FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2765658520> ?p ?o ?g. }

• W2765658520 endingPage "221" @default.
• W2765658520 startingPage "211" @default.
• W2765658520 abstract "Wind drift and evaporation losses (WDEL) play a significant role in the development of water conservation strategies in sprinkler irrigation. In this study, artificial neural network (ANN) and multiple linear regression (MLR) models were developed by taking data collected from published studies on predicted WDEL for several design, operational, and meteorological conditions of variables in sprinkler irrigation. Five combinations of input variables, including riser height, operating pressure, main nozzle diameter, auxiliary nozzle diameter (da), water discharge by main nozzle, water discharge by auxiliary nozzles, wind speed (WS), air temperature, and relative humidity were used to create prediction models for WDEL. The ANN and MLR models were trained and tested on 70% and 30% of the data points, respectively. The accuracy of the models was assessed by the coefficients of correlation (r), overall indices of model performance (OI), root mean square errors (RMSE), and mean absolute errors (MAE). Statistical results showed that the ANN and MLR models with all input variables had the best predicting capabilities. When comparing the results of different ANN and MLR models, it was seen that the ANN models had more success in predicting WDEL. The ANN models gave higher r (0.843–0.956) and OI (0.794–0.909) values, and lower RMSE (2.662%–4.886%) and MAE (2.197%–3.729%) values compared to the MLR models in the training stage. The MLR models’ r values ranged from 0.794 to 0.864, OI values ranged from 0.747 to 0.816, RMSE values ranged from 4.562% to 5.514%, and MAE values ranged from 3.513% to 4.414%. Furthermore, a contribution analysis found that the design parameter da and the climatic parameter WS were considered to obtain the most robust estimation model. It can be stated that the ANN model is a more suitable tool than the MLR model for the prediction of WDEL from sprinkler-irrigation." @default.
• W2765658520 created "2017-11-10" @default.
• W2765658520 creator A5000357038 @default.
• W2765658520 creator A5034772572 @default.
• W2765658520 creator A5058011635 @default.
• W2765658520 creator A5068256181 @default.
• W2765658520 date "2018-01-01" @default.
• W2765658520 modified "2023-10-01" @default.
• W2765658520 title "Prediction of wind drift and evaporation losses from sprinkler irrigation using neural network and multiple regression techniques" @default.
• W2765658520 cites W1812453514 @default.
• W2765658520 cites W1815340196 @default.
• W2765658520 cites W1834484028 @default.
• W2765658520 cites W1968407357 @default.
• W2765658520 cites W1969942076 @default.
• W2765658520 cites W1974313232 @default.
• W2765658520 cites W1977464589 @default.
• W2765658520 cites W1978017902 @default.
• W2765658520 cites W1991142958 @default.
• W2765658520 cites W1994804769 @default.
• W2765658520 cites W1998442441 @default.
• W2765658520 cites W2004280321 @default.
• W2765658520 cites W2011238510 @default.
• W2765658520 cites W2018790612 @default.
• W2765658520 cites W2025272164 @default.
• W2765658520 cites W2028070629 @default.
• W2765658520 cites W2029362666 @default.
• W2765658520 cites W2031292142 @default.
• W2765658520 cites W2037460094 @default.
• W2765658520 cites W2039240409 @default.
• W2765658520 cites W2040613666 @default.
• W2765658520 cites W2045882423 @default.
• W2765658520 cites W2049629133 @default.
• W2765658520 cites W2058763583 @default.
• W2765658520 cites W2060298585 @default.
• W2765658520 cites W2063851248 @default.
• W2765658520 cites W2065398165 @default.
• W2765658520 cites W2065514264 @default.
• W2765658520 cites W2067371767 @default.
• W2765658520 cites W2073901765 @default.
• W2765658520 cites W2074257964 @default.
• W2765658520 cites W2074770406 @default.
• W2765658520 cites W2080061349 @default.
• W2765658520 cites W2084696946 @default.
• W2765658520 cites W2086061909 @default.
• W2765658520 cites W2086368278 @default.
• W2765658520 cites W2088835583 @default.
• W2765658520 cites W2089668000 @default.
• W2765658520 cites W2089686343 @default.
• W2765658520 cites W2093571049 @default.
• W2765658520 cites W2095625135 @default.
• W2765658520 cites W2097049797 @default.
• W2765658520 cites W2120111827 @default.
• W2765658520 cites W2138798945 @default.
• W2765658520 cites W2140140868 @default.
• W2765658520 cites W2143286038 @default.
• W2765658520 cites W2154169239 @default.
• W2765658520 cites W2154612521 @default.
• W2765658520 cites W2170262218 @default.
• W2765658520 doi "https://doi.org/10.1016/j.agwat.2017.10.005" @default.
• W2765658520 hasPublicationYear "2018" @default.
• W2765658520 type Work @default.
• W2765658520 sameAs 2765658520 @default.
• W2765658520 citedByCount "28" @default.
• W2765658520 countsByYear W27656585202018 @default.
• W2765658520 countsByYear W27656585202019 @default.
• W2765658520 countsByYear W27656585202020 @default.
• W2765658520 countsByYear W27656585202021 @default.
• W2765658520 countsByYear W27656585202022 @default.
• W2765658520 countsByYear W27656585202023 @default.
• W2765658520 crossrefType "journal-article" @default.
• W2765658520 hasAuthorship W2765658520A5000357038 @default.
• W2765658520 hasAuthorship W2765658520A5034772572 @default.
• W2765658520 hasAuthorship W2765658520A5058011635 @default.
• W2765658520 hasAuthorship W2765658520A5068256181 @default.
• W2765658520 hasConcept C105795698 @default.
• W2765658520 hasConcept C119857082 @default.
• W2765658520 hasConcept C127413603 @default.
• W2765658520 hasConcept C128990827 @default.
• W2765658520 hasConcept C139945424 @default.
• W2765658520 hasConcept C152877465 @default.
• W2765658520 hasConcept C153294291 @default.
• W2765658520 hasConcept C158960510 @default.
• W2765658520 hasConcept C159390177 @default.
• W2765658520 hasConcept C159750122 @default.
• W2765658520 hasConcept C161067210 @default.
• W2765658520 hasConcept C205649164 @default.
• W2765658520 hasConcept C23430798 @default.
• W2765658520 hasConcept C2777589951 @default.
• W2765658520 hasConcept C33923547 @default.
• W2765658520 hasConcept C39432304 @default.
• W2765658520 hasConcept C41008148 @default.
• W2765658520 hasConcept C48921125 @default.
• W2765658520 hasConcept C50644808 @default.
• W2765658520 hasConcept C56200935 @default.
• W2765658520 hasConcept C61441594 @default.
• W2765658520 hasConcept C78519656 @default.
• W2765658520 hasConceptScore W2765658520C105795698 @default.
• W2765658520 hasConceptScore W2765658520C119857082 @default.

• next