FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3047980212> ?p ?o ?g. }

• W3047980212 endingPage "145576" @default.
• W3047980212 startingPage "145564" @default.
• W3047980212 abstract "Risk and susceptibility mapping of groundwater salinity (GWS) are challenging tasks for groundwater quality monitoring and management. Advancement of accurate prediction systems is essential for the identification of vulnerable areas in order to raise awareness about the potential salinity susceptibility and protect the groundwater and top-soil in due time. In this study, three machine learning models of Stochastic Gradient Boosting (StoGB), Rotation Forest (RotFor), and Bayesian Generalized Linear Model (Bayesglm) are developed for building prediction models and their performance evaluated in the delineation of salinity susceptibility maps. Both natural and human effective factors (16 features) were used as predictors for groundwater salinity modeling and were randomly divided into the training (80%) and testing (20%) datasets. The models were evaluated using testing datasets after calibration using the selected features by recursive feature elimination (RFE) method. The RFE indicated that modeling with 8 features had better performance among 1 to 16 features (Accuracy = 0.87). Results of the groundwater salinity prediction highlighted that StoGB had a good performance, whereas the RotFor and Bayesglm had an excellent performance based on the Kappa values (>0.85). Although spatial prediction of the models was different, all of the models indicated that central parts of the region have a very high susceptibility which matches with agricultural areas, lithology map, the locations with low depth to groundwater, low slope, and elevation. Additionally, areas near to the Maharlu lake and locations with a high decline in groundwater are also located in the very high susceptibility zone, which can confirm the effects of saltwater intrusion. The susceptibility maps produced in this study are of utmost importance for water security and sustainable agriculture." @default.
• W3047980212 created "2020-08-13" @default.
• W3047980212 creator A5048117331 @default.
• W3047980212 creator A5054910952 @default.
• W3047980212 creator A5072746309 @default.
• W3047980212 creator A5073111319 @default.
• W3047980212 creator A5083772809 @default.
• W3047980212 date "2020-01-01" @default.
• W3047980212 modified "2023-10-10" @default.
• W3047980212 title "Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models" @default.
• W3047980212 cites W1502810280 @default.
• W3047980212 cites W1678356000 @default.
• W3047980212 cites W1831050183 @default.
• W3047980212 cites W1878290015 @default.
• W3047980212 cites W1969889466 @default.
• W3047980212 cites W1984638002 @default.
• W3047980212 cites W1994395248 @default.
• W3047980212 cites W1997784112 @default.
• W3047980212 cites W2003685245 @default.
• W3047980212 cites W2007788579 @default.
• W3047980212 cites W2010944706 @default.
• W3047980212 cites W2011554323 @default.
• W3047980212 cites W2022944615 @default.
• W3047980212 cites W2023886705 @default.
• W3047980212 cites W2029549934 @default.
• W3047980212 cites W2035997732 @default.
• W3047980212 cites W2042610585 @default.
• W3047980212 cites W2043011466 @default.
• W3047980212 cites W2047358599 @default.
• W3047980212 cites W2050078710 @default.
• W3047980212 cites W2058173666 @default.
• W3047980212 cites W2059235357 @default.
• W3047980212 cites W2061821938 @default.
• W3047980212 cites W2065644533 @default.
• W3047980212 cites W2066464073 @default.
• W3047980212 cites W2077633200 @default.
• W3047980212 cites W2080720831 @default.
• W3047980212 cites W2092194387 @default.
• W3047980212 cites W2092411843 @default.
• W3047980212 cites W2095706676 @default.
• W3047980212 cites W2119285819 @default.
• W3047980212 cites W2128405541 @default.
• W3047980212 cites W2128728535 @default.
• W3047980212 cites W2136364160 @default.
• W3047980212 cites W2143426320 @default.
• W3047980212 cites W2147556469 @default.
• W3047980212 cites W2148754773 @default.
• W3047980212 cites W2150757437 @default.
• W3047980212 cites W2159301353 @default.
• W3047980212 cites W2161071907 @default.
• W3047980212 cites W2166926097 @default.
• W3047980212 cites W2171483638 @default.
• W3047980212 cites W2241867587 @default.
• W3047980212 cites W2264859459 @default.
• W3047980212 cites W2299952532 @default.
• W3047980212 cites W2314918740 @default.
• W3047980212 cites W2338526851 @default.
• W3047980212 cites W2342819486 @default.
• W3047980212 cites W2399797705 @default.
• W3047980212 cites W2405453291 @default.
• W3047980212 cites W2469986904 @default.
• W3047980212 cites W2513329965 @default.
• W3047980212 cites W2529828984 @default.
• W3047980212 cites W2571395702 @default.
• W3047980212 cites W2571699214 @default.
• W3047980212 cites W2581205918 @default.
• W3047980212 cites W2616613682 @default.
• W3047980212 cites W2616906159 @default.
• W3047980212 cites W2625912355 @default.
• W3047980212 cites W2626818264 @default.
• W3047980212 cites W2741602140 @default.
• W3047980212 cites W2755517150 @default.
• W3047980212 cites W2769933167 @default.
• W3047980212 cites W2772148654 @default.
• W3047980212 cites W2788465381 @default.
• W3047980212 cites W2789490350 @default.
• W3047980212 cites W2791185591 @default.
• W3047980212 cites W2792717144 @default.
• W3047980212 cites W2794365240 @default.
• W3047980212 cites W2794402240 @default.
• W3047980212 cites W2795401061 @default.
• W3047980212 cites W2800522465 @default.
• W3047980212 cites W2809058567 @default.
• W3047980212 cites W2837124910 @default.
• W3047980212 cites W2883190306 @default.
• W3047980212 cites W2884652781 @default.
• W3047980212 cites W2891991017 @default.
• W3047980212 cites W2893657137 @default.
• W3047980212 cites W2896060398 @default.
• W3047980212 cites W2899085952 @default.
• W3047980212 cites W2901312966 @default.
• W3047980212 cites W2903266193 @default.
• W3047980212 cites W2907274780 @default.
• W3047980212 cites W2908431192 @default.
• W3047980212 cites W2909663871 @default.
• W3047980212 cites W2910252586 @default.
• W3047980212 cites W2912912403 @default.
• W3047980212 cites W2915487290 @default.

• next