EVALUATION OF NEURAL NETWORK AND REGRESSION MODELS TO PREDICT SPECIES DIVERSITY USING SOME SOIL AND PHYSIOGRAPHIC FACTORS (CASE STUDY: KHARABEH SANJI WATERSHED OF URMIA)

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BAHRAMI B.; GHORBANI A.

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Journal: NATURAL ECOSYSTEMS OF IRAN Year:2014 | Volume:5 | Issue:2 Page(s): 65-80

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Title

EVALUATION OF NEURAL NETWORK AND REGRESSION MODELS TO PREDICT SPECIES DIVERSITY USING SOME SOIL AND PHYSIOGRAPHIC FACTORS (CASE STUDY: KHARABEH SANJI WATERSHED OF URMIA)

Author(s)

BAHRAMI B. | GHORBANI A. | Issue Writer Certificate

Keywords

MODELLINGQ1

RANGELANDSQ2

ENVIRONMENTAL FACTORSQ2

VEGETATIONQ2

Abstract

Direct measurement of species diversity is a time consuming and cost effective and somewhat unreliable because of errors in the sampling. This study was conducted by the aim of determining low cost factors for predicting species diversity using artificial neural network, adaptive- fuzzy neural network and regression models. Sampling was conducted using randomized-systematic method from 60 plots along 6 transects with 100m long and from 0-30cm of soil depth. VEGETATION data were recorded to calculate species diversity by Shannon-wiener index. Moreover, for determining the affective factors on species diversity, electrical conductivity, pH, bulk density, percentages of organic matter, clay, silt, wet saturation, coarse and fine aggregates and slope and elevation were measured and determined. Then species diversity was determined using multii-layer perceptron neural network, adaptive-fuzzy neural network and regression models. The results show that criteria such as root mean squire error and efficiency coefficient of the regression model were 0.14 and 0.39, in artificial neural network 0.07 and 0.86 and for adaptive- fuzzy neural network 0.09 and 0.7, respectively. that Shannon wiener index was 1.98 for the study area. The artificial neural network model as a powerful tool in predicting species diversity in comparison with the multiple linear regression analysis and neural network-fuzzy adaptive models showed reliable results.

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APA: Copy

BAHRAMI, B., & GHORBANI, A.. (2014). EVALUATION OF NEURAL NETWORK AND REGRESSION MODELS TO PREDICT SPECIES DIVERSITY USING SOME SOIL AND PHYSIOGRAPHIC FACTORS (CASE STUDY: KHARABEH SANJI WATERSHED OF URMIA). NATURAL ECOSYSTEMS OF IRAN, 5(2), 65-80. SID. https://sid.ir/paper/215293/en

Vancouver: Copy

BAHRAMI B., GHORBANI A.. EVALUATION OF NEURAL NETWORK AND REGRESSION MODELS TO PREDICT SPECIES DIVERSITY USING SOME SOIL AND PHYSIOGRAPHIC FACTORS (CASE STUDY: KHARABEH SANJI WATERSHED OF URMIA). NATURAL ECOSYSTEMS OF IRAN[Internet]. 2014;5(2):65-80. Available from: https://sid.ir/paper/215293/en

IEEE: Copy

B. BAHRAMI, and A. GHORBANI, “EVALUATION OF NEURAL NETWORK AND REGRESSION MODELS TO PREDICT SPECIES DIVERSITY USING SOME SOIL AND PHYSIOGRAPHIC FACTORS (CASE STUDY: KHARABEH SANJI WATERSHED OF URMIA),” NATURAL ECOSYSTEMS OF IRAN, vol. 5, no. 2, pp. 65–80, 2014, [Online]. Available: https://sid.ir/paper/215293/en

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