CAJ | 학술논문

土壤适宜性评价是获取土地质量状况的重要手段，可为土地科学规划、管理、科学决策提供重要依据。鉴于此，将支持向量机理论引入土壤评价领域，提出一个全新的土壤适宜性评价模型，为了提高评价精度，针对人为选择惩罚系数（C）、核函数参数（σ）的随机性，利用粒子群算法（PSO ）对其进行优化，构建了PSO SVM 模型，SVM 模型采用径向基函数（RBF）作为核函数。以溪洛渡水电站嘎勒移民安置区为例，利用PSO SVM 模型对土壤适宜性进行评价，同时与BP神经网络、普通SVM 模型进行比较。结果表明：PSO SVM 算法明显提高了分类正确率，结果优于BP神经网络和普通SVM ，能更好地反映土壤适宜性，可见，PSO SVM 是一种高精度的土壤适宜性评价模型。
토양괄의성평개시획취토지질량상황적중요수단，가위토지과학규화、관리、과학결책제공중요의거。감우차，장지지향량궤이론인입토양평개영역，제출일개전신적토양괄의성평개모형，위료제고평개정도，침대인위선택징벌계수（C）、핵함수삼수（σ）적수궤성，이용입자군산법（PSO ）대기진행우화，구건료PSO SVM 모형，SVM 모형채용경향기함수（RBF）작위핵함수。이계락도수전참알륵이민안치구위례，이용PSO SVM 모형대토양괄의성진행평개，동시여BP신경망락、보통SVM 모형진행비교。결과표명：PSO SVM 산법명현제고료분류정학솔，결과우우BP신경망락화보통SVM ，능경호지반영토양괄의성，가견，PSO SVM 시일충고정도적토양괄의성평개모형。
Soil suitability evaluation is an important means to obtain land quality status ,and can provide important basis for the land planning ,management and decision-making .In this paper ,the support vector machine (SVM ) theory is introduced to the field of soil suitability evaluation .Ai-ming at the randomness of artificial selection punishment coefficient (C) and kernel function pa-rameter(σ) ,a PSO-SVM model is constructed by using particle swarm optimization (PSO ) to achieve higher evaluation accuracy .The radial basis function (RBF) is used as the kernel function in SVM model .Based on the model construction ,Xiluodu Hydropower Station Gullah Resettlement Area was chosen as an example ;the PSO-SVM model was applied to evaluate soil suitability ,and was compared with BP neural network and normal SVM model .The results showed that PSO-SVM significantly improved the accuracy of soil classification ,compared with BP neural network and the SVM model .Therefore ,PSO-SVM is a high-precision soil suitability evaluation model .