FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4309787652> ?p ?o ?g. }

• W4309787652 endingPage "2040" @default.
• W4309787652 startingPage "2040" @default.
• W4309787652 abstract "Climate change has caused droughts to increase in frequency and severity worldwide, which has attracted scientists to create drought prediction models to mitigate the impacts of droughts. One of the most important challenges in addressing droughts is developing accurate models to predict their discrete characteristics, i.e., occurrence, duration, and severity. The current research examined the performance of several different machine learning models, including Artificial Neural Network (ANN) and M5P Tree in forecasting the most widely used drought measure, the Standardized Precipitation Index (SPI), at both discrete time scales (SPI 3, SPI 6). The drought model was developed utilizing rainfall data from two stations in India (i.e., Angangaon and Dahalewadi) for 2000–2019, wherein the first 14 years are employed for model training, while the remaining six years are employed for model validation. The subset regression analysis was performed on 12 different input combinations to choose the best input combination for SPI 3 and SPI 6. The sensitivity analysis was carried out on the given best input combination to find the most effective parameter for forecasting. The performance of all the developed models for ANN (4, 5), ANN (5, 6), ANN (6, 7), and M5P models was assessed through the different statistical indicators, namely, MAE, RMSE, RAE, RRSE, and r. The results revealed that SPI (t-1) is the most sensitive parameters with highest values of β = 0.916, 1.017, respectively, for SPI-3 and SPI-6 prediction at both stations on the best input combinations i.e., combination 7 (SPI-1/SPI-3/SPI-4/SPI-5/SPI-8/SPI-9/SPI-11) and combination 4 (SPI-1/SPI-2/SPI-6/SPI-7) based on the higher values of R2 and Adjusted R2 while the lowest values of MSE values. It is clear from the performance of models that the M5P model has higher r values and lesser RMSE values as compared to ANN (4, 5), ANN (5, 6), and ANN (6, 7) models. Therefore, the M5P model was superior to other developed models at both stations." @default.
• W4309787652 created "2022-11-29" @default.
• W4309787652 creator A5020825700 @default.
• W4309787652 creator A5021771514 @default.
• W4309787652 creator A5059034951 @default.
• W4309787652 creator A5059823880 @default.
• W4309787652 creator A5062668227 @default.
• W4309787652 creator A5066707001 @default.
• W4309787652 creator A5070829177 @default.
• W4309787652 creator A5084438474 @default.
• W4309787652 date "2022-11-14" @default.
• W4309787652 modified "2023-10-05" @default.
• W4309787652 title "Forecasting of SPI and Meteorological Drought Based on the Artificial Neural Network and M5P Model Tree" @default.
• W4309787652 cites W1502566860 @default.
• W4309787652 cites W1965165947 @default.
• W4309787652 cites W1987223597 @default.
• W4309787652 cites W1989629347 @default.
• W4309787652 cites W2009496794 @default.
• W4309787652 cites W2023191393 @default.
• W4309787652 cites W2027553546 @default.
• W4309787652 cites W2039240409 @default.
• W4309787652 cites W2041490648 @default.
• W4309787652 cites W2051226989 @default.
• W4309787652 cites W2070204060 @default.
• W4309787652 cites W2077766806 @default.
• W4309787652 cites W2083479130 @default.
• W4309787652 cites W2083883946 @default.
• W4309787652 cites W2104157175 @default.
• W4309787652 cites W2165301409 @default.
• W4309787652 cites W2166391252 @default.
• W4309787652 cites W2171932819 @default.
• W4309787652 cites W2177959459 @default.
• W4309787652 cites W2196961118 @default.
• W4309787652 cites W2536008880 @default.
• W4309787652 cites W2598379669 @default.
• W4309787652 cites W2733535563 @default.
• W4309787652 cites W2804999555 @default.
• W4309787652 cites W2910535189 @default.
• W4309787652 cites W2911831784 @default.
• W4309787652 cites W2912772317 @default.
• W4309787652 cites W2913267705 @default.
• W4309787652 cites W2943527853 @default.
• W4309787652 cites W2946559426 @default.
• W4309787652 cites W2971461196 @default.
• W4309787652 cites W2977553585 @default.
• W4309787652 cites W2982270353 @default.
• W4309787652 cites W2998503064 @default.
• W4309787652 cites W3005400762 @default.
• W4309787652 cites W3033341975 @default.
• W4309787652 cites W3084187496 @default.
• W4309787652 cites W3206578428 @default.
• W4309787652 cites W3214254203 @default.
• W4309787652 cites W4200407991 @default.
• W4309787652 cites W4210557061 @default.
• W4309787652 cites W4210676900 @default.
• W4309787652 cites W4212871224 @default.
• W4309787652 cites W4213325942 @default.
• W4309787652 cites W4213424961 @default.
• W4309787652 cites W4224327231 @default.
• W4309787652 cites W4229028179 @default.
• W4309787652 cites W4233186070 @default.
• W4309787652 cites W4250251222 @default.
• W4309787652 cites W4254279270 @default.
• W4309787652 cites W4283166815 @default.
• W4309787652 cites W4289278167 @default.
• W4309787652 doi "https://doi.org/10.3390/land11112040" @default.
• W4309787652 hasPublicationYear "2022" @default.
• W4309787652 type Work @default.
• W4309787652 citedByCount "21" @default.
• W4309787652 countsByYear W43097876522023 @default.
• W4309787652 crossrefType "journal-article" @default.
• W4309787652 hasAuthorship W4309787652A5020825700 @default.
• W4309787652 hasAuthorship W4309787652A5021771514 @default.
• W4309787652 hasAuthorship W4309787652A5059034951 @default.
• W4309787652 hasAuthorship W4309787652A5059823880 @default.
• W4309787652 hasAuthorship W4309787652A5062668227 @default.
• W4309787652 hasAuthorship W4309787652A5066707001 @default.
• W4309787652 hasAuthorship W4309787652A5070829177 @default.
• W4309787652 hasAuthorship W4309787652A5084438474 @default.
• W4309787652 hasBestOaLocation W43097876521 @default.
• W4309787652 hasConcept C105795698 @default.
• W4309787652 hasConcept C119857082 @default.
• W4309787652 hasConcept C124101348 @default.
• W4309787652 hasConcept C139945424 @default.
• W4309787652 hasConcept C154945302 @default.
• W4309787652 hasConcept C33923547 @default.
• W4309787652 hasConcept C41008148 @default.
• W4309787652 hasConcept C50644808 @default.
• W4309787652 hasConceptScore W4309787652C105795698 @default.
• W4309787652 hasConceptScore W4309787652C119857082 @default.
• W4309787652 hasConceptScore W4309787652C124101348 @default.
• W4309787652 hasConceptScore W4309787652C139945424 @default.
• W4309787652 hasConceptScore W4309787652C154945302 @default.
• W4309787652 hasConceptScore W4309787652C33923547 @default.
• W4309787652 hasConceptScore W4309787652C41008148 @default.
• W4309787652 hasConceptScore W4309787652C50644808 @default.
• W4309787652 hasIssue "11" @default.
• W4309787652 hasLocation W43097876521 @default.

• next