Evaluating the accuracy of ANN and LR models to estimate the water quality in Zarivar International Wetland, Iran

Jamil Amanollahi (), Shahram Kaboodvandpour () and Hiva Majidi ()
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Jamil Amanollahi: University of Kurdistan
Shahram Kaboodvandpour: University of Kurdistan
Hiva Majidi: University of Kurdistan

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2017, vol. 85, issue 3, No 10, 1527 pages

Abstract: Abstract One of the most important qualitative aspects of wetland ecosystem management is preserving the natural quality of water in such environments. This would not be achievable unless continuous water quality monitoring is implemented. With the recent advances in remote sensing technology, this technology could assist us to produce accurate models for estimating water quality variables in the ecosystem of wetlands. The present study was carried out to evaluate the capability of remote sensing data to estimate the water quality variables [pH, total suspended solids (TSS), total dissolved solids (TDS), turbidity, nitrate, sulfate, phosphate, chloride and the concentration of chlorophyll a] in Zarivar International Wetland using linear regression (LR) and artificial neural network (ANN) models. For this purpose, spectral reflectance of bands 2, 3, 4 and 5 of the OLI sensor of Landsat 8 was utilized as the input data and the collected chemical and physical data of water samples were selected as the objective data for both ANN and LR models. Based on our results overall, ANN model was the proper model compared with LR model. The spectral reflectance in bands 5 and 4 of OLI sensor revealed the best results to estimate TDS, TSS, turbidity and chlorophyll in comparison with other used bands in ANN model, respectively. We conclude that OLI sensor data are an excellent means for studying physical properties of water quality and comparing its chemical properties.

Keywords: Wetland; Model; Remote sensing; Chemical and physical; Turbidity; Chlorophyll (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11069-016-2641-1

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