Comparison of artificial neural network and multiple linear regressions efficiency for predicting soil salinity in Yazd -Ardakan plain, central Iran

نویسندگان

1 Combat to desertification, Natural Resource Faculty, Ardakan University, Yazd, Iran

2 Department of soil science, College of Agriculture, Isfahan University of Technology, Isfahan, Iran

3 Department of Soil Science, College of Agriculture, Shahid Chamran University of Ahvaz, Khuzestan, Iran

چکیده

The study was conducted to evaluate the efficacy of artificial neural network (ANN) and multivariate regression (MLR) analysis to predict spatial variability of soil salinity in central Iran, using remotely sensed data. The analysis was based on data acquired from EOS AMI remote sensing satellite. The two methods was used to study linear and non-linear relationship between soil reflectance and soil salinity. In MLR analysis, stepwise method and neural network were applied using sensitivity coefficient by arranging inputs through the backward propagation, and then modeling was done. The R2 and RMSE were 0.23 and 0.33 for MLR, and 0.79 and 0.11 for ANN, respectively. Digital values of VNIR1 and NDVI48 were identified as the most important factors in MLR, whereas Sum19 and SWIR6 were recognized as the most important data to predict soil salinity using ANN. The results indicated that ANN model is used to detect non- linear relationship between soil salinity and ASTER data at the study area.

کلیدواژه‌ها

عنوان مقاله [English]

Comparison of artificial neural network and multiple linear regressions efficiency for predicting soil salinity in Yazd -Ardakan plain, central Iran

نویسندگان [English]

  • Fatemeh Roustaei 1
  • Shayouby Atouby 2
  • Mojtaba Norouzi 3
1 Combat to desertification, Natural Resource Faculty, Ardakan University, Yazd, Iran
2 Department of soil science, College of Agriculture, Isfahan University of Technology, Isfahan, Iran
3 Department of Soil Science, College of Agriculture, Shahid Chamran University of Ahvaz, Khuzestan, Iran
چکیده [English]

The study was conducted to evaluate the efficacy of artificial neural network (ANN) and multivariate regression (MLR) analysis to predict spatial variability of soil salinity in central Iran, using remotely sensed data. The analysis was based on data acquired from EOS AMI remote sensing satellite. The two methods was used to study linear and non-linear relationship between soil reflectance and soil salinity. In MLR analysis, stepwise method and neural network were applied using sensitivity coefficient by arranging inputs through the backward propagation, and then modeling was done. The R2 and RMSE were 0.23 and 0.33 for MLR, and 0.79 and 0.11 for ANN, respectively. Digital values of VNIR1 and NDVI48 were identified as the most important factors in MLR, whereas Sum19 and SWIR6 were recognized as the most important data to predict soil salinity using ANN. The results indicated that ANN model is used to detect non- linear relationship between soil salinity and ASTER data at the study area.

کلیدواژه‌ها [English]

  • The study was conducted to evaluate the efficacy of artificial neural network (ANN) and multivariate regression (MLR) analysis to predict spatial variability of soil salinity in central Iran
  • using remotely sensed data. The analysis was based on data acquired from EOS AMI remote sensing satellite. The two methods was used to study linear and non-linear relationship between soil reflectance and soil salinity. In MLR analysis
  • stepwise method and neural network were applied using sensitivity coefficient by arranging inputs through the backward propagation
  • and then modeling was done. The R2 and RMSE were 0.23 and 0.33 for MLR
  • and 0.79 and 0.11 for ANN
  • respectively. Digital values of VNIR1 and NDVI48 were identified as the most important factors in MLR
  • whereas Sum19 and SWIR6 were recognized as the most important data to predict soil salinity using ANN. The results indicated that ANN model is used to detect non- linear relationship between soil salinity and ASTER data at the study area

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مهندسی اکوسیستم بیابان
دوره 7، شماره 1 انگلیسی
شهریور 1397
صفحه 20-11

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