An insight into machine learning models to predict the distribution of Leucanthemum vulgare Lam. in northwestern rangelands of Iran
Abstract Invasions by non-native species are an increasing problem, especially at valuable rangelands. In the present research, the suitable habitat of the invasive species of Leucanthemum vulgare Lam., in the rangelands of the Namin County in northwest Iran, was assessed using the topographic, clim...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Khanghah, Sahar Samadi [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), 9 vom: 23. Apr. |
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| Übergeordnetes Werk: |
volume:15 ; year:2022 ; number:9 ; day:23 ; month:04 |
| Links: |
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| DOI / URN: |
10.1007/s12517-022-10137-y |
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| Katalog-ID: |
SPR046828133 |
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| 100 | 1 | |a Khanghah, Sahar Samadi |e verfasserin |4 aut | |
| 245 | 1 | 3 | |a An insight into machine learning models to predict the distribution of Leucanthemum vulgare Lam. in northwestern rangelands of Iran |
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| 520 | |a Abstract Invasions by non-native species are an increasing problem, especially at valuable rangelands. In the present research, the suitable habitat of the invasive species of Leucanthemum vulgare Lam., in the rangelands of the Namin County in northwest Iran, was assessed using the topographic, climatic, and soil variables. Four machine learning models, including random forest (RF), boosted regression trees (BRTs), generalized linear model (GLM), and generalized additive models (GAM), were used in the R environment. This research was conducted in May and June 2019–2020. The presence and absence of the L. vulgare were recorded using a stratified random sampling method using a global positioning system. The soil samples were taken at a depth of 0 to 30 cm from the presence and absence sites of L. vulgare. The results showed that GAM performed with 95% Kappa, 91% AUC, 88% TSS better than others and, followed by GLM, BRTs, and RF in decreasing order among the implemented models. The predictive performance of the GAM model using tenfold cross-validation in the Caret package indicate that clay, organic matter, elevation, and phosphorus were the critical factors influencing the spread of L. vulgare. According to the GAM prediction, 12.95% (13,799.79 ha) of the Namin County is potentially suitable for the L. vulgare. The spatial pattern of invasive species in rangelands can help make sound decisions in controlling the expansion of invasive species and related ecological implications. | ||
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| 650 | 4 | |a Generalized linear model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Generalized additive models |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Mostafazadeh, Raoof |4 aut | |
| 700 | 1 | |a Esmali Ouri, Abazar |4 aut | |
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10.1007/s12517-022-10137-y doi (DE-627)SPR046828133 (SPR)s12517-022-10137-y-e DE-627 ger DE-627 rakwb eng Khanghah, Sahar Samadi verfasserin aut An insight into machine learning models to predict the distribution of Leucanthemum vulgare Lam. in northwestern rangelands of Iran 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences 2022 Abstract Invasions by non-native species are an increasing problem, especially at valuable rangelands. In the present research, the suitable habitat of the invasive species of Leucanthemum vulgare Lam., in the rangelands of the Namin County in northwest Iran, was assessed using the topographic, climatic, and soil variables. Four machine learning models, including random forest (RF), boosted regression trees (BRTs), generalized linear model (GLM), and generalized additive models (GAM), were used in the R environment. This research was conducted in May and June 2019–2020. The presence and absence of the L. vulgare were recorded using a stratified random sampling method using a global positioning system. The soil samples were taken at a depth of 0 to 30 cm from the presence and absence sites of L. vulgare. The results showed that GAM performed with 95% Kappa, 91% AUC, 88% TSS better than others and, followed by GLM, BRTs, and RF in decreasing order among the implemented models. The predictive performance of the GAM model using tenfold cross-validation in the Caret package indicate that clay, organic matter, elevation, and phosphorus were the critical factors influencing the spread of L. vulgare. According to the GAM prediction, 12.95% (13,799.79 ha) of the Namin County is potentially suitable for the L. vulgare. The spatial pattern of invasive species in rangelands can help make sound decisions in controlling the expansion of invasive species and related ecological implications. Invasive species (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Boosted regression trees (dpeaa)DE-He213 Generalized linear model (dpeaa)DE-He213 Generalized additive models (dpeaa)DE-He213 Moameri, Mehdi (orcid)0000-0003-2917-4736 aut Ghorbani, Ardavan aut Mostafazadeh, Raoof aut Esmali Ouri, Abazar aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 15(2022), 9 vom: 23. Apr. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:15 year:2022 number:9 day:23 month:04 https://dx.doi.org/10.1007/s12517-022-10137-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 9 23 04 |
| spelling |
10.1007/s12517-022-10137-y doi (DE-627)SPR046828133 (SPR)s12517-022-10137-y-e DE-627 ger DE-627 rakwb eng Khanghah, Sahar Samadi verfasserin aut An insight into machine learning models to predict the distribution of Leucanthemum vulgare Lam. in northwestern rangelands of Iran 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences 2022 Abstract Invasions by non-native species are an increasing problem, especially at valuable rangelands. In the present research, the suitable habitat of the invasive species of Leucanthemum vulgare Lam., in the rangelands of the Namin County in northwest Iran, was assessed using the topographic, climatic, and soil variables. Four machine learning models, including random forest (RF), boosted regression trees (BRTs), generalized linear model (GLM), and generalized additive models (GAM), were used in the R environment. This research was conducted in May and June 2019–2020. The presence and absence of the L. vulgare were recorded using a stratified random sampling method using a global positioning system. The soil samples were taken at a depth of 0 to 30 cm from the presence and absence sites of L. vulgare. The results showed that GAM performed with 95% Kappa, 91% AUC, 88% TSS better than others and, followed by GLM, BRTs, and RF in decreasing order among the implemented models. The predictive performance of the GAM model using tenfold cross-validation in the Caret package indicate that clay, organic matter, elevation, and phosphorus were the critical factors influencing the spread of L. vulgare. According to the GAM prediction, 12.95% (13,799.79 ha) of the Namin County is potentially suitable for the L. vulgare. The spatial pattern of invasive species in rangelands can help make sound decisions in controlling the expansion of invasive species and related ecological implications. Invasive species (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Boosted regression trees (dpeaa)DE-He213 Generalized linear model (dpeaa)DE-He213 Generalized additive models (dpeaa)DE-He213 Moameri, Mehdi (orcid)0000-0003-2917-4736 aut Ghorbani, Ardavan aut Mostafazadeh, Raoof aut Esmali Ouri, Abazar aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 15(2022), 9 vom: 23. Apr. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:15 year:2022 number:9 day:23 month:04 https://dx.doi.org/10.1007/s12517-022-10137-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 9 23 04 |
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10.1007/s12517-022-10137-y doi (DE-627)SPR046828133 (SPR)s12517-022-10137-y-e DE-627 ger DE-627 rakwb eng Khanghah, Sahar Samadi verfasserin aut An insight into machine learning models to predict the distribution of Leucanthemum vulgare Lam. in northwestern rangelands of Iran 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences 2022 Abstract Invasions by non-native species are an increasing problem, especially at valuable rangelands. In the present research, the suitable habitat of the invasive species of Leucanthemum vulgare Lam., in the rangelands of the Namin County in northwest Iran, was assessed using the topographic, climatic, and soil variables. Four machine learning models, including random forest (RF), boosted regression trees (BRTs), generalized linear model (GLM), and generalized additive models (GAM), were used in the R environment. This research was conducted in May and June 2019–2020. The presence and absence of the L. vulgare were recorded using a stratified random sampling method using a global positioning system. The soil samples were taken at a depth of 0 to 30 cm from the presence and absence sites of L. vulgare. The results showed that GAM performed with 95% Kappa, 91% AUC, 88% TSS better than others and, followed by GLM, BRTs, and RF in decreasing order among the implemented models. The predictive performance of the GAM model using tenfold cross-validation in the Caret package indicate that clay, organic matter, elevation, and phosphorus were the critical factors influencing the spread of L. vulgare. According to the GAM prediction, 12.95% (13,799.79 ha) of the Namin County is potentially suitable for the L. vulgare. The spatial pattern of invasive species in rangelands can help make sound decisions in controlling the expansion of invasive species and related ecological implications. Invasive species (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Boosted regression trees (dpeaa)DE-He213 Generalized linear model (dpeaa)DE-He213 Generalized additive models (dpeaa)DE-He213 Moameri, Mehdi (orcid)0000-0003-2917-4736 aut Ghorbani, Ardavan aut Mostafazadeh, Raoof aut Esmali Ouri, Abazar aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 15(2022), 9 vom: 23. Apr. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:15 year:2022 number:9 day:23 month:04 https://dx.doi.org/10.1007/s12517-022-10137-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 9 23 04 |
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10.1007/s12517-022-10137-y doi (DE-627)SPR046828133 (SPR)s12517-022-10137-y-e DE-627 ger DE-627 rakwb eng Khanghah, Sahar Samadi verfasserin aut An insight into machine learning models to predict the distribution of Leucanthemum vulgare Lam. in northwestern rangelands of Iran 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences 2022 Abstract Invasions by non-native species are an increasing problem, especially at valuable rangelands. In the present research, the suitable habitat of the invasive species of Leucanthemum vulgare Lam., in the rangelands of the Namin County in northwest Iran, was assessed using the topographic, climatic, and soil variables. Four machine learning models, including random forest (RF), boosted regression trees (BRTs), generalized linear model (GLM), and generalized additive models (GAM), were used in the R environment. This research was conducted in May and June 2019–2020. The presence and absence of the L. vulgare were recorded using a stratified random sampling method using a global positioning system. The soil samples were taken at a depth of 0 to 30 cm from the presence and absence sites of L. vulgare. The results showed that GAM performed with 95% Kappa, 91% AUC, 88% TSS better than others and, followed by GLM, BRTs, and RF in decreasing order among the implemented models. The predictive performance of the GAM model using tenfold cross-validation in the Caret package indicate that clay, organic matter, elevation, and phosphorus were the critical factors influencing the spread of L. vulgare. According to the GAM prediction, 12.95% (13,799.79 ha) of the Namin County is potentially suitable for the L. vulgare. The spatial pattern of invasive species in rangelands can help make sound decisions in controlling the expansion of invasive species and related ecological implications. Invasive species (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Boosted regression trees (dpeaa)DE-He213 Generalized linear model (dpeaa)DE-He213 Generalized additive models (dpeaa)DE-He213 Moameri, Mehdi (orcid)0000-0003-2917-4736 aut Ghorbani, Ardavan aut Mostafazadeh, Raoof aut Esmali Ouri, Abazar aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 15(2022), 9 vom: 23. Apr. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:15 year:2022 number:9 day:23 month:04 https://dx.doi.org/10.1007/s12517-022-10137-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 9 23 04 |
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10.1007/s12517-022-10137-y doi (DE-627)SPR046828133 (SPR)s12517-022-10137-y-e DE-627 ger DE-627 rakwb eng Khanghah, Sahar Samadi verfasserin aut An insight into machine learning models to predict the distribution of Leucanthemum vulgare Lam. in northwestern rangelands of Iran 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Saudi Society for Geosciences 2022 Abstract Invasions by non-native species are an increasing problem, especially at valuable rangelands. In the present research, the suitable habitat of the invasive species of Leucanthemum vulgare Lam., in the rangelands of the Namin County in northwest Iran, was assessed using the topographic, climatic, and soil variables. Four machine learning models, including random forest (RF), boosted regression trees (BRTs), generalized linear model (GLM), and generalized additive models (GAM), were used in the R environment. This research was conducted in May and June 2019–2020. The presence and absence of the L. vulgare were recorded using a stratified random sampling method using a global positioning system. The soil samples were taken at a depth of 0 to 30 cm from the presence and absence sites of L. vulgare. The results showed that GAM performed with 95% Kappa, 91% AUC, 88% TSS better than others and, followed by GLM, BRTs, and RF in decreasing order among the implemented models. The predictive performance of the GAM model using tenfold cross-validation in the Caret package indicate that clay, organic matter, elevation, and phosphorus were the critical factors influencing the spread of L. vulgare. According to the GAM prediction, 12.95% (13,799.79 ha) of the Namin County is potentially suitable for the L. vulgare. The spatial pattern of invasive species in rangelands can help make sound decisions in controlling the expansion of invasive species and related ecological implications. Invasive species (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Boosted regression trees (dpeaa)DE-He213 Generalized linear model (dpeaa)DE-He213 Generalized additive models (dpeaa)DE-He213 Moameri, Mehdi (orcid)0000-0003-2917-4736 aut Ghorbani, Ardavan aut Mostafazadeh, Raoof aut Esmali Ouri, Abazar aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 15(2022), 9 vom: 23. Apr. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:15 year:2022 number:9 day:23 month:04 https://dx.doi.org/10.1007/s12517-022-10137-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 9 23 04 |
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Enthalten in Arabian journal of geosciences 15(2022), 9 vom: 23. Apr. volume:15 year:2022 number:9 day:23 month:04 |
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Invasive species Random forest Boosted regression trees Generalized linear model Generalized additive models |
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Arabian journal of geosciences |
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Khanghah, Sahar Samadi @@aut@@ Moameri, Mehdi @@aut@@ Ghorbani, Ardavan @@aut@@ Mostafazadeh, Raoof @@aut@@ Esmali Ouri, Abazar @@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">SPR046828133</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230507192129.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220424s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12517-022-10137-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR046828133</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12517-022-10137-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">Khanghah, Sahar Samadi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="3"><subfield code="a">An insight into machine learning models to predict the distribution of Leucanthemum vulgare Lam. in northwestern rangelands of Iran</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 Invasions by non-native species are an increasing problem, especially at valuable rangelands. In the present research, the suitable habitat of the invasive species of Leucanthemum vulgare Lam., in the rangelands of the Namin County in northwest Iran, was assessed using the topographic, climatic, and soil variables. Four machine learning models, including random forest (RF), boosted regression trees (BRTs), generalized linear model (GLM), and generalized additive models (GAM), were used in the R environment. This research was conducted in May and June 2019–2020. The presence and absence of the L. vulgare were recorded using a stratified random sampling method using a global positioning system. The soil samples were taken at a depth of 0 to 30 cm from the presence and absence sites of L. vulgare. The results showed that GAM performed with 95% Kappa, 91% AUC, 88% TSS better than others and, followed by GLM, BRTs, and RF in decreasing order among the implemented models. The predictive performance of the GAM model using tenfold cross-validation in the Caret package indicate that clay, organic matter, elevation, and phosphorus were the critical factors influencing the spread of L. vulgare. According to the GAM prediction, 12.95% (13,799.79 ha) of the Namin County is potentially suitable for the L. vulgare. 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Khanghah, Sahar Samadi |
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Khanghah, Sahar Samadi misc Invasive species misc Random forest misc Boosted regression trees misc Generalized linear model misc Generalized additive models An insight into machine learning models to predict the distribution of Leucanthemum vulgare Lam. in northwestern rangelands of Iran |
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An insight into machine learning models to predict the distribution of Leucanthemum vulgare Lam. in northwestern rangelands of Iran Invasive species (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Boosted regression trees (dpeaa)DE-He213 Generalized linear model (dpeaa)DE-He213 Generalized additive models (dpeaa)DE-He213 |
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insight into machine learning models to predict the distribution of leucanthemum vulgare lam. in northwestern rangelands of iran |
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An insight into machine learning models to predict the distribution of Leucanthemum vulgare Lam. in northwestern rangelands of Iran |
| abstract |
Abstract Invasions by non-native species are an increasing problem, especially at valuable rangelands. In the present research, the suitable habitat of the invasive species of Leucanthemum vulgare Lam., in the rangelands of the Namin County in northwest Iran, was assessed using the topographic, climatic, and soil variables. Four machine learning models, including random forest (RF), boosted regression trees (BRTs), generalized linear model (GLM), and generalized additive models (GAM), were used in the R environment. This research was conducted in May and June 2019–2020. The presence and absence of the L. vulgare were recorded using a stratified random sampling method using a global positioning system. The soil samples were taken at a depth of 0 to 30 cm from the presence and absence sites of L. vulgare. The results showed that GAM performed with 95% Kappa, 91% AUC, 88% TSS better than others and, followed by GLM, BRTs, and RF in decreasing order among the implemented models. The predictive performance of the GAM model using tenfold cross-validation in the Caret package indicate that clay, organic matter, elevation, and phosphorus were the critical factors influencing the spread of L. vulgare. According to the GAM prediction, 12.95% (13,799.79 ha) of the Namin County is potentially suitable for the L. vulgare. The spatial pattern of invasive species in rangelands can help make sound decisions in controlling the expansion of invasive species and related ecological implications. © Saudi Society for Geosciences 2022 |
| abstractGer |
Abstract Invasions by non-native species are an increasing problem, especially at valuable rangelands. In the present research, the suitable habitat of the invasive species of Leucanthemum vulgare Lam., in the rangelands of the Namin County in northwest Iran, was assessed using the topographic, climatic, and soil variables. Four machine learning models, including random forest (RF), boosted regression trees (BRTs), generalized linear model (GLM), and generalized additive models (GAM), were used in the R environment. This research was conducted in May and June 2019–2020. The presence and absence of the L. vulgare were recorded using a stratified random sampling method using a global positioning system. The soil samples were taken at a depth of 0 to 30 cm from the presence and absence sites of L. vulgare. The results showed that GAM performed with 95% Kappa, 91% AUC, 88% TSS better than others and, followed by GLM, BRTs, and RF in decreasing order among the implemented models. The predictive performance of the GAM model using tenfold cross-validation in the Caret package indicate that clay, organic matter, elevation, and phosphorus were the critical factors influencing the spread of L. vulgare. According to the GAM prediction, 12.95% (13,799.79 ha) of the Namin County is potentially suitable for the L. vulgare. The spatial pattern of invasive species in rangelands can help make sound decisions in controlling the expansion of invasive species and related ecological implications. © Saudi Society for Geosciences 2022 |
| abstract_unstemmed |
Abstract Invasions by non-native species are an increasing problem, especially at valuable rangelands. In the present research, the suitable habitat of the invasive species of Leucanthemum vulgare Lam., in the rangelands of the Namin County in northwest Iran, was assessed using the topographic, climatic, and soil variables. Four machine learning models, including random forest (RF), boosted regression trees (BRTs), generalized linear model (GLM), and generalized additive models (GAM), were used in the R environment. This research was conducted in May and June 2019–2020. The presence and absence of the L. vulgare were recorded using a stratified random sampling method using a global positioning system. The soil samples were taken at a depth of 0 to 30 cm from the presence and absence sites of L. vulgare. The results showed that GAM performed with 95% Kappa, 91% AUC, 88% TSS better than others and, followed by GLM, BRTs, and RF in decreasing order among the implemented models. The predictive performance of the GAM model using tenfold cross-validation in the Caret package indicate that clay, organic matter, elevation, and phosphorus were the critical factors influencing the spread of L. vulgare. According to the GAM prediction, 12.95% (13,799.79 ha) of the Namin County is potentially suitable for the L. vulgare. The spatial pattern of invasive species in rangelands can help make sound decisions in controlling the expansion of invasive species and related ecological implications. © Saudi Society for Geosciences 2022 |
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| container_issue |
9 |
| title_short |
An insight into machine learning models to predict the distribution of Leucanthemum vulgare Lam. in northwestern rangelands of Iran |
| url |
https://dx.doi.org/10.1007/s12517-022-10137-y |
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| author2 |
Moameri, Mehdi Ghorbani, Ardavan Mostafazadeh, Raoof Esmali Ouri, Abazar |
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Moameri, Mehdi Ghorbani, Ardavan Mostafazadeh, Raoof Esmali Ouri, Abazar |
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| doi_str |
10.1007/s12517-022-10137-y |
| up_date |
2024-07-04T00:36:09.539Z |
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| score |
7.399687 |