Dergi makalesi Açık Erişim
Mohammed, MAA;
Kaya, F;
Mohamed, A;
Alarifi,SS;
Abdelrady, A;
Keshavarzi, Ali;
Szabó, NP;
Szűcs, P
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nam##2200000uu#4500</leader> <datafield tag="041" ind1=" " ind2=" "> <subfield code="a">eng</subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.3389/feart.2023.1274142</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">Application of GIS-based machine learning algorithms for prediction of irrigational groundwater quality indices</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">artificial intelligence</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">spatial uncertainty</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">irrigation</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">groundwater quality</subfield> </datafield> <controlfield tag="001">263033</controlfield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">publication</subfield> <subfield code="b">article</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="u">http://www.opendefinition.org/licenses/cc-by-sa</subfield> <subfield code="a">Creative Commons Attribution Share-Alike</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Kaya, F</subfield> <subfield code="0">(orcid)0000-0003-0011-9020</subfield> <subfield code="u">Isparta Uygulamalı Bilimler Üniversitesi</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Mohamed, A</subfield> <subfield code="u">Geology Department, Faculty of Science, Assiut University</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Alarifi,SS</subfield> <subfield code="u">Department of Geology and Geophysics, College of Science, King Saud University</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Abdelrady, A</subfield> <subfield code="u">Faculty of Civil Engineering and Geoscience, Delft University of Technology</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Keshavarzi, Ali</subfield> <subfield code="u">University of Tehran</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Szabó, NP</subfield> <subfield code="u">Faculty of Earth Science and Engineering, University of Miskolc; MTA-ME Geoengineering Research Group, University of Miskolc</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Szűcs, P</subfield> <subfield code="u">Faculty of Earth Science and Engineering, University of Miskolc; MTA-ME Geoengineering Research Group, University of Miskolc</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="o">oai:aperta.ulakbim.gov.tr:263033</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a"><p>Agriculture is considered one of the primary elements for socioeconomic stability in most parts of Sudan. Consequently, the irrigation water should be properly managed to achieve sustainable crop yield and soil fertility. This research aims to predict the irrigation indices of sodium adsorption ratio (SAR), sodium percentage (Na%), permeability index (PI), and potential salinity (PS) using innovative machine learning (ML) techniques, including K-nearest neighbor (KNN), random forest (RF), support vector regression (SVR), and Gaussian process regression (GPR). Thirty-seven groundwater samples are collected and analyzed for twelve physiochemical parameters (TDS, pH, EC, TH, Ca<sup>+2</sup>, Mg<sup>+2</sup>, Na<sup>+</sup>, HCO<sub>3</sub><sup>&minus;</sup>, Cl, SO<sub>4</sub><sup>&minus;2</sup>, and NO<sub>3</sub><sup>&minus;</sup>) to assess the hydrochemical characteristics of groundwater and its suitability for irrigation purposes. The primary investigation indicated that the samples are dominated by Ca-Mg-HCO<sub>3</sub>&nbsp;and Na-HCO<sub>3</sub>&nbsp;water types resulted from groundwater recharge and ion exchange reactions. The observed irrigation indices of SAR, Na%, PI, and PS showed average values of 7, 42.5%, 64.7%, and 0.5, respectively. The ML modeling is based on the ion&rsquo;s concentration as input and the observed values of the indices as output. The data is divided into two sets for training (70%) and validation (30%), and the models are validated using a 10-fold cross-validation technique. The models are tested with three statistical criteria, including mean square error (MSE), root means square error (RMSE), and correlation coefficient (<em>R</em><sup>2</sup>). The SVR algorithm showed the best performance in predicting the irrigation indices, with the lowest RMSE value of 1.45 for SAR. The RMSE values for the other indices, Na%, PI, and PS, were 6.70, 7.10, and 0.55, respectively. The models were applied to digital predictive data in the Nile River area of Khartoum state, and the uncertainty of the maps was estimated by running the models 10 times iteratively. The standard deviation maps were generated to assess the model&rsquo;s sensitivity to the data, and the uncertainty of the model can be used to identify areas where a denser sampling is needed to improve the accuracy of the irrigation indices estimates.</p></subfield> </datafield> <datafield tag="909" ind1="C" ind2="4"> <subfield code="v">11</subfield> <subfield code="p">Frontiers in Earth Science</subfield> </datafield> <controlfield tag="005">20230909105350.0</controlfield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="u">https://aperta.ulakbim.gov.trrecord/263033/files/feart-11-1274142.pdf</subfield> <subfield code="s">5669445</subfield> <subfield code="z">md5:ccfffe08324c9240f23acb35a0994da8</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="a">cc-by</subfield> <subfield code="2">opendefinition.org</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="a">Mohammed, MAA</subfield> <subfield code="u">Faculty of Earth Science and Engineering, University of Miskolc;College of Petroleum Geology and Minerals, University of Bahri</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2023-09-07</subfield> </datafield> </record>
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