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Archives of Agronomy and Soil Science Volume 62, 2016 - Issue 1
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Original Articles

Predicting ESP and SAR by artificial neural network and regression models using soil pH and EC data (Miankangi Region, Sistan and Baluchestan Province, Iran)

Fereydoon SaraniDepartment of Soil Science, College of Water and Soil, University of Zabol, Zabol, Iran
,
Ahmad Gholamalizadeh AhangarDepartment of Soil Science, College of Water and Soil, University of Zabol, Zabol, Iran
&
Asma ShabaniDepartment of Soil Science, College of Water and Soil, University of Zabol, Zabol, IranCorrespondence[email protected]
Pages 127-138 | Received 29 Nov 2014, Accepted 03 Mar 2015, Published online: 05 May 2015

Citations (13)

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Sevda Taşan & Yusuf Demir. (2022) Assessment of Data-based Models (ANN, ANFIS and SVR) for Estimation of Exchangeable Sodium Percentage (ESP) of Bafra Plain Soils. Communications in Soil Science and Plant Analysis 53:2, pages 199-213.
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Hamdi Zurqani, Elena Mikhailova, Christopher Post, Mark Schlautman & Julia Sharp. (2018) Predicting the Classes and Distribution of Salt-Affected Soils in Northwest Libya. Communications in Soil Science and Plant Analysis 49:6, pages 689-700.
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Quang Toan Dinh, Dongli Liang, Tran Thi Anh Thu, Trong Dieu Hien Le, Nguyen Dinh Vuong & Van Tat Pham. (2018) Spatial prediction of saline and sodic soils in rice‒shrimp farming land by using integrated artificial neural network/regression model and kriging. Archives of Agronomy and Soil Science 64:3, pages 371-383.
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Mahmoud Shahabi, Ali Asghar Jafarzadeh, Mohammad Reza Neyshabouri, Mohammad Ali Ghorbani & Khalil Valizadeh Kamran. (2017) Spatial modeling of soil salinity using multiple linear regression, ordinary kriging and artificial neural network methods. Archives of Agronomy and Soil Science 63:2, pages 151-160.
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Nastaran Chitsaz, Ali Azarnivand & Shahab Araghinejad. (2016) Pre-processing of data-driven river flow forecasting models by singular value decomposition (SVD) technique. Hydrological Sciences Journal 61:12, pages 2164-2178.
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Articles from other publishers (8)

Sudipa Halder, Shuvoshri Bhattacharya, Malabika Biswas Roy & Pankaj Kumar Roy. (2023) Application of fuzzy C-means clustering and fuzzy EDAS to assess groundwater irrigation suitability and prioritization for agricultural development in a complex hydrogeological basin. Environmental Science and Pollution Research 30:20, pages 57529-57557.
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Arup Kumar MANDAL. (2023) Necessity for quantified measurement of soil sodicity and selection of suitable gypsum amendment for proper reclamation of sodic soils. Pedosphere 33:1, pages 231-235.
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Lu Zhang, Yu-Hang Du, Xiu-Juan Yang & Heng-Hui Fan. (2022) Application of Artificial Neural Network in Predicting the Dispersibility of Soil. Iranian Journal of Science and Technology, Transactions of Civil Engineering 46:3, pages 2315-2324.
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Shunmuga Priya Kaliyappan, Kirubakaran Muniraj, Gopinath Rajendran, Butsawan Bidorn, Maciej Thomas & Balamurugan Panneerselvam. 2022. Climate Change Impact on Groundwater Resources. Climate Change Impact on Groundwater Resources 163 180 .
Balamurugan Panneerselvam, Kirubakaran Muniraj, Maciej Thomas, Nagavinothini Ravichandran & Butsawan Bidorn. (2021) Identifying influencing groundwater parameter on human health associate with irrigation indices using the Automatic Linear Model (ALM) in a semi-arid region in India. Environmental Research 202, pages 111778.
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Mohammad Taghi Sattari, Hajar Feizi, Muslume Sevba Colak, Ahmet Ozturk, Halit Apaydin & Fazli Ozturk. (2020) Estimation of sodium adsorption ratio in a river with kernel-based and decision-tree models. Environmental Monitoring and Assessment 192:9.
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Tian, Fu, Su, Yau & Xiong. (2019) Classifying and Predicting Salinization Level in Arid Area Soil Using a Combination of Chua’s Circuit and Fractional Order Sprott Chaotic System. Sensors 19:20, pages 4517.
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Ali Keshavarzi, Ali Bagherzadeh, El-Sayed Ewis Omran & Munawar Iqbal. (2016) Modeling of soil exchangeable sodium percentage using easily obtained indices and artificial intelligence-based models. Modeling Earth Systems and Environment 2:3.
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