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<p>To solve the low accuracy of traditional landscape form analysis methods, a method combining the firefly algorithm and a self-organizing feature mapping neural network is proposed. Based on this algorithm, a landscape shape-assisted quantization model is designed. The performance of the proposed algorithm is compared with other algorithms. The results showed that the training error and average accuracy of the proposed algorithm were 0.00093 and 97.82%, respectively, which were better than comparison algorithms. The performance analysis experiment of the proposed landscape shape-assisted quantization model showed that the model had a good classification and recognition effect. The proposed landscape form auxiliary quantification model has good practical value, which is conducive to improving the cognition ability of landscape form structure law.</p>

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Design of landscape morphology assisted quantification model based on improved FA-SOM

  • 期刊论文

  • 发表日期：

    2025年4月9日

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  • Hui Chi

摘要

To solve the low accuracy of traditional landscape form analysis methods, a method combining the firefly algorithm and a self-organizing feature mapping neural network is proposed. Based on this algorithm, a landscape shape-assisted quantization model is designed. The performance of the proposed algorithm is compared with other algorithms. The results showed that the training error and average accuracy of the proposed algorithm were 0.00093 and 97.82%, respectively, which were better than comparison algorithms. The performance analysis experiment of the proposed landscape shape-assisted quantization model showed that the model had a good classification and recognition effect. The proposed landscape form auxiliary quantification model has good practical value, which is conducive to improving the cognition ability of landscape form structure law.