DIGITAL MAPPING OF SNOW WATER EQUIVALENT USING AN ARTIFICIAL NEURAL NETWORK AND GEOMORPHOMETRIC PARAMETERS (CASE STUDY: SAKHVID WATERSHED, YAZD)

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EBDAM SOMAYE; FATHZADEH ALI; TAGHIZADEH MEHRJARDI ROHOLLAH; Mahjoobi Javad

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Journal: JOURNAL OF WATERSHED MANAGEMENT RESEARCH Year:2016 | Volume:7 | Issue:13 Page(s): 138-149

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Title

DIGITAL MAPPING OF SNOW WATER EQUIVALENT USING AN ARTIFICIAL NEURAL NETWORK AND GEOMORPHOMETRIC PARAMETERS (CASE STUDY: SAKHVID WATERSHED, YAZD)

Author(s)

EBDAM SOMAYE | FATHZADEH ALI | TAGHIZADEH MEHRJARDI ROHOLLAH | Mahjoobi Javad | Issue Writer Certificate

Keywords

ARTIFICIAL NEURAL NETWORKQ2

GEOMORPHOMETRIC PARAMETERSQ2

SAKHVID WATERSHEDQ1

SENSITIVITY ANALYSISQ1

SNOW WATER EQUIVALENTQ1

Abstract

Although a small portion of the Earth's surface is covered by the mountains, but it has a large impact on watershed hydrological perspective Because of the water crisis in arid and semi-arid regions of Iran, monitoring of the amount of snow in these areas is very important. Usually, access to the spatial distribution of SNOW WATER EQUIVALENT is limited to small scale using sampled data. However, due to the limitations of the mountainous, snow sampling of area is difficult and sometimes impossible in the large basins. Thus, the development of methods in order to estimate SNOW WATER EQUIVALENT at the un-sampled locations is essential. In this research, an area of 16 ha area in Yazd province was selected and SNOW WATER EQUIVALENT was measured at 216 points using a Mt. Rose snow sampler. Then the application of ARTIFICIAL NEURAL NETWORK method was evaluated using 31 GEOMORPHOMETRIC PARAMETERS and the digital map of SNOW WATER EQUIVALENT was obtained. The results showed that the ARTIFICIAL NEURAL NETWORK can estimate the SNOW WATER EQUIVALENT by a R2=0.83 and RMSE= 3.55.The results of the SENSITIVITY ANALYSIS are also showed that among the ANN parameters used in the prediction of SNOW WATER EQUIVALENT, Plan Curvature, Profile Curvature, Curvature, Wind Effect, Slope, Multiresolution ridge top flatness index (MRRTF), Catchment slope and Multi resolution index of valley bottom flatness (MRIVBF) are the effective parameters to predict SNOW WATER EQUIVALENT, respectively.

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APA: Copy

EBDAM, SOMAYE, FATHZADEH, ALI, TAGHIZADEH MEHRJARDI, ROHOLLAH, & Mahjoobi, Javad. (2016). DIGITAL MAPPING OF SNOW WATER EQUIVALENT USING AN ARTIFICIAL NEURAL NETWORK AND GEOMORPHOMETRIC PARAMETERS (CASE STUDY: SAKHVID WATERSHED, YAZD). JOURNAL OF WATERSHED MANAGEMENT RESEARCH, 7(13), 138-149. SID. https://sid.ir/paper/230268/en

Vancouver: Copy

EBDAM SOMAYE, FATHZADEH ALI, TAGHIZADEH MEHRJARDI ROHOLLAH, Mahjoobi Javad. DIGITAL MAPPING OF SNOW WATER EQUIVALENT USING AN ARTIFICIAL NEURAL NETWORK AND GEOMORPHOMETRIC PARAMETERS (CASE STUDY: SAKHVID WATERSHED, YAZD). JOURNAL OF WATERSHED MANAGEMENT RESEARCH[Internet]. 2016;7(13):138-149. Available from: https://sid.ir/paper/230268/en

IEEE: Copy

SOMAYE EBDAM, ALI FATHZADEH, ROHOLLAH TAGHIZADEH MEHRJARDI, and Javad Mahjoobi, “DIGITAL MAPPING OF SNOW WATER EQUIVALENT USING AN ARTIFICIAL NEURAL NETWORK AND GEOMORPHOMETRIC PARAMETERS (CASE STUDY: SAKHVID WATERSHED, YAZD),” JOURNAL OF WATERSHED MANAGEMENT RESEARCH, vol. 7, no. 13, pp. 138–149, 2016, [Online]. Available: https://sid.ir/paper/230268/en

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