Matches in SemOpenAlex for { <https://semopenalex.org/work/W4200619798> ?p ?o ?g. }
- W4200619798 endingPage "107423" @default.
- W4200619798 startingPage "107423" @default.
- W4200619798 abstract "Accurate groundwater quality estimation is of great significance for effective irrigation management in agricultural areas. However, due to lack of data, the parameterization of the physical models is restricted for large areas with diverse underlying surface conditions. To obtain effective and convenient estimations of groundwater quality with the most accessible data is urgently needed. In this study, the validity of the data-based models in irrigation water quality index estimation was investigated by only using physical groundwater parameters as inputs. 15 combination scenarios of the physical parameters including temperature, pH, electrical conductivity, and dissolved oxygen were examined by support vector machine (SVM), random forests (RF), artificial neural networks (ANN) and extreme learning machine (ELM) models for total dissolved solids (TDS), potential salinity (PS), and sodium adsorption ratio (SAR) estimation for the Zhangye Basin, northwest China. Performance of the artificial intelligence (AI) models was evaluated according to the coefficient of correlation (R), root mean squared error (RMSE), mean absolute percentage error (MAPE), and Nash-Sutcliffe efficiency (NS). The Monte Carlo (MC) approach was performed to assess the uncertainty of the physical groundwater parameters and the sensitivity of the AI models. The results revealed an input pattern, which emphasized the important role of EC and the improving function of pH, in revealing the nature of data-based irrigation water quality index estimation. Results of the uncertainty analysis also confirmed the prominent superiority and robustness of the SVM, RF and ELM models in producing excellent estimations with only physical parameters as inputs. The developed AI models were valuable in estimating irrigation water quality indexes, thus could help decision makers manage irrigation strategies. By using physical groundwater parameters as input variables, the AI models showed prospects in convenient and cost-effective irrigation water quality index estimating." @default.
- W4200619798 created "2021-12-31" @default.
- W4200619798 creator A5009433928 @default.
- W4200619798 creator A5015320272 @default.
- W4200619798 creator A5049021556 @default.
- W4200619798 creator A5064953206 @default.
- W4200619798 creator A5066400668 @default.
- W4200619798 creator A5090899965 @default.
- W4200619798 date "2022-03-01" @default.
- W4200619798 modified "2023-10-01" @default.
- W4200619798 title "Data-based groundwater quality estimation and uncertainty analysis for irrigation agriculture" @default.
- W4200619798 cites W1992486482 @default.
- W4200619798 cites W2026131661 @default.
- W4200619798 cites W2028459236 @default.
- W4200619798 cites W2049436562 @default.
- W4200619798 cites W2050700668 @default.
- W4200619798 cites W2055228777 @default.
- W4200619798 cites W2058998445 @default.
- W4200619798 cites W2078326179 @default.
- W4200619798 cites W2083242503 @default.
- W4200619798 cites W2095239580 @default.
- W4200619798 cites W2095613351 @default.
- W4200619798 cites W2121971770 @default.
- W4200619798 cites W2128003492 @default.
- W4200619798 cites W2132417139 @default.
- W4200619798 cites W2141412323 @default.
- W4200619798 cites W2153635508 @default.
- W4200619798 cites W2157276885 @default.
- W4200619798 cites W2264220930 @default.
- W4200619798 cites W2328395745 @default.
- W4200619798 cites W2430287324 @default.
- W4200619798 cites W2483805006 @default.
- W4200619798 cites W2768815805 @default.
- W4200619798 cites W2790367482 @default.
- W4200619798 cites W2791081216 @default.
- W4200619798 cites W2791185591 @default.
- W4200619798 cites W2907891425 @default.
- W4200619798 cites W2908904086 @default.
- W4200619798 cites W2911964244 @default.
- W4200619798 cites W2916458420 @default.
- W4200619798 cites W2970381989 @default.
- W4200619798 cites W2972358831 @default.
- W4200619798 cites W2979569561 @default.
- W4200619798 cites W2990857758 @default.
- W4200619798 cites W2999990820 @default.
- W4200619798 cites W3005798889 @default.
- W4200619798 cites W3010142642 @default.
- W4200619798 cites W3024714115 @default.
- W4200619798 cites W3034089822 @default.
- W4200619798 cites W3046781693 @default.
- W4200619798 cites W3046973866 @default.
- W4200619798 cites W3085538245 @default.
- W4200619798 cites W3087085230 @default.
- W4200619798 cites W3091771789 @default.
- W4200619798 cites W3095322988 @default.
- W4200619798 cites W3105432036 @default.
- W4200619798 cites W3109132290 @default.
- W4200619798 cites W3110544876 @default.
- W4200619798 cites W3119201525 @default.
- W4200619798 cites W3127765525 @default.
- W4200619798 cites W3135112728 @default.
- W4200619798 cites W3136883316 @default.
- W4200619798 cites W3149057642 @default.
- W4200619798 cites W3158543373 @default.
- W4200619798 cites W3180872679 @default.
- W4200619798 cites W3183580960 @default.
- W4200619798 cites W3191148335 @default.
- W4200619798 cites W3191246754 @default.
- W4200619798 cites W3199757170 @default.
- W4200619798 cites W3201097864 @default.
- W4200619798 cites W3202719784 @default.
- W4200619798 cites W4239510810 @default.
- W4200619798 cites W4250054092 @default.
- W4200619798 cites W571971660 @default.
- W4200619798 doi "https://doi.org/10.1016/j.agwat.2021.107423" @default.
- W4200619798 hasPublicationYear "2022" @default.
- W4200619798 type Work @default.
- W4200619798 citedByCount "8" @default.
- W4200619798 countsByYear W42006197982022 @default.
- W4200619798 countsByYear W42006197982023 @default.
- W4200619798 crossrefType "journal-article" @default.
- W4200619798 hasAuthorship W4200619798A5009433928 @default.
- W4200619798 hasAuthorship W4200619798A5015320272 @default.
- W4200619798 hasAuthorship W4200619798A5049021556 @default.
- W4200619798 hasAuthorship W4200619798A5064953206 @default.
- W4200619798 hasAuthorship W4200619798A5066400668 @default.
- W4200619798 hasAuthorship W4200619798A5090899965 @default.
- W4200619798 hasConcept C105795698 @default.
- W4200619798 hasConcept C119857082 @default.
- W4200619798 hasConcept C12267149 @default.
- W4200619798 hasConcept C127413603 @default.
- W4200619798 hasConcept C139945424 @default.
- W4200619798 hasConcept C150217764 @default.
- W4200619798 hasConcept C152453397 @default.
- W4200619798 hasConcept C159390177 @default.
- W4200619798 hasConcept C169258074 @default.
- W4200619798 hasConcept C187320778 @default.
- W4200619798 hasConcept C18903297 @default.