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Application of ANFIS, ELM, and ANN models to assess water productivity indicators based on agronomic techniques in the Lake Urmia Basin
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- Emami, Somayeh (author)
- Department of Water Engineering, University of Tabriz, Tabriz, Iran
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- Dehghanisanij, Hossein (author)
- Agricultural Research, Education and Extension Organization, Agricultural Engineering Research Institute, P.O. Box 31585-845, Karaj, Alborz, Iran
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- Achite, Mohammed (author)
- Laboratory of Water and Environment, Faculty of Nature and Life Sciences, Hassiba Benbouali University of Chlef, 02180, Chlef, Algeria
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- Department of Water Engineering, University of Tabriz, Tabriz, Iran Agricultural Research, Education and Extension Organization, Agricultural Engineering Research Institute, PO. Box 31585-845, Karaj, Alborz, Iran (creator_code:org_t)
- 2022-12-30
- 2023
- English.
- In: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 13:2
- Journal article (peer-reviewed)
Abstract
Subject headings
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- Water productivity (WP) is one of the most important critical indicators in the essential planning of water consumption in the agricultural sector. For this purpose, the WP and economic water productivity (WPe) were estimated using agronomic technologies. The impact of agronomic technologies on WP and WPe was carried out in two parts of field monitoring and modeling using novel intelligent approaches. Extreme learning machine (ELM), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN) methods were used to model WP and WPe. A dataset including 200 field data was collected from five treatment and control sections in the Malekan region, located in the southeast of Lake Urmia, Iran, for the crop year 2020–2021. Six different input combinations were introduced to estimate WP and WPe. The models used were evaluated using mean squared error (RMSE), relative mean squared error (RRMSE), and efficiency measures (NSE). Field monitoring results showed that in the treatment fields, with the application of agronomic technologies, the crop yield, WP, and WPe increased by 17.9%, 30.1%, and 19.9%, respectively. The results explained that irrigation water in farms W1, W2, W3, W4, and W5 decreased by 23.9%, 21.3%, 29.5%, 16.5%, and 2.7%, respectively. The modeling results indicated that the ANFIS model with values of RMSE = 0.016, RRMSE = 0.018, and NSE = 0.960 performed better in estimating WP and WPe than ANN and ELM models. The results confirmed that the crop variety, fertilizer, and irrigation plot dimensions are the most critical influencing parameters in improving WP and WPe.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Vattenteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Water Engineering (hsv//eng)
- LANTBRUKSVETENSKAPER -- Lantbruksvetenskap, skogsbruk och fiske -- Jordbruksvetenskap (hsv//swe)
- AGRICULTURAL SCIENCES -- Agriculture, Forestry and Fisheries -- Agricultural Science (hsv//eng)
Keyword
- Adaptive neuro-fuzzy inference system
- Crop variety
- Irrigation
- Yield
- Soil Mechanics
- Geoteknik
Publication and Content Type
- ref (subject category)
- art (subject category)
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