basin, the planning and management of regional water resources, and alleviating the harmful effects of drought. It helps relevant departments to optimize the operation of the water resources system, and to do a good job in the corresponding drought prevention and disaster mitigation measures and decision analysis. Therefore, how to make the drought disaster weather forecast more objective, quantitative and accurate has become a crucial factor in the drought disaster weather forecasting business. Important ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06F119/02
CPCG06F30/27G06F2119/02Y02A90/10
Inventor 段勇王鑫炎
Owner SHENYANG POLYTECHNIC UNIV
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