本文關(guān)鍵詞： 煤矸石充填復(fù)墾 土壤重金屬含量 高光譜 SMLR PLSR ANN　出處：《光譜學(xué)與光譜分析》2017年12期 　論文類型：期刊論文

【摘要】：為研究煤矸石充填復(fù)墾土壤重金屬含量快速有效的監(jiān)測(cè)方法,以淮南創(chuàng)大生態(tài)園煤矸石充填復(fù)墾田間試驗(yàn)小區(qū)為研究區(qū)域,首先采用化學(xué)方法監(jiān)測(cè)土壤(0~20cm)重金屬(Cu,Cr,As)含量,然后采用ASD(analytical spectral devices)FiSpec4型高光譜儀測(cè)量土壤樣品的反射光譜,提取光譜特征,并對(duì)光譜進(jìn)行一階微分變換、二階微分變換及倒數(shù)對(duì)數(shù)變換;將變換后的各光譜特征參數(shù)與監(jiān)測(cè)的土壤重金屬含量進(jìn)行相關(guān)性分析,并依據(jù)相關(guān)性分析結(jié)果選擇顯著相關(guān)的波段作為相關(guān)因子供建模使用。采用多元逐步回歸(stepwise multiple liner regression,SMLR)分析、偏最小二乘回歸(partial least squares regression,PLSR)及人工神經(jīng)網(wǎng)絡(luò)(artificial neural network,ANN)三種方法分別建立基于光譜反射率估算土壤重金屬含量的預(yù)測(cè)模型,并采用回歸模型進(jìn)行精度評(píng)定,然后確定各重金屬含量的最佳預(yù)測(cè)模型。實(shí)驗(yàn)結(jié)果表明,經(jīng)過(guò)微分變換的光譜波段與土壤重金屬含量達(dá)到了顯著相關(guān);重金屬Cu和Cr的一階微分光譜的人工神經(jīng)網(wǎng)絡(luò)模型為最佳預(yù)測(cè)模型,重金屬元素As的二階微分光譜的偏最小二乘回歸模型為最佳預(yù)測(cè)模型。
[Abstract]:In order to study the rapid and effective monitoring method of heavy metal content in reclaimed soil with coal gangue filling, the chemical method was used to monitor the content of the heavy metal Cu (Cr) in the soil (20 cm) in Huainan Chuangda Ecological Park, taking the field experiment area of coal gangue filling reclamation as the research area. Then, the reflectance spectra of soil samples were measured by ASD(analytical spectral devices)FiSpec4 type high spectrometer, and the spectral characteristics were extracted, and the spectrum was transformed by first-order differential transformation, second-order differential transformation and reciprocal logarithmic transformation. The correlation analysis was carried out between the transformed spectral characteristic parameters and the monitored soil heavy metal content, and the significant correlation bands were selected as the correlation factors according to the results of the correlation analysis. The stepwise multiple liner regression analysis was used for modeling. Three methods, partial least squares regression (PLSR) and artificial neural Network (Ann), were used to establish prediction models for estimating soil heavy metal content based on spectral reflectance, and the precision was evaluated by regression model. The experimental results show that the spectral band of differential transformation has significant correlation with the content of heavy metals in soil. The artificial neural network model for the first order differential spectra of heavy metals Cu and Cr is the best prediction model, and the partial least square regression model for the second order differential spectra of heavy metal elements as is the best prediction model.
【作者單位】： 安徽理工大學(xué)測(cè)繪學(xué)院;中國(guó)礦業(yè)大學(xué)測(cè)繪科學(xué)與技術(shù)博士后流動(dòng)站;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(41472323) 安徽省國(guó)土資源科技項(xiàng)目(2012-k-23)資助
【分類號(hào)】：O657.3;X833

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