Assessing erosion prone areas in a watershed using interval rough-analytical hierarchy process (IR-AHP) and fuzzy logic (FL)
Soil erosion is one of the major land loss problems in agricultural land and is regarded as a serious environmental hazard worldwide. This study focused on watershed prioritization using morphometric parameters using Fuzzy Logic (FL), Interval Rough-Analytical Hierarchy Process (IR-AHP) and Geograph...
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| Veröffentlicht in: | Stochastic environmental research and risk assessment 2022-02, Vol.36 (2), p.297-312 |
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| creator | Meshram, Sarita Gajbhiye Singh, Vijay P. Kahya, Ercan Sepehri, Mehdi Meshram, Chandrashekhar Hasan, Mohd Abul Islam, Saiful Duc, Pham Anh |
| description | Soil erosion is one of the major land loss problems in agricultural land and is regarded as a serious environmental hazard worldwide. This study focused on watershed prioritization using morphometric parameters using Fuzzy Logic (FL), Interval Rough-Analytical Hierarchy Process (IR-AHP) and Geographic Information Systems (GIS) integration for Gusru Watershed, India. Fourteen morphometric parameters, including circulatory ratio (
R
c
), form factor (
R
f
), elongation ratio (
R
e
), compactness coefficient (
C
c
), drainage density (
D
d
), stream frequency (
F
s
), texture ratio (
T
), relief ratio (
R
h
), relative relief (
R
r
), ruggedness number (
R
N
), bifurcation ratio (
R
b
), average slope (
S
a
), length of overland flow (
L
o
), and hypsometric integral (HI) were evaluated to determine the erosion susceptibility. Each morphometric parameter was assigned a weight value by the FL and IR-AHP methods, and mapping and analysis were then carried out in the GIS environment. Our results showed that the sub-watersheds (SW) 9, 2, and 11 were most susceptible to soil erosion and the sub-watershed 1 was the least from the viewpoint of soil erosion ranking. |
| doi_str_mv | 10.1007/s00477-021-02134-6 |
| format | Article |
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R
c
), form factor (
R
f
), elongation ratio (
R
e
), compactness coefficient (
C
c
), drainage density (
D
d
), stream frequency (
F
s
), texture ratio (
T
), relief ratio (
R
h
), relative relief (
R
r
), ruggedness number (
R
N
), bifurcation ratio (
R
b
), average slope (
S
a
), length of overland flow (
L
o
), and hypsometric integral (HI) were evaluated to determine the erosion susceptibility. Each morphometric parameter was assigned a weight value by the FL and IR-AHP methods, and mapping and analysis were then carried out in the GIS environment. Our results showed that the sub-watersheds (SW) 9, 2, and 11 were most susceptible to soil erosion and the sub-watershed 1 was the least from the viewpoint of soil erosion ranking.</description><identifier>ISSN: 1436-3240</identifier><identifier>EISSN: 1436-3259</identifier><identifier>DOI: 10.1007/s00477-021-02134-6</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Agricultural land ; Analytic hierarchy process ; Aquatic Pollution ; Chemistry and Earth Sciences ; Computational Intelligence ; Computer Science ; Drainage density ; Earth and Environmental Science ; Earth Sciences ; Elongation ; Environment ; Environmental hazards ; Form factors ; Fuzzy logic ; Geographic information systems ; Information processing ; Math. Appl. in Environmental Science ; Mathematical analysis ; Original Paper ; Overland flow ; Parameters ; Physics ; Probability Theory and Stochastic Processes ; Remote sensing ; Ruggedness ; Soil erosion ; Statistics for Engineering ; Surface runoff ; Waste Water Technology ; Water Management ; Water Pollution Control ; Watersheds</subject><ispartof>Stochastic environmental research and risk assessment, 2022-02, Vol.36 (2), p.297-312</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-819d5e18bb9f9bfbfa0ac7a93664c47b4c2d9e1ce01bdcab0686f4a81592a3a23</citedby><cites>FETCH-LOGICAL-c319t-819d5e18bb9f9bfbfa0ac7a93664c47b4c2d9e1ce01bdcab0686f4a81592a3a23</cites><orcidid>0000-0001-5453-3791</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00477-021-02134-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00477-021-02134-6$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids></links><search><creatorcontrib>Meshram, Sarita Gajbhiye</creatorcontrib><creatorcontrib>Singh, Vijay P.</creatorcontrib><creatorcontrib>Kahya, Ercan</creatorcontrib><creatorcontrib>Sepehri, Mehdi</creatorcontrib><creatorcontrib>Meshram, Chandrashekhar</creatorcontrib><creatorcontrib>Hasan, Mohd Abul</creatorcontrib><creatorcontrib>Islam, Saiful</creatorcontrib><creatorcontrib>Duc, Pham Anh</creatorcontrib><title>Assessing erosion prone areas in a watershed using interval rough-analytical hierarchy process (IR-AHP) and fuzzy logic (FL)</title><title>Stochastic environmental research and risk assessment</title><addtitle>Stoch Environ Res Risk Assess</addtitle><description>Soil erosion is one of the major land loss problems in agricultural land and is regarded as a serious environmental hazard worldwide. This study focused on watershed prioritization using morphometric parameters using Fuzzy Logic (FL), Interval Rough-Analytical Hierarchy Process (IR-AHP) and Geographic Information Systems (GIS) integration for Gusru Watershed, India. Fourteen morphometric parameters, including circulatory ratio (
R
c
), form factor (
R
f
), elongation ratio (
R
e
), compactness coefficient (
C
c
), drainage density (
D
d
), stream frequency (
F
s
), texture ratio (
T
), relief ratio (
R
h
), relative relief (
R
r
), ruggedness number (
R
N
), bifurcation ratio (
R
b
), average slope (
S
a
), length of overland flow (
L
o
), and hypsometric integral (HI) were evaluated to determine the erosion susceptibility. Each morphometric parameter was assigned a weight value by the FL and IR-AHP methods, and mapping and analysis were then carried out in the GIS environment. Our results showed that the sub-watersheds (SW) 9, 2, and 11 were most susceptible to soil erosion and the sub-watershed 1 was the least from the viewpoint of soil erosion ranking.</description><subject>Agricultural land</subject><subject>Analytic hierarchy process</subject><subject>Aquatic Pollution</subject><subject>Chemistry and Earth Sciences</subject><subject>Computational Intelligence</subject><subject>Computer Science</subject><subject>Drainage density</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Elongation</subject><subject>Environment</subject><subject>Environmental hazards</subject><subject>Form factors</subject><subject>Fuzzy logic</subject><subject>Geographic information systems</subject><subject>Information processing</subject><subject>Math. Appl. in Environmental Science</subject><subject>Mathematical analysis</subject><subject>Original Paper</subject><subject>Overland flow</subject><subject>Parameters</subject><subject>Physics</subject><subject>Probability Theory and Stochastic Processes</subject><subject>Remote sensing</subject><subject>Ruggedness</subject><subject>Soil erosion</subject><subject>Statistics for Engineering</subject><subject>Surface runoff</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><subject>Watersheds</subject><issn>1436-3240</issn><issn>1436-3259</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kEtLw0AUhYMoWLR_wNWAm3YRnVcesyzF2kJBEV0PdyaTh8SkzjRKij_eSSO6c3G5D845XL4guCL4hmCc3DqMeZKEmJKhGA_jk2BCOItDRiNx-jtzfB5MnauUN0VMCIInwdfCOeNvTYGMbV3VNmhn28YgsAYcqhoE6BP2xrrSZKg7CqvG7x9QI9t2RRlCA3W_r7Q_lJWxYHXZDyHa56LZ5ilcrB_nCJoM5d3h0KO6LSqNZqvt_DI4y6F2ZvrTL4KX1d3zch1uH-43y8U21IyIfZgSkUWGpEqJXKhc5YBBJyBYHHPNE8U1zYQh2mCiMg0Kx2mcc0hJJCgwoOwiuB5z_VfvnXF7-dp21r_tJI0pTzHByaCio0p7EM6aXO5s9Qa2lwTLAbQcQUsPWR5By9ib2GhyXtwUxv5F_-P6Boimgac</recordid><startdate>20220201</startdate><enddate>20220201</enddate><creator>Meshram, Sarita Gajbhiye</creator><creator>Singh, Vijay P.</creator><creator>Kahya, Ercan</creator><creator>Sepehri, Mehdi</creator><creator>Meshram, Chandrashekhar</creator><creator>Hasan, Mohd Abul</creator><creator>Islam, Saiful</creator><creator>Duc, Pham Anh</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7XB</scope><scope>88I</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>KR7</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0W</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0001-5453-3791</orcidid></search><sort><creationdate>20220201</creationdate><title>Assessing erosion prone areas in a watershed using interval rough-analytical hierarchy process (IR-AHP) and fuzzy logic (FL)</title><author>Meshram, Sarita Gajbhiye ; Singh, Vijay P. ; Kahya, Ercan ; Sepehri, Mehdi ; Meshram, Chandrashekhar ; Hasan, Mohd Abul ; Islam, Saiful ; Duc, Pham Anh</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-819d5e18bb9f9bfbfa0ac7a93664c47b4c2d9e1ce01bdcab0686f4a81592a3a23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Agricultural land</topic><topic>Analytic hierarchy process</topic><topic>Aquatic Pollution</topic><topic>Chemistry and Earth Sciences</topic><topic>Computational Intelligence</topic><topic>Computer Science</topic><topic>Drainage density</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Elongation</topic><topic>Environment</topic><topic>Environmental hazards</topic><topic>Form factors</topic><topic>Fuzzy logic</topic><topic>Geographic information systems</topic><topic>Information processing</topic><topic>Math. Appl. in Environmental Science</topic><topic>Mathematical analysis</topic><topic>Original Paper</topic><topic>Overland flow</topic><topic>Parameters</topic><topic>Physics</topic><topic>Probability Theory and Stochastic Processes</topic><topic>Remote sensing</topic><topic>Ruggedness</topic><topic>Soil erosion</topic><topic>Statistics for Engineering</topic><topic>Surface runoff</topic><topic>Waste Water Technology</topic><topic>Water Management</topic><topic>Water Pollution Control</topic><topic>Watersheds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Meshram, Sarita Gajbhiye</creatorcontrib><creatorcontrib>Singh, Vijay P.</creatorcontrib><creatorcontrib>Kahya, Ercan</creatorcontrib><creatorcontrib>Sepehri, Mehdi</creatorcontrib><creatorcontrib>Meshram, Chandrashekhar</creatorcontrib><creatorcontrib>Hasan, Mohd Abul</creatorcontrib><creatorcontrib>Islam, Saiful</creatorcontrib><creatorcontrib>Duc, Pham Anh</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>DELNET Engineering & Technology Collection</collection><collection>Environment Abstracts</collection><jtitle>Stochastic environmental research and risk assessment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Meshram, Sarita Gajbhiye</au><au>Singh, Vijay P.</au><au>Kahya, Ercan</au><au>Sepehri, Mehdi</au><au>Meshram, Chandrashekhar</au><au>Hasan, Mohd Abul</au><au>Islam, Saiful</au><au>Duc, Pham Anh</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessing erosion prone areas in a watershed using interval rough-analytical hierarchy process (IR-AHP) and fuzzy logic (FL)</atitle><jtitle>Stochastic environmental research and risk assessment</jtitle><stitle>Stoch Environ Res Risk Assess</stitle><date>2022-02-01</date><risdate>2022</risdate><volume>36</volume><issue>2</issue><spage>297</spage><epage>312</epage><pages>297-312</pages><issn>1436-3240</issn><eissn>1436-3259</eissn><abstract>Soil erosion is one of the major land loss problems in agricultural land and is regarded as a serious environmental hazard worldwide. This study focused on watershed prioritization using morphometric parameters using Fuzzy Logic (FL), Interval Rough-Analytical Hierarchy Process (IR-AHP) and Geographic Information Systems (GIS) integration for Gusru Watershed, India. Fourteen morphometric parameters, including circulatory ratio (
R
c
), form factor (
R
f
), elongation ratio (
R
e
), compactness coefficient (
C
c
), drainage density (
D
d
), stream frequency (
F
s
), texture ratio (
T
), relief ratio (
R
h
), relative relief (
R
r
), ruggedness number (
R
N
), bifurcation ratio (
R
b
), average slope (
S
a
), length of overland flow (
L
o
), and hypsometric integral (HI) were evaluated to determine the erosion susceptibility. Each morphometric parameter was assigned a weight value by the FL and IR-AHP methods, and mapping and analysis were then carried out in the GIS environment. Our results showed that the sub-watersheds (SW) 9, 2, and 11 were most susceptible to soil erosion and the sub-watershed 1 was the least from the viewpoint of soil erosion ranking.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00477-021-02134-6</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0001-5453-3791</orcidid></addata></record> |
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| ispartof | Stochastic environmental research and risk assessment, 2022-02, Vol.36 (2), p.297-312 |
| issn | 1436-3240 1436-3259 |
| language | eng |
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| source | Springer Nature - Complete Springer Journals |
| subjects | Agricultural land Analytic hierarchy process Aquatic Pollution Chemistry and Earth Sciences Computational Intelligence Computer Science Drainage density Earth and Environmental Science Earth Sciences Elongation Environment Environmental hazards Form factors Fuzzy logic Geographic information systems Information processing Math. Appl. in Environmental Science Mathematical analysis Original Paper Overland flow Parameters Physics Probability Theory and Stochastic Processes Remote sensing Ruggedness Soil erosion Statistics for Engineering Surface runoff Waste Water Technology Water Management Water Pollution Control Watersheds |
| title | Assessing erosion prone areas in a watershed using interval rough-analytical hierarchy process (IR-AHP) and fuzzy logic (FL) |
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