Runoff mapping using the SCS-CN method and artificial neural network algorithm, Ratga Basin, Iraq
Abstract The traditional Soil Conservation Service )SCS( process for calculating the runoff depth tends to be a very tedious and time time-consuming hydrological modeling process. Therefore, a geographic information system (GIS) is now being utilized as a tool alongside the common SCS-CN method for...
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| Autor*in: |
Muneer, Ahmed Shahadha [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© Saudi Society for Geosciences 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Arabian journal of geosciences - Berlin : Springer, 2008, 15(2022), 7 vom: 31. März |
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| Übergeordnetes Werk: |
volume:15 ; year:2022 ; number:7 ; day:31 ; month:03 |
| Links: |
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| DOI / URN: |
10.1007/s12517-022-09954-y |
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| Katalog-ID: |
SPR046641459 |
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| 245 | 1 | 0 | |a Runoff mapping using the SCS-CN method and artificial neural network algorithm, Ratga Basin, Iraq |
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| 520 | |a Abstract The traditional Soil Conservation Service )SCS( process for calculating the runoff depth tends to be a very tedious and time time-consuming hydrological modeling process. Therefore, a geographic information system (GIS) is now being utilized as a tool alongside the common SCS-CN method for runoff calculations. This research aims to estimate the spatial distribution of runoff depth from Ratga, an agricultural watershed from the Iraqi Western Desert, using the SCS-CN method, GIS, generalized regression neural network (GRNN), field observation dataset, and remote sensing data. The GRNN model was used to predict the soil type based on spectral reflectance data. The results refer to an excellent performance of this model with the maximum absolute error was 8.44%, 14.11%, and 4.15% for sand, silt, and clay soil, respectively, and the sandy soil has the highest correlation coefficient (0.83). The outcome of the SCS method showed the CN value ranged from 70 to 85 of normal conditions. This investigation outline that the maximum volume of surface runoff of the 2018 to 2020 years was 4,324,528 $ m^{3} $. This paper proves that incorporating GIS with the SCS-CN model and ANN provides a robust tool for calculation runoff depth in the Iraqi Western Desert, representing barren catchments of Iraq. | ||
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| 650 | 4 | |a GIS |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Remote sensing |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a GRNN |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Runoff |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Afan, Haitham Abdulmohsin |4 aut | |
| 700 | 1 | |a Kamel, Ammar Hatem |4 aut | |
| 700 | 1 | |a Sayl, Khamis Naba |4 aut | |
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10.1007/s12517-022-09954-y doi (DE-627)SPR046641459 (SPR)s12517-022-09954-y-e DE-627 ger DE-627 rakwb eng Muneer, Ahmed Shahadha verfasserin (orcid)0000-0001-8512-5168 aut Runoff mapping using the SCS-CN method and artificial neural network algorithm, Ratga Basin, Iraq 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences 2022 Abstract The traditional Soil Conservation Service )SCS( process for calculating the runoff depth tends to be a very tedious and time time-consuming hydrological modeling process. Therefore, a geographic information system (GIS) is now being utilized as a tool alongside the common SCS-CN method for runoff calculations. This research aims to estimate the spatial distribution of runoff depth from Ratga, an agricultural watershed from the Iraqi Western Desert, using the SCS-CN method, GIS, generalized regression neural network (GRNN), field observation dataset, and remote sensing data. The GRNN model was used to predict the soil type based on spectral reflectance data. The results refer to an excellent performance of this model with the maximum absolute error was 8.44%, 14.11%, and 4.15% for sand, silt, and clay soil, respectively, and the sandy soil has the highest correlation coefficient (0.83). The outcome of the SCS method showed the CN value ranged from 70 to 85 of normal conditions. This investigation outline that the maximum volume of surface runoff of the 2018 to 2020 years was 4,324,528 $ m^{3} $. This paper proves that incorporating GIS with the SCS-CN model and ANN provides a robust tool for calculation runoff depth in the Iraqi Western Desert, representing barren catchments of Iraq. SCS-CN method (dpeaa)DE-He213 GIS (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 GRNN (dpeaa)DE-He213 Runoff (dpeaa)DE-He213 Afan, Haitham Abdulmohsin aut Kamel, Ammar Hatem aut Sayl, Khamis Naba aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 15(2022), 7 vom: 31. März (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:15 year:2022 number:7 day:31 month:03 https://dx.doi.org/10.1007/s12517-022-09954-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2022 7 31 03 |
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10.1007/s12517-022-09954-y doi (DE-627)SPR046641459 (SPR)s12517-022-09954-y-e DE-627 ger DE-627 rakwb eng Muneer, Ahmed Shahadha verfasserin (orcid)0000-0001-8512-5168 aut Runoff mapping using the SCS-CN method and artificial neural network algorithm, Ratga Basin, Iraq 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences 2022 Abstract The traditional Soil Conservation Service )SCS( process for calculating the runoff depth tends to be a very tedious and time time-consuming hydrological modeling process. Therefore, a geographic information system (GIS) is now being utilized as a tool alongside the common SCS-CN method for runoff calculations. This research aims to estimate the spatial distribution of runoff depth from Ratga, an agricultural watershed from the Iraqi Western Desert, using the SCS-CN method, GIS, generalized regression neural network (GRNN), field observation dataset, and remote sensing data. The GRNN model was used to predict the soil type based on spectral reflectance data. The results refer to an excellent performance of this model with the maximum absolute error was 8.44%, 14.11%, and 4.15% for sand, silt, and clay soil, respectively, and the sandy soil has the highest correlation coefficient (0.83). The outcome of the SCS method showed the CN value ranged from 70 to 85 of normal conditions. This investigation outline that the maximum volume of surface runoff of the 2018 to 2020 years was 4,324,528 $ m^{3} $. This paper proves that incorporating GIS with the SCS-CN model and ANN provides a robust tool for calculation runoff depth in the Iraqi Western Desert, representing barren catchments of Iraq. SCS-CN method (dpeaa)DE-He213 GIS (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 GRNN (dpeaa)DE-He213 Runoff (dpeaa)DE-He213 Afan, Haitham Abdulmohsin aut Kamel, Ammar Hatem aut Sayl, Khamis Naba aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 15(2022), 7 vom: 31. März (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:15 year:2022 number:7 day:31 month:03 https://dx.doi.org/10.1007/s12517-022-09954-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2022 7 31 03 |
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10.1007/s12517-022-09954-y doi (DE-627)SPR046641459 (SPR)s12517-022-09954-y-e DE-627 ger DE-627 rakwb eng Muneer, Ahmed Shahadha verfasserin (orcid)0000-0001-8512-5168 aut Runoff mapping using the SCS-CN method and artificial neural network algorithm, Ratga Basin, Iraq 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences 2022 Abstract The traditional Soil Conservation Service )SCS( process for calculating the runoff depth tends to be a very tedious and time time-consuming hydrological modeling process. Therefore, a geographic information system (GIS) is now being utilized as a tool alongside the common SCS-CN method for runoff calculations. This research aims to estimate the spatial distribution of runoff depth from Ratga, an agricultural watershed from the Iraqi Western Desert, using the SCS-CN method, GIS, generalized regression neural network (GRNN), field observation dataset, and remote sensing data. The GRNN model was used to predict the soil type based on spectral reflectance data. The results refer to an excellent performance of this model with the maximum absolute error was 8.44%, 14.11%, and 4.15% for sand, silt, and clay soil, respectively, and the sandy soil has the highest correlation coefficient (0.83). The outcome of the SCS method showed the CN value ranged from 70 to 85 of normal conditions. This investigation outline that the maximum volume of surface runoff of the 2018 to 2020 years was 4,324,528 $ m^{3} $. This paper proves that incorporating GIS with the SCS-CN model and ANN provides a robust tool for calculation runoff depth in the Iraqi Western Desert, representing barren catchments of Iraq. SCS-CN method (dpeaa)DE-He213 GIS (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 GRNN (dpeaa)DE-He213 Runoff (dpeaa)DE-He213 Afan, Haitham Abdulmohsin aut Kamel, Ammar Hatem aut Sayl, Khamis Naba aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 15(2022), 7 vom: 31. März (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:15 year:2022 number:7 day:31 month:03 https://dx.doi.org/10.1007/s12517-022-09954-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2022 7 31 03 |
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10.1007/s12517-022-09954-y doi (DE-627)SPR046641459 (SPR)s12517-022-09954-y-e DE-627 ger DE-627 rakwb eng Muneer, Ahmed Shahadha verfasserin (orcid)0000-0001-8512-5168 aut Runoff mapping using the SCS-CN method and artificial neural network algorithm, Ratga Basin, Iraq 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences 2022 Abstract The traditional Soil Conservation Service )SCS( process for calculating the runoff depth tends to be a very tedious and time time-consuming hydrological modeling process. Therefore, a geographic information system (GIS) is now being utilized as a tool alongside the common SCS-CN method for runoff calculations. This research aims to estimate the spatial distribution of runoff depth from Ratga, an agricultural watershed from the Iraqi Western Desert, using the SCS-CN method, GIS, generalized regression neural network (GRNN), field observation dataset, and remote sensing data. The GRNN model was used to predict the soil type based on spectral reflectance data. The results refer to an excellent performance of this model with the maximum absolute error was 8.44%, 14.11%, and 4.15% for sand, silt, and clay soil, respectively, and the sandy soil has the highest correlation coefficient (0.83). The outcome of the SCS method showed the CN value ranged from 70 to 85 of normal conditions. This investigation outline that the maximum volume of surface runoff of the 2018 to 2020 years was 4,324,528 $ m^{3} $. This paper proves that incorporating GIS with the SCS-CN model and ANN provides a robust tool for calculation runoff depth in the Iraqi Western Desert, representing barren catchments of Iraq. SCS-CN method (dpeaa)DE-He213 GIS (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 GRNN (dpeaa)DE-He213 Runoff (dpeaa)DE-He213 Afan, Haitham Abdulmohsin aut Kamel, Ammar Hatem aut Sayl, Khamis Naba aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 15(2022), 7 vom: 31. März (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:15 year:2022 number:7 day:31 month:03 https://dx.doi.org/10.1007/s12517-022-09954-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2022 7 31 03 |
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10.1007/s12517-022-09954-y doi (DE-627)SPR046641459 (SPR)s12517-022-09954-y-e DE-627 ger DE-627 rakwb eng Muneer, Ahmed Shahadha verfasserin (orcid)0000-0001-8512-5168 aut Runoff mapping using the SCS-CN method and artificial neural network algorithm, Ratga Basin, Iraq 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences 2022 Abstract The traditional Soil Conservation Service )SCS( process for calculating the runoff depth tends to be a very tedious and time time-consuming hydrological modeling process. Therefore, a geographic information system (GIS) is now being utilized as a tool alongside the common SCS-CN method for runoff calculations. This research aims to estimate the spatial distribution of runoff depth from Ratga, an agricultural watershed from the Iraqi Western Desert, using the SCS-CN method, GIS, generalized regression neural network (GRNN), field observation dataset, and remote sensing data. The GRNN model was used to predict the soil type based on spectral reflectance data. The results refer to an excellent performance of this model with the maximum absolute error was 8.44%, 14.11%, and 4.15% for sand, silt, and clay soil, respectively, and the sandy soil has the highest correlation coefficient (0.83). The outcome of the SCS method showed the CN value ranged from 70 to 85 of normal conditions. This investigation outline that the maximum volume of surface runoff of the 2018 to 2020 years was 4,324,528 $ m^{3} $. This paper proves that incorporating GIS with the SCS-CN model and ANN provides a robust tool for calculation runoff depth in the Iraqi Western Desert, representing barren catchments of Iraq. SCS-CN method (dpeaa)DE-He213 GIS (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 GRNN (dpeaa)DE-He213 Runoff (dpeaa)DE-He213 Afan, Haitham Abdulmohsin aut Kamel, Ammar Hatem aut Sayl, Khamis Naba aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 15(2022), 7 vom: 31. März (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:15 year:2022 number:7 day:31 month:03 https://dx.doi.org/10.1007/s12517-022-09954-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2022 7 31 03 |
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Muneer, Ahmed Shahadha @@aut@@ Afan, Haitham Abdulmohsin @@aut@@ Kamel, Ammar Hatem @@aut@@ Sayl, Khamis Naba @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR046641459</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230507160700.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220402s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12517-022-09954-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR046641459</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12517-022-09954-y-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Muneer, Ahmed Shahadha</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-8512-5168</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Runoff mapping using the SCS-CN method and artificial neural network algorithm, Ratga Basin, Iraq</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Saudi Society for Geosciences 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The traditional Soil Conservation Service )SCS( process for calculating the runoff depth tends to be a very tedious and time time-consuming hydrological modeling process. Therefore, a geographic information system (GIS) is now being utilized as a tool alongside the common SCS-CN method for runoff calculations. This research aims to estimate the spatial distribution of runoff depth from Ratga, an agricultural watershed from the Iraqi Western Desert, using the SCS-CN method, GIS, generalized regression neural network (GRNN), field observation dataset, and remote sensing data. The GRNN model was used to predict the soil type based on spectral reflectance data. The results refer to an excellent performance of this model with the maximum absolute error was 8.44%, 14.11%, and 4.15% for sand, silt, and clay soil, respectively, and the sandy soil has the highest correlation coefficient (0.83). The outcome of the SCS method showed the CN value ranged from 70 to 85 of normal conditions. This investigation outline that the maximum volume of surface runoff of the 2018 to 2020 years was 4,324,528 $ m^{3} $. 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Muneer, Ahmed Shahadha |
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Muneer, Ahmed Shahadha misc SCS-CN method misc GIS misc Remote sensing misc GRNN misc Runoff Runoff mapping using the SCS-CN method and artificial neural network algorithm, Ratga Basin, Iraq |
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Runoff mapping using the SCS-CN method and artificial neural network algorithm, Ratga Basin, Iraq SCS-CN method (dpeaa)DE-He213 GIS (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 GRNN (dpeaa)DE-He213 Runoff (dpeaa)DE-He213 |
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misc SCS-CN method misc GIS misc Remote sensing misc GRNN misc Runoff |
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Runoff mapping using the SCS-CN method and artificial neural network algorithm, Ratga Basin, Iraq |
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Runoff mapping using the SCS-CN method and artificial neural network algorithm, Ratga Basin, Iraq |
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runoff mapping using the scs-cn method and artificial neural network algorithm, ratga basin, iraq |
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Runoff mapping using the SCS-CN method and artificial neural network algorithm, Ratga Basin, Iraq |
| abstract |
Abstract The traditional Soil Conservation Service )SCS( process for calculating the runoff depth tends to be a very tedious and time time-consuming hydrological modeling process. Therefore, a geographic information system (GIS) is now being utilized as a tool alongside the common SCS-CN method for runoff calculations. This research aims to estimate the spatial distribution of runoff depth from Ratga, an agricultural watershed from the Iraqi Western Desert, using the SCS-CN method, GIS, generalized regression neural network (GRNN), field observation dataset, and remote sensing data. The GRNN model was used to predict the soil type based on spectral reflectance data. The results refer to an excellent performance of this model with the maximum absolute error was 8.44%, 14.11%, and 4.15% for sand, silt, and clay soil, respectively, and the sandy soil has the highest correlation coefficient (0.83). The outcome of the SCS method showed the CN value ranged from 70 to 85 of normal conditions. This investigation outline that the maximum volume of surface runoff of the 2018 to 2020 years was 4,324,528 $ m^{3} $. This paper proves that incorporating GIS with the SCS-CN model and ANN provides a robust tool for calculation runoff depth in the Iraqi Western Desert, representing barren catchments of Iraq. © Saudi Society for Geosciences 2022 |
| abstractGer |
Abstract The traditional Soil Conservation Service )SCS( process for calculating the runoff depth tends to be a very tedious and time time-consuming hydrological modeling process. Therefore, a geographic information system (GIS) is now being utilized as a tool alongside the common SCS-CN method for runoff calculations. This research aims to estimate the spatial distribution of runoff depth from Ratga, an agricultural watershed from the Iraqi Western Desert, using the SCS-CN method, GIS, generalized regression neural network (GRNN), field observation dataset, and remote sensing data. The GRNN model was used to predict the soil type based on spectral reflectance data. The results refer to an excellent performance of this model with the maximum absolute error was 8.44%, 14.11%, and 4.15% for sand, silt, and clay soil, respectively, and the sandy soil has the highest correlation coefficient (0.83). The outcome of the SCS method showed the CN value ranged from 70 to 85 of normal conditions. This investigation outline that the maximum volume of surface runoff of the 2018 to 2020 years was 4,324,528 $ m^{3} $. This paper proves that incorporating GIS with the SCS-CN model and ANN provides a robust tool for calculation runoff depth in the Iraqi Western Desert, representing barren catchments of Iraq. © Saudi Society for Geosciences 2022 |
| abstract_unstemmed |
Abstract The traditional Soil Conservation Service )SCS( process for calculating the runoff depth tends to be a very tedious and time time-consuming hydrological modeling process. Therefore, a geographic information system (GIS) is now being utilized as a tool alongside the common SCS-CN method for runoff calculations. This research aims to estimate the spatial distribution of runoff depth from Ratga, an agricultural watershed from the Iraqi Western Desert, using the SCS-CN method, GIS, generalized regression neural network (GRNN), field observation dataset, and remote sensing data. The GRNN model was used to predict the soil type based on spectral reflectance data. The results refer to an excellent performance of this model with the maximum absolute error was 8.44%, 14.11%, and 4.15% for sand, silt, and clay soil, respectively, and the sandy soil has the highest correlation coefficient (0.83). The outcome of the SCS method showed the CN value ranged from 70 to 85 of normal conditions. This investigation outline that the maximum volume of surface runoff of the 2018 to 2020 years was 4,324,528 $ m^{3} $. This paper proves that incorporating GIS with the SCS-CN model and ANN provides a robust tool for calculation runoff depth in the Iraqi Western Desert, representing barren catchments of Iraq. © Saudi Society for Geosciences 2022 |
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Runoff mapping using the SCS-CN method and artificial neural network algorithm, Ratga Basin, Iraq |
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https://dx.doi.org/10.1007/s12517-022-09954-y |
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Afan, Haitham Abdulmohsin Kamel, Ammar Hatem Sayl, Khamis Naba |
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10.1007/s12517-022-09954-y |
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2024-07-03T23:41:25.422Z |
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| score |
7.3995895 |