Eco-Environmental Quality Evaluation Using Remote Sensing and Artificial Neural Networks (Case Study: Cities of Tabriz and Rasht)

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NOURANI V.; FOROUMANDI E.; SHARGHI E.

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Journal: IRAN-WATER RESOURCES RESEARCH Year:2019 | Volume:15 | Issue:3 Page(s): 218-233

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Title

Eco-Environmental Quality Evaluation Using Remote Sensing and Artificial Neural Networks (Case Study: Cities of Tabriz and Rasht)

Author(s)

NOURANI V. | FOROUMANDI E. | SHARGHI E. | Issue Writer Certificate

Keywords

Eco-environment Value AssessmentQ1

Remote SensingQ1

Meteorological DataQ2

Artificial Neural NetworkQ2

Abstract

Environmental problems are among the major problems facing humankind in the recent century, and so far, various indices have been proposed and examined to assess them. Accordingly, in this study, to evaluate the ecological environmental quality (EQ) of 500 pixels (pixel area=1km2) around the city of Tabriz (center of East Azerbaijan Province) and 500 pixels around the city of Rasht (center of Gilan Province), Iran, with different climates, the EQ index has been investigated using Artificial Neural Networks (ANN) and Remote Sensing techniques. Eco-environment Background Value (EBV) based on a scoring and ranking system, was used to evaluate EQ. The higher the EBV, the better the ecological environmental quality. For the modeling, indicators including vegetation index, wetness index, Land Surface Temperature (LST), and Digital Elevation Model (DEM) data as well as precipitation and temperature were exploited as input of the three-layer back propagation Artificial Neural Network (BPANN) model. The average of the data for the past 8 years for these indicators for the study regions were entered the network; once seasonally and once annually. The analysis showed that the ANN model has acceptable performance for estimating complex environmental functions, which are affected by various environmental parameters. The best performance of the network was obtained for Tabriz region in the spring with a root mean square error (RMSE) of 0. 02 and coefficient of determination (R2) of 0. 95. The better network performance for Tabriz compared to Rasht may be due to the weakness of the Remote Sensing tool in examining areas like Gilan with high vegetation density (VD) and high relative humidity (HRH). It seems that the high VD and HRH impede proper reflection without deviation from the land surface and disrupts the reception of the required data. Analyzing the spatial correlation between EQ and the land uses, it was found that forest lands were the best eco-environmental area, whereas the urban area had the relatively worst EQ. Human activities are a major impact on the EQ in this area.

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APA: Copy

NOURANI, V., FOROUMANDI, E., & SHARGHI, E.. (2019). Eco-Environmental Quality Evaluation Using Remote Sensing and Artificial Neural Networks (Case Study: Cities of Tabriz and Rasht). IRAN-WATER RESOURCES RESEARCH, 15(3 ), 218-233. SID. https://sid.ir/paper/100378/en

Vancouver: Copy

NOURANI V., FOROUMANDI E., SHARGHI E.. Eco-Environmental Quality Evaluation Using Remote Sensing and Artificial Neural Networks (Case Study: Cities of Tabriz and Rasht). IRAN-WATER RESOURCES RESEARCH[Internet]. 2019;15(3 ):218-233. Available from: https://sid.ir/paper/100378/en

IEEE: Copy

V. NOURANI, E. FOROUMANDI, and E. SHARGHI, “Eco-Environmental Quality Evaluation Using Remote Sensing and Artificial Neural Networks (Case Study: Cities of Tabriz and Rasht),” IRAN-WATER RESOURCES RESEARCH, vol. 15, no. 3 , pp. 218–233, 2019, [Online]. Available: https://sid.ir/paper/100378/en

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