FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313644977> ?p ?o ?g. }

• W4313644977 abstract "Worldwide, water security is adversely affected by factors such as population growth, rural–urban migration, climate, hydrological conditions, over-abstraction of groundwater, and increased per-capita water use. Water security modeling is one of the key strategies to better manage water safety and develop appropriate policies to improve security. In view of the growing global demand for safe water, intelligent methods and algorithms must be developed. Therefore, this paper proposes an integrated interval type-2 Fuzzy VIseKriterijumska Optimizcija I Kompromisno Resenje (IT2FVIKOR) with unsupervised machine learning (ML). This includes IT2FVIKOR for ranking and selecting a set of alternatives. Unsupervised machine learning includes hierarchical clustering, self-organizing map, and autoencoder for clustering, silhouette analysis and elbow method to find the most optimal cluster count, and finally Adjusted Rank Index (ARI) to find the best comparison within two clusters. This proposed integrated method can be divided into a two-phase fuzzy-machine learning-based framework to select the best water security strategies and categorize the polluted area using the water datasets from the Terengganu River, one of Malaysia’s rivers. Phase 1 focuses on the IT2FVIKOR method to select five different strategies with five different criteria using five decision makers for finding the best water security strategies. Phase 2 continues the unsupervised machine learning where three different clustering algorithms, namely, hierarchical clustering, self-organizing map, and autoencoder, are used to cluster the polluted area in the Terengganu River. Silhouette analysis is applied along with the clustering algorithms to estimate the number of optimal clusters in a dataset. Then, the ARI is applied to find the best comparison within the original data with hierarchical clustering, self-organizing map, and autoencoder. Next, the elbow method is applied to double-confirm the best clusters for each clustering algorithm. Last, lists of polluted areas in each cluster are retrieved. Finally, this 2-phase fuzzy-Machine learning–based framework offers an alternative intelligent model to solve the water security problems and find the most polluted area." @default.
• W4313644977 created "2023-01-07" @default.
• W4313644977 creator A5021296430 @default.
• W4313644977 creator A5032140983 @default.
• W4313644977 creator A5050531504 @default.
• W4313644977 creator A5075463533 @default.
• W4313644977 creator A5081905332 @default.
• W4313644977 date "2023-01-06" @default.
• W4313644977 modified "2023-10-04" @default.
• W4313644977 title "An analysis of finding the best strategies of water security for water source areas using an integrated IT2FVIKOR with machine learning" @default.
• W4313644977 cites W1850100130 @default.
• W4313644977 cites W1987971958 @default.
• W4313644977 cites W1997797066 @default.
• W4313644977 cites W1998871699 @default.
• W4313644977 cites W2006255103 @default.
• W4313644977 cites W2011301426 @default.
• W4313644977 cites W2011430131 @default.
• W4313644977 cites W2015053255 @default.
• W4313644977 cites W2016381774 @default.
• W4313644977 cites W2038547851 @default.
• W4313644977 cites W2053154970 @default.
• W4313644977 cites W2110802877 @default.
• W4313644977 cites W2112364454 @default.
• W4313644977 cites W2118585668 @default.
• W4313644977 cites W2123679438 @default.
• W4313644977 cites W2141326363 @default.
• W4313644977 cites W2469643629 @default.
• W4313644977 cites W2558067512 @default.
• W4313644977 cites W2565575913 @default.
• W4313644977 cites W2807918403 @default.
• W4313644977 cites W2887028048 @default.
• W4313644977 cites W2894597154 @default.
• W4313644977 cites W2952809827 @default.
• W4313644977 cites W2968304380 @default.
• W4313644977 cites W2978675114 @default.
• W4313644977 cites W2982565920 @default.
• W4313644977 cites W3016794093 @default.
• W4313644977 cites W3021554815 @default.
• W4313644977 cites W3037044802 @default.
• W4313644977 cites W3090045915 @default.
• W4313644977 cites W3112159384 @default.
• W4313644977 cites W3119540806 @default.
• W4313644977 cites W3133916786 @default.
• W4313644977 cites W3162573363 @default.
• W4313644977 cites W3189461415 @default.
• W4313644977 cites W3198859582 @default.
• W4313644977 cites W3203505658 @default.
• W4313644977 cites W4206908936 @default.
• W4313644977 cites W4206977179 @default.
• W4313644977 cites W4210711142 @default.
• W4313644977 cites W4212943671 @default.
• W4313644977 cites W4213436317 @default.
• W4313644977 cites W4221091478 @default.
• W4313644977 cites W4225834163 @default.
• W4313644977 cites W4235169531 @default.
• W4313644977 cites W4255087125 @default.
• W4313644977 cites W592067329 @default.
• W4313644977 doi "https://doi.org/10.3389/fenvs.2022.971129" @default.
• W4313644977 hasPublicationYear "2023" @default.
• W4313644977 type Work @default.
• W4313644977 citedByCount "0" @default.
• W4313644977 crossrefType "journal-article" @default.
• W4313644977 hasAuthorship W4313644977A5021296430 @default.
• W4313644977 hasAuthorship W4313644977A5032140983 @default.
• W4313644977 hasAuthorship W4313644977A5050531504 @default.
• W4313644977 hasAuthorship W4313644977A5075463533 @default.
• W4313644977 hasAuthorship W4313644977A5081905332 @default.
• W4313644977 hasBestOaLocation W43136449771 @default.
• W4313644977 hasConcept C101738243 @default.
• W4313644977 hasConcept C119857082 @default.
• W4313644977 hasConcept C124101348 @default.
• W4313644977 hasConcept C153823671 @default.
• W4313644977 hasConcept C154945302 @default.
• W4313644977 hasConcept C18903297 @default.
• W4313644977 hasConcept C189430467 @default.
• W4313644977 hasConcept C2778570914 @default.
• W4313644977 hasConcept C41008148 @default.
• W4313644977 hasConcept C50644808 @default.
• W4313644977 hasConcept C73555534 @default.
• W4313644977 hasConcept C8038995 @default.
• W4313644977 hasConcept C86803240 @default.
• W4313644977 hasConceptScore W4313644977C101738243 @default.
• W4313644977 hasConceptScore W4313644977C119857082 @default.
• W4313644977 hasConceptScore W4313644977C124101348 @default.
• W4313644977 hasConceptScore W4313644977C153823671 @default.
• W4313644977 hasConceptScore W4313644977C154945302 @default.
• W4313644977 hasConceptScore W4313644977C18903297 @default.
• W4313644977 hasConceptScore W4313644977C189430467 @default.
• W4313644977 hasConceptScore W4313644977C2778570914 @default.
• W4313644977 hasConceptScore W4313644977C41008148 @default.
• W4313644977 hasConceptScore W4313644977C50644808 @default.
• W4313644977 hasConceptScore W4313644977C73555534 @default.
• W4313644977 hasConceptScore W4313644977C8038995 @default.
• W4313644977 hasConceptScore W4313644977C86803240 @default.
• W4313644977 hasLocation W43136449771 @default.
• W4313644977 hasOpenAccess W4313644977 @default.
• W4313644977 hasPrimaryLocation W43136449771 @default.
• W4313644977 hasRelatedWork W2784313445 @default.
• W4313644977 hasRelatedWork W3044458868 @default.
• W4313644977 hasRelatedWork W3046775127 @default.

• next