Modeling of Groundwater Salinity Using Artificial Neural Network (ANN) and Geographic Information System (GIS) on the Caspian Southern Coasts

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Sebghati m.; GHOLAMI V.

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ISSN: 2588-4824

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Journal: Journal of Irrigation Sciences and Engineering Year:2020 | Volume:42 | Issue:4 Page(s): 181-194

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Title

Modeling of Groundwater Salinity Using Artificial Neural Network (ANN) and Geographic Information System (GIS) on the Caspian Southern Coasts

Author(s)

Sebghati m. | GHOLAMI V. | Issue Writer Certificate

Keywords

ECQ2

Groundwater SalinityQ2

Mazandaran PlainQ1

MLPQ1

Abstract

Introduction Groundwater is one of the most important water resources on earth, and water salinity studies are very important for the protECtion and planning of water resources, espECially in arid and semiarid areas such as Iran. Groundwater currently accounts for more than 90 percent of Iran’ s total drinking water consumption. This water resource is less susceptible to bacterial pollution and evaporation than surface water, and hence it is more important than surface water...

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APA: Copy

Sebghati, m., & GHOLAMI, V.. (2020). Modeling of Groundwater Salinity Using Artificial Neural Network (ANN) and Geographic Information System (GIS) on the Caspian Southern Coasts. IRRIGATION SCIENCES AND ENGINEERING (JISE) (SCIENTIFIC JOURNAL OF AGRICULTURE), 42(4 ), 181-194. SID. https://sid.ir/paper/217114/en

Vancouver: Copy

Sebghati m., GHOLAMI V.. Modeling of Groundwater Salinity Using Artificial Neural Network (ANN) and Geographic Information System (GIS) on the Caspian Southern Coasts. IRRIGATION SCIENCES AND ENGINEERING (JISE) (SCIENTIFIC JOURNAL OF AGRICULTURE)[Internet]. 2020;42(4 ):181-194. Available from: https://sid.ir/paper/217114/en

IEEE: Copy

m. Sebghati, and V. GHOLAMI, “Modeling of Groundwater Salinity Using Artificial Neural Network (ANN) and Geographic Information System (GIS) on the Caspian Southern Coasts,” IRRIGATION SCIENCES AND ENGINEERING (JISE) (SCIENTIFIC JOURNAL OF AGRICULTURE), vol. 42, no. 4 , pp. 181–194, 2020, [Online]. Available: https://sid.ir/paper/217114/en

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