Research of Groundwater Environment Early Warning Based on Intelligent Algorithm

Wu, Yuchun; Long, Xiaojian

doi:10.1109/iccsee.2012.321

Cited by 1 publication

(1 citation statement)

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“…The agricultural industry has certain special characteristics in terms of operational risk compared with other industries, and most of the existing studies on the establishment and comparison of financial early warning models for listed agricultural companies are limited to traditional methods (e.g., univariate models, multivariate models, logit models, and artificial neural network models) and rarely use more advanced methods combined with computer networks (e.g., unit learning framework models, rough set models, decision tree models, gradient advancement models, and fuzzy OSVR methods) to conduct empirical studies [14]. For example, [15] used univariate analysis and multivariate analysis to study the financial distress of agricultural listed companies, and [16] conducted a financial warning study on agricultural listed companies based on survival analysis method. [17] illustrated how traditional accounting indicators can be applied to financial early warning of agricultural listed companies.…”

Section: Introductionmentioning

confidence: 99%

Research on Agricultural Economic Early Warning Based on Genetic Algorithm and SVM

Bian

Tian

2022

Journal of Sensors

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Agriculture is unique in that the industry is subject to a certain level of uncertainty due to seasonal and other factors, and its risk level is significantly higher than that of other industries. Therefore, it is necessary to establish an appropriate financial early warning model to predict, analyze, and control its financial risks. The article uses a genetic algorithm and support vector machine-based economic forecasting model for agribusinesses to adapt its own pollutant weights in a practical application environment to improve the forecasting accuracy. This model first uses a genetic algorithm to train a feature weight vector of current data so that the weights are adapted to the current prediction problem and then uses this feature weight vector to apply to SVM model training. It is found that 62.79% of the listed agricultural companies are in warning status, and their development is not optimistic. The article provides new ideas for the subsequent research on financial warning methods and also expands the boundaries of theoretical research for the research system of financial warning problems and enriches the experience and evidence of practical research.

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Section: Introductionmentioning

confidence: 99%