{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T06:37:20Z","timestamp":1763534240933},"reference-count":61,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T00:00:00Z","timestamp":1658707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,7,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>This paper intends to introduce a novel groundwater prediction model by inducing the novel hydro indices that are not yet popular in earlier techniques. As per the proposed work, statistical features like mean, median, skewness and kurtosis are estimated. Moreover, the vegetation index includes simple ratio, normalized difference vegetation index, Kauth\u2013Thomas Tasseled cap transformation and infrared index transformation. Furthermore, a novel hydro index is formulated by combining the statistical model function with the vegetation index. Subsequently, the detection process is carried out by ensemble technique, which includes the classifiers like random forest (RF), neural network (NN), support vector machine (SVM) and deep belief network (DBN). The final predicted result is attained from DBN. The performance of the adopted model is computed to the existing models with respect to certain measures. At learning rate 50, the maximum accuracy of the proposed model is 45.65, 34.78, 58.70, 72.83, 18.48 and 23.91% better than the existing models like SVM, RF, convolutional neural network, K-nearest neighbors, NN and artificial neural network, respectively.<\/jats:p>","DOI":"10.1093\/comjnl\/bxac101","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T12:01:22Z","timestamp":1658750482000},"page":"1784-1801","source":"Crossref","is-referenced-by-count":4,"title":["A Novel Representation and Prediction Initiative for Underground Water by Using Deep Learning Technique of Remote Sensing Images"],"prefix":"10.1093","volume":"66","author":[{"given":"Veluguri","family":"Sureshkumar","sequence":"first","affiliation":[{"name":"Research Scholar, Electronics and Communication Engineering, Annamalai University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajasomashekar","family":"Somarajadikshitar","sequence":"additional","affiliation":[{"name":"Assistant Professor, Electrical and Electronics Engineering, Government College of Engineering , Thanjavur"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B Sarala","family":"Beeram","sequence":"additional","affiliation":[{"name":"Professor, Electronics and Communication Engineering, Maturi Venkata Subba Rao Engineering College\/Osmania University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,7,25]]},"reference":[{"key":"2023071709113747600_ref1","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1007\/s00521-010-0360-1","article-title":"Hybrid neural modeling for groundwater level prediction","volume":"19","author":"Dash","year":"2010","journal-title":"Neural Comput. Appl."},{"key":"2023071709113747600_ref2","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1007\/s11269-006-9066-7","article-title":"Regional groundwater prediction model using automatic parameter calibration SCE method for a coastal plain of Seto Inland Sea","volume":"21","author":"He","year":"2007","journal-title":"Water Resour. Manag."},{"key":"2023071709113747600_ref3","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/S0269-7491(01)00111-7","article-title":"Predicting groundwater nitrate concentrations in a region of mixed agricultural land use: a comparison of three approaches","volume":"115","author":"McLay","year":"2001","journal-title":"Environ. Pollut."},{"key":"2023071709113747600_ref4","first-page":"1","article-title":"The impact of COVID-19 (SARS-CoV-2) in tourism industry: evidence of Kosovo during Q1, Q2 and Q3 period of 2020","volume":"24","author":"Kuqi","year":"2021","journal-title":"J Sustainable Finance Investment."},{"key":"2023071709113747600_ref5","article-title":"Early public outlook on the coronavirus disease (COVID-19)","author":"Bojja","year":"2020","journal-title":"A Social Media Study"},{"key":"2023071709113747600_ref6","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.neucom.2014.05.026","article-title":"An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam","volume":"145","author":"Suryanarayana","year":"2014","journal-title":"India Neurocomput."},{"key":"2023071709113747600_ref7","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1515\/jwld-2017-0012","article-title":"Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction","volume":"32","author":"Basant","year":"2017","journal-title":"J. Water Land Dev."},{"key":"2023071709113747600_ref8","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s10346-005-0049-1","article-title":"Prediction of ground displacements and velocities from groundwater level changes at the Vallcebre landslide (Eastern Pyrenees, Spain)","volume":"2","author":"Jordi","year":"2005","journal-title":"Landslides."},{"key":"2023071709113747600_ref9","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1190\/1.1440751","article-title":"Geophysical investigations of groundwater resources in the Kalahari Basin","volume":"42","author":"Worthington Paul","year":"1997","journal-title":"Geophysics"},{"key":"2023071709113747600_ref10","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/S0899-5362(98)00053-0","article-title":"Determination of potential groundwater sites using geological and geophysical techniques in the Cross River State, southeastern Nigeria","volume":"27","author":"Okereke","year":"1998","journal-title":"J. Afr. Earth Sci."},{"key":"2023071709113747600_ref11","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1007\/s10040-005-0008-x","article-title":"Optimization of the DRASTIC method for groundwater vulnerability assessment via the use of simple statistical methods and GIS","volume":"14","author":"Panagopoulos","year":"2006","journal-title":"Hydrogeol. J."},{"key":"2023071709113747600_ref12","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/s10230-017-0458-0","article-title":"Assessment of water sources and mixing of groundwater in a coastal mine: the Sanshandao Gold Mine, China","volume":"37","author":"Hongyu","year":"2018","journal-title":"Mine Water Environ."},{"key":"2023071709113747600_ref13","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1007\/s100400050061","article-title":"Hydrogeological characterisation and water-supply potential of basement aquifers in tropical Africa","volume":"3","author":"John","year":"1995","journal-title":"Hydrogeol. J."},{"key":"2023071709113747600_ref14","doi-asserted-by":"crossref","DOI":"10.1029\/2007GL029804","article-title":"Improving land-surface model hydrology: is an explicit aquifer model better than a deeper soil profile?","volume":"34","author":"Gulden","year":"2007","journal-title":"Geophys. Res. Lett."},{"key":"2023071709113747600_ref15","first-page":"81","article-title":"Kriging of groundwater levels-a case study","author":"Vijay","year":"2001"},{"key":"2023071709113747600_ref16","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s12040-011-0075-4","article-title":"Application and evaluation of universal kriging for optimal contouring of groundwater levels","volume":"120","author":"Kambhammettu","year":"2011","journal-title":"J. Earth Syst. Sci."},{"key":"2023071709113747600_ref17","first-page":"1404","article-title":"Improving kriging of groundwater level data using nonlinear normalizing transformations\u2014a field application","volume":"57","author":"Varouchakis","year":"2012","journal-title":"Hydrogeol. J."},{"key":"2023071709113747600_ref18","doi-asserted-by":"crossref","first-page":"1941","DOI":"10.1007\/s00477-016-1334-3","article-title":"SOM-DRASTIC: using self-organizing map for evaluating groundwater potential to pollution","volume":"31","author":"Farshad","year":"2017","journal-title":"Stochastic Environ. Res. Risk Assess."},{"key":"2023071709113747600_ref19","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1007\/s11269-018-2143-x","article-title":"A two-stage approach integrating SOM-and MOGA-SVM-based algorithms to forecast spatial-temporal groundwater level with meteorological factors","volume":"33","author":"Hsi-Ting","year":"2019","journal-title":"Water Resour. Manage."},{"key":"2023071709113747600_ref20","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.jher.2013.03.005","article-title":"Prediction and simulation of monthly groundwater levels by genetic programming","volume":"7","author":"Fallah-Mehdipour","year":"2013","journal-title":"J. Hydro-Environ. Res."},{"key":"2023071709113747600_ref21","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1016\/j.jhydrol.2016.06.014","article-title":"Quantifying uncertainty in predictions of groundwater levels using formal likelihood methods","volume":"540","author":"Marchant","year":"2016","journal-title":"J. Hydrol."},{"key":"2023071709113747600_ref22","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.jhydrol.2018.02.028","article-title":"Novel patch modelling method for efficient simulation and prediction uncertainty analysis of multi-scale groundwater flow and transport processes","volume":"559","author":"Sreekanth","year":"2018","journal-title":"J. Hydrol."},{"key":"2023071709113747600_ref23","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1016\/j.jhydrol.2018.02.006","article-title":"Prediction uncertainty and data worth assessment for groundwater transport times in an agricultural catchment","volume":"561","author":"Zell","year":"2018","journal-title":"J. Hydrol."},{"key":"2023071709113747600_ref24","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1016\/j.jhydrol.2015.09.038","article-title":"Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model","volume":"529","author":"Juan","year":"2015","journal-title":"J. Hydrol."},{"key":"2023071709113747600_ref25","first-page":"12","article-title":"Enhanced crow search optimization algorithm and hybrid NN-CNN classifiers for classification of land cover images","volume":"2","author":"Gangappa","year":"2019","journal-title":"Multimedia Res."},{"key":"2023071709113747600_ref26","first-page":"21","article-title":"Vertical handover using mutated-salp swarm optimization algorithm based on radial basis function neural network in heterogeneous networks","volume":"3","author":"Gupta","year":"2020","journal-title":"J. Networking Commun. Syst."},{"key":"2023071709113747600_ref27","first-page":"11","article-title":"Enhancement in biodiesel blend with the aid of neural network and SAPSO","volume":"1","author":"Vinolin","year":"2018","journal-title":"J. Comput. Mech. Power Syst. Control."},{"key":"2023071709113747600_ref28","doi-asserted-by":"crossref","first-page":"39","DOI":"10.46253\/jcmps.v2i3.a5","article-title":"Enhanced deep convolutional neural network for fault signal recognition in the power distribution system","volume":"2","author":"Srinivasa Rao","year":"2019","journal-title":"J. Comput. Mech. Power Syst. Control"},{"key":"2023071709113747600_ref29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.46253\/jcmps.v3i1.a1","article-title":"Deep neural network and social ski-driver optimization algorithm for power system restoration with VSC - HVDC technology","volume":"3","author":"Shaik","year":"2020","journal-title":"J. Comput. Mech. Power Syst. Control."},{"key":"2023071709113747600_ref30","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1093\/gji\/ggu196","article-title":"Groundwater level changes induced by the 2011 Tohoku earthquake in China mainland","volume":"199","author":"Yan","year":"2014","journal-title":"Geophys. J. Int."},{"key":"2023071709113747600_ref31","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.geog.2016.07.005","article-title":"Groundwater level prediction of landslide based on classification and regression tree","volume":"7","author":"Zhao","year":"2016","journal-title":"Geodesy Geodyn."},{"key":"2023071709113747600_ref32","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1016\/j.jhydrol.2018.01.045","article-title":"Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX)","volume":"567","author":"Wunsch","year":"2018","journal-title":"J. Hydrol."},{"key":"2023071709113747600_ref33","first-page":"33","article-title":"Hybrid optimization based DBN for face recognition using low-resolution images","volume":"1","author":"Thomas","year":"2018","journal-title":"Multimedia Res."},{"key":"2023071709113747600_ref34","first-page":"40","article-title":"Convolutional neural network for water quality prediction in WSN","volume":"2","author":"Chandanapalli","year":"2019","journal-title":"J. Networking Commun. Syst."},{"key":"2023071709113747600_ref35","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/LGRS.2013.2246538","article-title":"Object detection in high-resolution remote sensing images using rotation invariant parts based model","volume":"11","author":"Zhang","year":"2014","journal-title":"IEEE Geosci. Remote Sens."},{"key":"2023071709113747600_ref36","first-page":"1656","article-title":"Remote sensing image fusion with deep convolutional neural network","volume":"11","author":"Shao","year":"2018","journal-title":"IEEE J. Sel. Top. Appl."},{"key":"2023071709113747600_ref37","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.autcon.2018.03.002","article-title":"An anomalous event detection and tracking method for a tunnel look-ahead ground prediction system","volume":"91","author":"Wei","year":"2018","journal-title":"Automat. Constr."},{"key":"2023071709113747600_ref38","first-page":"57","article-title":"Groundwater level prediction using M5 model trees","volume":"96","author":"Nalarajan","year":"2015","journal-title":"J. Inst. Eng(India): Ser. A."},{"key":"2023071709113747600_ref39","doi-asserted-by":"crossref","first-page":"5433","DOI":"10.1007\/s11269-014-0810-0","article-title":"Prediction the groundwater level of Bastam Plain (Iran) by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)","volume":"28","author":"Emamgholizadeh","year":"2014","journal-title":"Water Resour. Manag."},{"key":"2023071709113747600_ref40","doi-asserted-by":"crossref","DOI":"10.1007\/s12665-016-6059-6","article-title":"Dynamic panel-data-based groundwater level prediction and decomposition in an arid hardrock\u2013alluvium aquifer","volume":"75","author":"Izady","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"2023071709113747600_ref41","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s12594-015-0197-4","article-title":"Forecasting groundwater level by artificial neural networks as an alternative approach to groundwater modeling","volume":"85","author":"Chitsazan","year":"2015","journal-title":"J. Geol. Soc. India."},{"key":"2023071709113747600_ref42","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1109\/LGRS.2017.2787338","article-title":"Hyperspectral image classification via fusing correlation coefficient and joint sparse representation","volume":"15","author":"Tu","year":"2018","journal-title":"IEEE Geosci. Remote Sens."},{"key":"2023071709113747600_ref43","doi-asserted-by":"crossref","first-page":"10996","DOI":"10.1109\/ACCESS.2018.2810113","article-title":"Incremental graph embedding based on spatial-spectral neighbors for hyperspectral image classification","volume":"6","author":"Li","year":"2018","journal-title":"IEEE Access."},{"key":"2023071709113747600_ref44","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1109\/LGRS.2018.2800080","article-title":"Hyperspectral image classification with discriminative kernel collaborative representation and tikhonov regularization","volume":"15","author":"Ma","year":"2018","journal-title":"IEEE Geosci. Remote Sens."},{"key":"2023071709113747600_ref45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13201-019-1094-6","article-title":"Prediction of groundwater level in basement complex terrain using artificial neural network: a case of Ijebu-Jesa, southwestern Nigeria","volume":"10","author":"Adiat","year":"2020","journal-title":"Appl. Water Sci."},{"key":"2023071709113747600_ref46","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/j.applthermaleng.2015.10.104","article-title":"Research on a feature selection method based on median impact value for modeling in thermal power plants","volume":"94","author":"Minfang","year":"2016","journal-title":"Appl. Therm. Eng."},{"key":"2023071709113747600_ref47","doi-asserted-by":"crossref","first-page":"478","DOI":"10.3390\/e22040478","article-title":"Mechanical fault diagnosis of a high voltage circuit breaker based on high-efficiency time-domain feature extraction with entropy features","volume":"22","author":"Jiajin","year":"2020","journal-title":"Entropy."},{"key":"2023071709113747600_ref48","article-title":"Artificial bee colony-based general adversarial network for liver cancer detection using CT images","volume":"3","author":"Khan","year":"2020","journal-title":"Multimedia Research"},{"key":"2023071709113747600_ref49","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.compag.2018.04.028","article-title":"Predicting the normalized difference vegetation index (NDVI) by training a crop growth model with historical data","volume":"161","author":"Andr\u00e9s","year":"2019","journal-title":"Comput. Electron Agr."},{"key":"2023071709113747600_ref50","volume-title":"The Tasseled Cap\u2014A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen by Landsat LARS: Proceedings of the Symposium on Machine Processing of Remotely Sensed Data","author":"Kauth","year":"1976"},{"key":"2023071709113747600_ref51","first-page":"17","article-title":"Significant remote sensing vegetation indices: a review of developments and applications","volume":"1353691","author":"Xue","year":"2017","journal-title":"Hindawi, J. Sensors."},{"key":"2023071709113747600_ref52","first-page":"115","volume-title":"2020 International Conference on Computational Intelligence (ICCI)","author":"Hamza","year":"2020"},{"key":"2023071709113747600_ref53","first-page":"14","article-title":"Classification and prediction of student academic performance in King Khalid University-a machine learning approach","volume":"12","author":"Prasanalakshmi","year":"2019","journal-title":"Indian J Sci. Technol."},{"key":"2023071709113747600_ref54","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1007\/s12530-018-9232-1","article-title":"An efficient classification approach in imbalanced datasets for intrinsic plagiarism detection","volume":"11","author":"Andrianna","year":"2020","journal-title":"Evolv. Syst."},{"key":"2023071709113747600_ref55","first-page":"207","volume-title":"Machine Learning Models and Algorithms for Big Data Classification","author":"Shan","year":"2016"},{"key":"2023071709113747600_ref56","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1198\/106186006X94072","article-title":"Unsupervised learning with random forest predictors","volume":"15","author":"Tao","year":"2006","journal-title":"J. Comput. Graph. Stat."},{"key":"2023071709113747600_ref57","volume-title":"Neural Network Learning: Theoretical Foundations. 9","author":"Martin","year":"1999"},{"key":"2023071709113747600_ref58","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.sigpro.2015.09.027","article-title":"DeepChart: combining deep convolutional networks and deep belief networks in chart classification","volume":"124","author":"Tang","year":"2016","journal-title":"Signal Process."},{"key":"2023071709113747600_ref59"},{"key":"2023071709113747600_ref60","first-page":"1","volume-title":"2017 International Conference on Engineering and Technology (ICET)","author":"Saad","year":"2017"},{"key":"2023071709113747600_ref61","first-page":"48","article-title":"A study of K-nearest neighbour as an imputation method","volume":"87","author":"Gustavo","year":"2002","journal-title":"His"}],"container-title":["The Computer Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/66\/7\/1784\/50876238\/bxac101.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/66\/7\/1784\/50876238\/bxac101.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T09:18:31Z","timestamp":1689585511000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/comjnl\/article\/66\/7\/1784\/6649482"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,25]]},"references-count":61,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,7,25]]},"published-print":{"date-parts":[[2023,7,13]]}},"URL":"https:\/\/doi.org\/10.1093\/comjnl\/bxac101","relation":{},"ISSN":["0010-4620","1460-2067"],"issn-type":[{"value":"0010-4620","type":"print"},{"value":"1460-2067","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,7]]},"published":{"date-parts":[[2022,7,25]]}}}