机译:基于可见光和近红外光谱和机器学习提高土壤有机碳含量预测的准确性
Zhejiang Inst Geol Survey Hangzhou 311203 Peoples R China|Minist Nat Resources Agr Land Ecol Assessment & Rehabil Plain Area Engn Technol Innovat Ctr Hangzhou 311203 Peoples R China|Zhejiang Gongshang Univ Sch Tourism & Urban Rural Planning Hangzhou 310018 Peoples R China;
Zhejiang Inst Geol Survey Hangzhou 311203 Peoples R China|Minist Nat Resources Agr Land Ecol Assessment & Rehabil Plain Area Engn Technol Innovat Ctr Hangzhou 311203 Peoples R China;
Zhejiang Inst Geol Survey Hangzhou 311203 Peoples R China|Minist Nat Resources Agr Land Ecol Assessment & Rehabil Plain Area Engn Technol Innovat Ctr Hangzhou 311203 Peoples R China;
11Th Geol Team Zhejiang Prov Wenzhou 325006 Peoples R China;
Zhejiang Univ Finance & Econ Inst Land & Urban Rural Dev Hangzhou 310018 Peoples R China;
Vis-NIR spectroscopy; Wavelet neural network; Support vector machine; Machine learning; Soil organic carbon;
机译:基于数分阶衍生物和内存的学习算法的组合,通过可见和近红外光谱提高土壤有机物估计精度
机译:基于地面可见近红外反射光谱法的土壤有机碳预测精度
机译:人工神经网络与偏最小二乘法的比较,使用可见和近红外光谱法预测不同水分含量下的土壤有机碳和pH
机译:基于最小二乘支持向量机的可见-近红外光谱法预测胶州湾海滩沉积物中有机碳含量
机译:通过从Cortez Hills地下矿井粉碎矿石样品的可见近红外(VNIR)光谱通过粉碎近红外(VNIR)光谱预测金恢复
机译:将辅助土壤数据与VisNIR光谱相结合以改善对土壤有机碳和无机碳含量的预测
机译:利用可见光和近红外光谱比较人工神经网络和偏最小二乘法预测不同含水量土壤有机碳和pH值