Matches in SemOpenAlex for { <https://semopenalex.org/work/W2396704715> ?p ?o ?g. }
Showing items 1 to 86 of
86
with 100 items per page.
- W2396704715 endingPage "49" @default.
- W2396704715 startingPage "38" @default.
- W2396704715 abstract "Abstract Change allocation is an important step in the Land Use Land Cover (LULC) change modelling. Many established LULC models use transition potential maps for the allocation of the estimated land demand. This study compares three commonly used techniques for transition potential modelling: (1) Multi-Layer Perceptron Neural Network (MLP), (2) Logistic Regression (LogReg), and (3) Similarity Weighted Instance-based Learning (SimWeight); and evaluates their applicability for built-up transitions. A case study has been taken from Guwahati city, in North-East India which experiences heterogeneous built-up growth in a limited area within the large topographic variations. With the same set of input and tested driving factors, all three models were simulated for the period 1989–2001 to produce the transition potential maps for 2011 and same amount of land demands, as in 2011 were allocated on the potential maps. The validation was done by (1) a multi-resolution validation method and (2) a region based method using the wards of the city. For this particular study, with the specific landscape environment and scale, MLP produced the most accurate change and predicted areas. The LogReg simulated the no change areas the most accurately, while the SimWeight could generate the edge extensions satisfactorily. We presented a detailed comparison of the change potentials and simulated maps and discuss the importance of evaluating the ability of the transition potential model used for LULC model. The results from this study can assist the LULC modelers to validate their transition potential models for generating accurate prediction maps. It can be also useful for planners and decision makers of Guwahati city and similar landscape, environment, scale in producing accurate transition potential zones for precise built-up growth modelling." @default.
- W2396704715 created "2016-06-24" @default.
- W2396704715 creator A5015432540 @default.
- W2396704715 creator A5027217605 @default.
- W2396704715 creator A5063456070 @default.
- W2396704715 date "2016-09-01" @default.
- W2396704715 modified "2023-10-14" @default.
- W2396704715 title "Comparing three transition potential models: A case study of built-up transitions in North-East India" @default.
- W2396704715 cites W1571538446 @default.
- W2396704715 cites W1597814351 @default.
- W2396704715 cites W1804184653 @default.
- W2396704715 cites W1965865770 @default.
- W2396704715 cites W1968885079 @default.
- W2396704715 cites W1970746706 @default.
- W2396704715 cites W1973186727 @default.
- W2396704715 cites W1975935356 @default.
- W2396704715 cites W1983668540 @default.
- W2396704715 cites W1983898356 @default.
- W2396704715 cites W1998807797 @default.
- W2396704715 cites W2001877646 @default.
- W2396704715 cites W2006255071 @default.
- W2396704715 cites W2008633810 @default.
- W2396704715 cites W2013168176 @default.
- W2396704715 cites W2029109413 @default.
- W2396704715 cites W2035488637 @default.
- W2396704715 cites W2037387228 @default.
- W2396704715 cites W2052250761 @default.
- W2396704715 cites W2054855365 @default.
- W2396704715 cites W2062696697 @default.
- W2396704715 cites W2068735183 @default.
- W2396704715 cites W2081652659 @default.
- W2396704715 cites W2091720785 @default.
- W2396704715 cites W2093422142 @default.
- W2396704715 cites W2107300678 @default.
- W2396704715 cites W2135822449 @default.
- W2396704715 cites W2157528778 @default.
- W2396704715 cites W2159421932 @default.
- W2396704715 cites W2165294228 @default.
- W2396704715 cites W2496025094 @default.
- W2396704715 doi "https://doi.org/10.1016/j.compenvurbsys.2016.04.009" @default.
- W2396704715 hasPublicationYear "2016" @default.
- W2396704715 type Work @default.
- W2396704715 sameAs 2396704715 @default.
- W2396704715 citedByCount "23" @default.
- W2396704715 countsByYear W23967047152016 @default.
- W2396704715 countsByYear W23967047152017 @default.
- W2396704715 countsByYear W23967047152018 @default.
- W2396704715 countsByYear W23967047152019 @default.
- W2396704715 countsByYear W23967047152020 @default.
- W2396704715 countsByYear W23967047152021 @default.
- W2396704715 countsByYear W23967047152022 @default.
- W2396704715 countsByYear W23967047152023 @default.
- W2396704715 crossrefType "journal-article" @default.
- W2396704715 hasAuthorship W2396704715A5015432540 @default.
- W2396704715 hasAuthorship W2396704715A5027217605 @default.
- W2396704715 hasAuthorship W2396704715A5063456070 @default.
- W2396704715 hasConcept C104317684 @default.
- W2396704715 hasConcept C185592680 @default.
- W2396704715 hasConcept C194232998 @default.
- W2396704715 hasConcept C205649164 @default.
- W2396704715 hasConcept C55493867 @default.
- W2396704715 hasConceptScore W2396704715C104317684 @default.
- W2396704715 hasConceptScore W2396704715C185592680 @default.
- W2396704715 hasConceptScore W2396704715C194232998 @default.
- W2396704715 hasConceptScore W2396704715C205649164 @default.
- W2396704715 hasConceptScore W2396704715C55493867 @default.
- W2396704715 hasLocation W23967047151 @default.
- W2396704715 hasOpenAccess W2396704715 @default.
- W2396704715 hasPrimaryLocation W23967047151 @default.
- W2396704715 hasRelatedWork W1727353606 @default.
- W2396704715 hasRelatedWork W2126787609 @default.
- W2396704715 hasRelatedWork W2277236374 @default.
- W2396704715 hasRelatedWork W2292168444 @default.
- W2396704715 hasRelatedWork W2360747494 @default.
- W2396704715 hasRelatedWork W2392008502 @default.
- W2396704715 hasRelatedWork W2748952813 @default.
- W2396704715 hasRelatedWork W2899084033 @default.
- W2396704715 hasRelatedWork W2975897150 @default.
- W2396704715 hasRelatedWork W4280540706 @default.
- W2396704715 hasVolume "59" @default.
- W2396704715 isParatext "false" @default.
- W2396704715 isRetracted "false" @default.
- W2396704715 magId "2396704715" @default.
- W2396704715 workType "article" @default.