FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4281719150> ?p ?o ?g. }

• W4281719150 endingPage "135265" @default.
• W4281719150 startingPage "135265" @default.
• W4281719150 abstract "Although groundwater (GW) potential zoning can be beneficial for water management, it is currently lacking in several places around the world, including Pakistan's Quetta Valley. Due to ever increasing population growth and industrial development, GW is being used indiscriminately all over the world. Recognizing the importance of GW potential for sustainable growth, this study used to 16 GW drive factors to evaluate their effectiveness by using six machine learning algorithms (MLA's) that include artificial neural networks (ANN), random forest (RF), support vector machine (SVM), K- Nearest Neighbor (KNN), Naïve Bayes (NB) and Extreme Gradient Boosting (XGBoost). The GW yield data were collected and divided into 70% for training and 30% for validation. The training data of GW yields were integrated into the MLA's along with the GW driver variables and the projected results were checked using the Receiver Operating Characteristic (ROC) curve and the validation data. Out of six ML algorithms, ROC curve showed that the XGBoost, RF and ANN models performed well with 98.3%, 96.8% and 93.5% accuracy respectively. In addition, the accuracy of the models was evaluated using the mean absolute error (MAE), root mean square error (RMSE), F-score and correlation-coefficient. Hydro-chemical data were evaluated, and the water quality index (WQI) was also calculated. The final GW productivity potential (GWPP) maps were created using the MLA's output and WQI as they identify the different classification zones that can be used by the government and other agenciesto locate new GW wells and provide a basis for water management in rocky terrain." @default.
• W4281719150 created "2022-06-13" @default.
• W4281719150 creator A5008472067 @default.
• W4281719150 creator A5024545581 @default.
• W4281719150 creator A5026508642 @default.
• W4281719150 creator A5036814212 @default.
• W4281719150 creator A5042188610 @default.
• W4281719150 creator A5078637996 @default.
• W4281719150 creator A5080427402 @default.
• W4281719150 creator A5085990695 @default.
• W4281719150 date "2022-09-01" @default.
• W4281719150 modified "2023-09-29" @default.
• W4281719150 title "Mapping of groundwater productivity potential with machine learning algorithms: A case study in the provincial capital of Baluchistan, Pakistan" @default.
• W4281719150 cites W1192997862 @default.
• W4281719150 cites W1972068079 @default.
• W4281719150 cites W1974230032 @default.
• W4281719150 cites W1982623886 @default.
• W4281719150 cites W1996031526 @default.
• W4281719150 cites W2013341039 @default.
• W4281719150 cites W2013558387 @default.
• W4281719150 cites W2013640190 @default.
• W4281719150 cites W2018093805 @default.
• W4281719150 cites W2034621771 @default.
• W4281719150 cites W2035549409 @default.
• W4281719150 cites W2038921269 @default.
• W4281719150 cites W2050807833 @default.
• W4281719150 cites W2058731966 @default.
• W4281719150 cites W2066203025 @default.
• W4281719150 cites W2103082150 @default.
• W4281719150 cites W2108946099 @default.
• W4281719150 cites W2113809701 @default.
• W4281719150 cites W2118037698 @default.
• W4281719150 cites W2119787382 @default.
• W4281719150 cites W2125223451 @default.
• W4281719150 cites W2135274825 @default.
• W4281719150 cites W2139833307 @default.
• W4281719150 cites W2201333553 @default.
• W4281719150 cites W2521201985 @default.
• W4281719150 cites W2521816397 @default.
• W4281719150 cites W2557977272 @default.
• W4281719150 cites W2579894614 @default.
• W4281719150 cites W2593192809 @default.
• W4281719150 cites W2609194414 @default.
• W4281719150 cites W2794405349 @default.
• W4281719150 cites W2797310831 @default.
• W4281719150 cites W2895335656 @default.
• W4281719150 cites W2896033842 @default.
• W4281719150 cites W2896412441 @default.
• W4281719150 cites W2905215511 @default.
• W4281719150 cites W2911688499 @default.
• W4281719150 cites W2911964244 @default.
• W4281719150 cites W2913138094 @default.
• W4281719150 cites W2920548804 @default.
• W4281719150 cites W2924738385 @default.
• W4281719150 cites W2943155923 @default.
• W4281719150 cites W2946648277 @default.
• W4281719150 cites W2965030991 @default.
• W4281719150 cites W2992350695 @default.
• W4281719150 cites W2998917588 @default.
• W4281719150 cites W3015368251 @default.
• W4281719150 cites W3023830374 @default.
• W4281719150 cites W3036027615 @default.
• W4281719150 cites W3042612344 @default.
• W4281719150 cites W3045040272 @default.
• W4281719150 cites W3092453021 @default.
• W4281719150 cites W3093801116 @default.
• W4281719150 cites W3106300197 @default.
• W4281719150 cites W3124590480 @default.
• W4281719150 cites W3172906286 @default.
• W4281719150 cites W3182706339 @default.
• W4281719150 cites W3188744712 @default.
• W4281719150 cites W4210949798 @default.
• W4281719150 cites W4239510810 @default.
• W4281719150 doi "https://doi.org/10.1016/j.chemosphere.2022.135265" @default.
• W4281719150 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35691394" @default.
• W4281719150 hasPublicationYear "2022" @default.
• W4281719150 type Work @default.
• W4281719150 citedByCount "8" @default.
• W4281719150 countsByYear W42817191502022 @default.
• W4281719150 countsByYear W42817191502023 @default.
• W4281719150 crossrefType "journal-article" @default.
• W4281719150 hasAuthorship W4281719150A5008472067 @default.
• W4281719150 hasAuthorship W4281719150A5024545581 @default.
• W4281719150 hasAuthorship W4281719150A5026508642 @default.
• W4281719150 hasAuthorship W4281719150A5036814212 @default.
• W4281719150 hasAuthorship W4281719150A5042188610 @default.
• W4281719150 hasAuthorship W4281719150A5078637996 @default.
• W4281719150 hasAuthorship W4281719150A5080427402 @default.
• W4281719150 hasAuthorship W4281719150A5085990695 @default.
• W4281719150 hasConcept C105795698 @default.
• W4281719150 hasConcept C11413529 @default.
• W4281719150 hasConcept C119857082 @default.
• W4281719150 hasConcept C12267149 @default.
• W4281719150 hasConcept C139719470 @default.
• W4281719150 hasConcept C139945424 @default.
• W4281719150 hasConcept C154945302 @default.
• W4281719150 hasConcept C162324750 @default.
• W4281719150 hasConcept C204983608 @default.

• next