FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3162958620> ?p ?o ?g. }

• W3162958620 endingPage "108450" @default.
• W3162958620 startingPage "108450" @default.
• W3162958620 abstract "• New information on the frost prediction for forest. • Frost forecasting from spatial data. • Machine learning algorithms to classify frost risk probability. • High prediction performance using Random Forest classifier. • Transferability of the predictive approach for other agricultural plantations. Brazil is one of the leading timber producers in the world. However, in South Brazil, frost events frequently cause damage and reduce yield in forest plantations, a situation predicted to become more common under global change scenarios. This raises the need for low cost and efficient tools, such as machine learning algorithms to improve forecasting of frost risk. This study used machine learning algorithms to create zoning classifications forecasting frost risk for forest plantations located in the south-central region of Rio Grande do Sul State, Brazil. For this, we gathered and processed data from a local geodatabase (i.e. high-spatial-resolution contour lines, hydrography, and forest stands limits) comprising 30 management units with consistent historical data of frost occurrence. Then, we generated possible local-scale predictors of frost occurrence, which included longitude, latitude, elevation, relative altitude, relief orientation, and Euclidean distance from hydrography. We carried out tests of three machine learning classifiers (Random Forest – RF; Support Vector Machine-SVM and Multi-layer Perceptron-MLP) in order to determine which would most accurately predict frost occurrence. We found that RF provided the highest accuracy (> 90%), as well as the smallest percentages of class-specific errors (i.e. commission and omission errors), when compared to SVM and MLP. Latitude was the most important predictor of frost occurrence when using RF. Conversely, MLP performed worst, especially for classifying frost occurrence versus non-occurrence, and therefore had the highest percentage of class-specific errors. Our findings lead us to conclude that RF is the most proficient algorithm for forecasting frost occurrence from local-scale geomorphological data, without the need for high-cost investment in micro-meteorological sensors to monitor climate frost events linking temperature to plant damage. With increasing global climate extreme events, accurate risk zoning is essential for planning strategies of plantation at the landscape scale." @default.
• W3162958620 created "2021-05-24" @default.
• W3162958620 creator A5016543121 @default.
• W3162958620 creator A5021457643 @default.
• W3162958620 creator A5039916387 @default.
• W3162958620 creator A5045065046 @default.
• W3162958620 creator A5047282137 @default.
• W3162958620 creator A5053136067 @default.
• W3162958620 creator A5062884995 @default.
• W3162958620 creator A5066560220 @default.
• W3162958620 creator A5091290102 @default.
• W3162958620 date "2021-08-01" @default.
• W3162958620 modified "2023-09-30" @default.
• W3162958620 title "Forecasting frost risk in forest plantations by the combination of spatial data and machine learning algorithms" @default.
• W3162958620 cites W1520812622 @default.
• W3162958620 cites W180836830 @default.
• W3162958620 cites W1831050183 @default.
• W3162958620 cites W1855682752 @default.
• W3162958620 cites W1912080753 @default.
• W3162958620 cites W1977905297 @default.
• W3162958620 cites W1981861271 @default.
• W3162958620 cites W1992442607 @default.
• W3162958620 cites W1997204615 @default.
• W3162958620 cites W1998851574 @default.
• W3162958620 cites W2003651989 @default.
• W3162958620 cites W2008496103 @default.
• W3162958620 cites W2008951609 @default.
• W3162958620 cites W2019879201 @default.
• W3162958620 cites W2054584137 @default.
• W3162958620 cites W2068689590 @default.
• W3162958620 cites W2070332790 @default.
• W3162958620 cites W2082340057 @default.
• W3162958620 cites W2088123298 @default.
• W3162958620 cites W2105981176 @default.
• W3162958620 cites W2361227687 @default.
• W3162958620 cites W2409841266 @default.
• W3162958620 cites W2411428493 @default.
• W3162958620 cites W2413492692 @default.
• W3162958620 cites W2474679271 @default.
• W3162958620 cites W2511283079 @default.
• W3162958620 cites W2551944316 @default.
• W3162958620 cites W2594398464 @default.
• W3162958620 cites W2756138331 @default.
• W3162958620 cites W2775196825 @default.
• W3162958620 cites W2782380951 @default.
• W3162958620 cites W2809828947 @default.
• W3162958620 cites W2884738788 @default.
• W3162958620 cites W2889467566 @default.
• W3162958620 cites W2890760507 @default.
• W3162958620 cites W2899849657 @default.
• W3162958620 cites W2911964244 @default.
• W3162958620 cites W2951251697 @default.
• W3162958620 cites W2995150843 @default.
• W3162958620 cites W3014824265 @default.
• W3162958620 cites W4239510810 @default.
• W3162958620 doi "https://doi.org/10.1016/j.agrformet.2021.108450" @default.
• W3162958620 hasPublicationYear "2021" @default.
• W3162958620 type Work @default.
• W3162958620 sameAs 3162958620 @default.
• W3162958620 citedByCount "11" @default.
• W3162958620 countsByYear W31629586202021 @default.
• W3162958620 countsByYear W31629586202022 @default.
• W3162958620 countsByYear W31629586202023 @default.
• W3162958620 crossrefType "journal-article" @default.
• W3162958620 hasAuthorship W3162958620A5016543121 @default.
• W3162958620 hasAuthorship W3162958620A5021457643 @default.
• W3162958620 hasAuthorship W3162958620A5039916387 @default.
• W3162958620 hasAuthorship W3162958620A5045065046 @default.
• W3162958620 hasAuthorship W3162958620A5047282137 @default.
• W3162958620 hasAuthorship W3162958620A5053136067 @default.
• W3162958620 hasAuthorship W3162958620A5062884995 @default.
• W3162958620 hasAuthorship W3162958620A5066560220 @default.
• W3162958620 hasAuthorship W3162958620A5091290102 @default.
• W3162958620 hasConcept C11413529 @default.
• W3162958620 hasConcept C119857082 @default.
• W3162958620 hasConcept C12267149 @default.
• W3162958620 hasConcept C153294291 @default.
• W3162958620 hasConcept C154945302 @default.
• W3162958620 hasConcept C169258074 @default.
• W3162958620 hasConcept C205649164 @default.
• W3162958620 hasConcept C41008148 @default.
• W3162958620 hasConcept C4988496 @default.
• W3162958620 hasConceptScore W3162958620C11413529 @default.
• W3162958620 hasConceptScore W3162958620C119857082 @default.
• W3162958620 hasConceptScore W3162958620C12267149 @default.
• W3162958620 hasConceptScore W3162958620C153294291 @default.
• W3162958620 hasConceptScore W3162958620C154945302 @default.
• W3162958620 hasConceptScore W3162958620C169258074 @default.
• W3162958620 hasConceptScore W3162958620C205649164 @default.
• W3162958620 hasConceptScore W3162958620C41008148 @default.
• W3162958620 hasConceptScore W3162958620C4988496 @default.
• W3162958620 hasFunder F4320316427 @default.
• W3162958620 hasLocation W31629586201 @default.
• W3162958620 hasOpenAccess W3162958620 @default.
• W3162958620 hasPrimaryLocation W31629586201 @default.
• W3162958620 hasRelatedWork W2123296398 @default.
• W3162958620 hasRelatedWork W2979979539 @default.
• W3162958620 hasRelatedWork W2985924212 @default.

• next