{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T17:55:01Z","timestamp":1778003701635,"version":"3.51.4"},"reference-count":93,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,7,3]],"date-time":"2017-07-03T00:00:00Z","timestamp":1499040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The objective of this study was to develop information mining methodology for drought modeling and predictions using historical records of climate, satellite, environmental, and oceanic data. The classification and regression tree (CART) approach was used for extracting drought episodes at different time-lag prediction intervals. Using the CART approach, a number of successful model trees were constructed, which can easily be interpreted and used by decision makers in their drought management decisions. The regression rules produced by CART were found to have correlation coefficients from 0.71\u20130.95 in rules-alone modeling. The accuracies of the models were found to be higher in the instance and rules model (0.77\u20130.96) compared to the rules-alone model. From the experimental analysis, it was concluded that different combinations of the nearest neighbor and committee models significantly increase the performances of CART drought models. For more robust results from the developed methodology, it is recommended that future research focus on selecting relevant attributes for slow-onset drought episode identification and prediction.<\/jats:p>","DOI":"10.3390\/info8030079","type":"journal-article","created":{"date-parts":[[2017,7,3]],"date-time":"2017-07-03T10:27:31Z","timestamp":1499077651000},"page":"79","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Information Mining from Heterogeneous Data Sources: A Case Study on Drought Predictions"],"prefix":"10.3390","volume":"8","author":[{"given":"Getachew","family":"Demisse","sequence":"first","affiliation":[{"name":"National Drought Mitigation Center, School of Natural Resources, University of Nebraska-Lincoln, Hardin Hall, 3310 Holdrege Street, P.O. Box 830988, Lincoln, NE 68583-0988, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4102-1137","authenticated-orcid":false,"given":"Tsegaye","family":"Tadesse","sequence":"additional","affiliation":[{"name":"National Drought Mitigation Center, School of Natural Resources, University of Nebraska-Lincoln, Hardin Hall, 3310 Holdrege Street, P.O. Box 830988, Lincoln, NE 68583-0988, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Solomon","family":"Atnafu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Addis Ababa University, P.O. Box 1176, Addis Ababa, Ethiopia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shawndra","family":"Hill","sequence":"additional","affiliation":[{"name":"The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4767-581X","authenticated-orcid":false,"given":"Brian","family":"Wardlow","sequence":"additional","affiliation":[{"name":"National Drought Mitigation Center, School of Natural Resources, University of Nebraska-Lincoln, Hardin Hall, 3310 Holdrege Street, P.O. Box 830988, Lincoln, NE 68583-0988, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2367-6559","authenticated-orcid":false,"given":"Yared","family":"Bayissa","sequence":"additional","affiliation":[{"name":"National Drought Mitigation Center, School of Natural Resources, University of Nebraska-Lincoln, Hardin Hall, 3310 Holdrege Street, P.O. 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Enterp."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., and Treur, J. (2013, January 17\u201321). Recent Trends in Applied Artificial Intelligence. Proceedings of the 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA\/AIE 2013), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-642-38577-3"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1109\/TPWRD.2004.838515","article-title":"Extracting Knowledge From Substations for Decision Support","volume":"20","author":"Hor","year":"2005","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1287\/orsc.5.1.14","article-title":"A Dynamic Theory of Organizational Knowledge Creation","volume":"5","author":"Nonaka","year":"1994","journal-title":"Organ. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1017\/S0140525X99002186","article-title":"A theory of implicit and explicit knowledge","volume":"22","author":"Dienes","year":"1999","journal-title":"Behav. Brain Sci."},{"key":"ref_7","unstructured":"Han, H., and Kamber, M. (2006). Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers. [2nd ed.]."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1145\/240455.240464","article-title":"The KDD process for extracting useful knowledge from volumes of data","volume":"39","author":"Fayyad","year":"1996","journal-title":"Commun. ACM"},{"key":"ref_9","first-page":"267","article-title":"Data Mining: A Conceptual Overview","volume":"8","author":"Jackson","year":"2002","journal-title":"Commun. Assoc. Inf. Syst."},{"key":"ref_10","unstructured":"Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996, January 2\u20134). Knowledge Discovery and Data Mining: Towards a Unifying Framework. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96 AAAI), Portland, OR, USA."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Miller, H.J., and Han, J. (2001). Geographic Data Mining and Knowledge Discovery, Taylor & Francis.","DOI":"10.1201\/b12382"},{"key":"ref_12","unstructured":"UNCCD (1999). United Nations Convention to Combat Desertification, Article 1, United Nations."},{"key":"ref_13","first-page":"45","article-title":"Drought under global warming: A review","volume":"2","author":"Dai","year":"2011","journal-title":"Adv. Rev. Natl. Center Atmos. Res."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wilhite, D. (2005). Drought and Water Crisis: Science, Technology and Management Issues, Taylor & Francis.","DOI":"10.1201\/9781420028386.pt4"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3635","DOI":"10.5194\/hess-18-3635-2014","article-title":"A review of droughts on the African continent: A geospatial and long-term perspective","volume":"18","author":"Masih","year":"2014","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_16","unstructured":"EM-DAT (2015, August 22). EM-DAT: The International Disaster Database. Available online: http:\/\/www.emdat.be\/."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1111\/j.1752-1688.1997.tb03550.x","article-title":"Predictive assessment of severity of agricultural droughts based on agro-climatic factors","volume":"33","author":"Kumar","year":"1997","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.jaridenv.2004.10.011","article-title":"Statistical analysis of wheat yield under drought conditions","volume":"61","author":"Leilah","year":"2005","journal-title":"J. Arid Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1007\/s00477-005-0238-4","article-title":"Drought forecasting using stochastic models","volume":"19","author":"Mishra","year":"2005","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1007\/s00477-010-0366-3","article-title":"Application of linear stochastic models for drought forecasting in the Buyuk Menderes river basin, western Turkey","volume":"24","author":"Durdu","year":"2010","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/s00477-006-0058-1","article-title":"Streamflow drought time series forecasting","volume":"21","author":"Modarres","year":"2007","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_22","first-page":"1398","article-title":"Drought forecasting based on the remote sensing data using. ARIMA Models","volume":"51","author":"Han","year":"2010","journal-title":"ARIMA Model."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1007\/s00477-008-0277-8","article-title":"Streamflow drought time series forecasting: A case study in a small watershed in North West Spain","volume":"23","author":"Fernandez","year":"2009","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.1111\/j.1752-1688.1997.tb03560.x","article-title":"An early warning system for drought management using the palmer drought index","volume":"33","author":"Lohani","year":"1997","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.agwat.2004.09.039","article-title":"Drought class transition analysis through Markov and Loglinear models, an approach to early warning","volume":"77","author":"Paulo","year":"2005","journal-title":"Agric. Water Manag."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1007\/s11269-006-9062-y","article-title":"Drought forecasting using the Standardized Precipitation Index","volume":"21","author":"Cancelliere","year":"2007","journal-title":"Water Resour. Manag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1111\/j.1752-1688.2003.tb03704.x","article-title":"Drought indicators and triggers: A stochastic approach to evaluation","volume":"39","author":"Steinemann","year":"2003","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1155\/S1026022602000262","article-title":"Markov chain analysis of weekly rainfall data in determining drought-proneness","volume":"7","author":"Banik","year":"2002","journal-title":"Discret. Dyn. Nat. Soc."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1002\/ird.94","article-title":"A Markov chain simulation model for predicting critical wet and dry spells in Kenya: Analysing rainfall events in the Kano plains","volume":"52","author":"Ochola","year":"2003","journal-title":"Irrig. Drain."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.jhydrol.2006.05.022","article-title":"Analysis of SPI drought class transitions using loglinear models","volume":"331","author":"Moreira","year":"2006","journal-title":"J. Hydrol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2103","DOI":"10.1002\/joc.1498","article-title":"Drought forecasting using artificial neural networks and time series of drought indices","volume":"27","author":"Morid","year":"2007","journal-title":"Int. J. Climatol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.ecolmodel.2006.04.017","article-title":"Drought forecasting using feed-forward recursive neural network","volume":"198","author":"Mishra","year":"2006","journal-title":"Ecol. Model."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1061\/(ASCE)1084-0699(2003)8:6(319)","article-title":"A nonlinear model for drought forecasting based on conjunction of wavelet transforms and neural networks","volume":"8","author":"Kim","year":"2003","journal-title":"J. Hydrol. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1061\/(ASCE)1084-0699(2007)12:6(626)","article-title":"Drought forecasting using a hybrid stochastic and neural network model","volume":"12","author":"Mishra","year":"2007","journal-title":"J. Hydrol. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1007\/s00477-008-0288-5","article-title":"Adaptive Neuro-Fuzzy Inference System for drought forecasting","volume":"23","author":"Bacanli","year":"2009","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/S0022-1694(99)00131-6","article-title":"Application of fuzzy rule-based modeling technique to regional drought","volume":"224","author":"Pongracz","year":"1999","journal-title":"J. Hydrol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s10584-006-9157-8","article-title":"Analysis of drought determinants for the Colorado River Basin","volume":"82","author":"Balling","year":"2007","journal-title":"Clim. Chang."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1175\/JAM2401.1","article-title":"Using climate forecasts for drought management","volume":"75","author":"Steinemann","year":"2006","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s00704-010-0317-4","article-title":"Application of global SST and SLP data for drought forecasting on Tehran plain using data mining and ANFIS techniques","volume":"104","author":"Farokhnia","year":"2011","journal-title":"Theor. Appl. Climatol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2008JD010485","article-title":"Data mining for evolution of association rules for droughts and floods in India using climate inputs","volume":"114","author":"Dhanya","year":"2009","journal-title":"J. Geophys. Res."},{"key":"ref_41","unstructured":"Vasiliades, L., and Loukas, A. (2010, January 2\u20137). Spatiotemporal drought forecasting using nonlinear models. Proceedings of the EGU General Assembly 2010, Vienna, Austria."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1023\/B:NHAZ.0000035020.76733.0b","article-title":"Drought Monitoring Using Data Mining Techniques: A Case Study for Nebraska, USA","volume":"33","author":"Tadesse","year":"2004","journal-title":"Nat. Hazards"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.jhydrol.2011.03.049","article-title":"Drought modeling\u2014A review","volume":"403","author":"Mishra","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_44","first-page":"1","article-title":"Drought Spatial Object Prediction Approach using Artificial Neural Network","volume":"3","author":"Demisse","year":"2015","journal-title":"Geoinform. Geostat. Overv."},{"key":"ref_45","unstructured":"Demisse, G.B. (2013). Knowledge Discovery From Satellite Images for Drought Monitoring. [Ph.D. Thesis, Addis Ababa University]."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1175\/JHM450.1","article-title":"Twentieth-century drought in the conterminous United States","volume":"6","author":"Andreadis","year":"2005","journal-title":"J. Hydrometeorol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s00704-008-0020-x","article-title":"Application of relative drought indices in assessing climate-change impacts on drought conditions in Czechia","volume":"96","author":"Dubrovsky","year":"2008","journal-title":"Theor. Appl. Climatol."},{"key":"ref_48","unstructured":"NOAA (2016, January 04). DROUGHT: Monitoring Economic, Environmental, and Social Impacts, Available online: http:\/\/www.ncdc.noaa.gov\/news\/drought-monitoring-economic-environmental-and-social-impacts."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s00382-007-0340-z","article-title":"Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations","volume":"31","author":"Sheffield","year":"2008","journal-title":"Clim. Dyn."},{"key":"ref_50","unstructured":"UCS (2016, December 10). Causes of Drought: What\u2019s the Climate Connection?. Available online: http:\/\/www.ucsusa.org\/global_warming\/science_and_impacts\/impacts\/causes-of-drought-climate-change-connection.html#.VprO5k98wRI."},{"key":"ref_51","unstructured":"National Meteorological Services Agency (NMSA) (1996). Assessment of Drought in Ethiopia."},{"key":"ref_52","unstructured":"EMA (2016, December 22). Ethiopian Mapping Agency (EMA), Available online: http:\/\/www.ema.gov.et\/."},{"key":"ref_53","unstructured":"FEWSNET (2011, June 20). Normalized Difference Vegetation Index, Product Documentation, Available online: http:\/\/earlywarning.usgs.gov\/fews\/africa\/web\/readme.php?symbol=nd."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1080\/01431168608948945","article-title":"Characteristics of maximum-value composite images from temporal data","volume":"7","author":"Holben","year":"1986","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","unstructured":"USGS (2011, September 01). USGS\u2014Earth Resources Observation and Science (EROS) Center-Elevation Data, Available online: http:\/\/eros.usgs.gov\/#\/Find_Data\/Products_and_Data_Available\/gtopo30\/hydro\/africa."},{"key":"ref_56","unstructured":"Ecodiv.org. (2011, September 01). Atlas of the Potential Vegetation of Ethiopia. Available online: http:\/\/ecodiv.org\/atlas_ethiopia\/index.html."},{"key":"ref_57","unstructured":"ESA (2011, November 10). European Space Agency, Global Land Cover Map. Available online: http:\/\/ionia1.esrin.esa.int\/index.asp."},{"key":"ref_58","unstructured":"GLCF (2010, December 20). Global Land Cover Facility. Available online: http:\/\/www.landcover.org\/aboutUs\/."},{"key":"ref_59","unstructured":"NOAA (2011, September 01). National Oceanic and Atmospheric Administration, Climate Indices: Monthly Atmospheric and Ocean Time Series, Available online: http:\/\/www.esrl.noaa.gov\/psd\/data\/climateindices\/list\/."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1029\/2000GL012745","article-title":"The Atlantic multidecadal oscillation and it\u2019s relation to rainfall and river flows in the continental U.S.","volume":"28","author":"Enfield","year":"2001","journal-title":"Geophys. Res. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1126\/science.269.5224.676","article-title":"Decadal trends in the North Atlantic Oscillation and relationships to regional temperature and precipitation","volume":"269","author":"Hurrell","year":"1995","journal-title":"Science"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1002\/(SICI)1097-0088(19971115)17:13<1433::AID-JOC203>3.0.CO;2-P","article-title":"Extension to the North Atlantic Oscillation using early instrumental pressure observations from Gibraltar and South-West Iceland","volume":"17","author":"Jones","year":"1997","journal-title":"Int. J. Climatol."},{"key":"ref_63","first-page":"315","article-title":"Measuring the strength of ENSO\u2014How does 1997\/98 rank?","volume":"53","author":"Wolter","year":"1998","journal-title":"Weather Forecast."},{"key":"ref_64","first-page":"1800","article-title":"Partial Lease Square Solutions for Multicomponent Analysis","volume":"55","author":"Frank","year":"1983","journal-title":"Lab. Chemom."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1198\/016214504000001565","article-title":"Bayesian Variable Selection in Clustering HighDimensional Data","volume":"100","author":"Tadesse","year":"2005","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.chemolab.2007.06.007","article-title":"Soil parameter quantification by NIRS as a Chemometric challenge at \u2018Chimiom\u00e9trie 2006\u2019","volume":"91","author":"Pierna","year":"2008","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.chemolab.2004.02.007","article-title":"Multivariate adaptive regression splines\u2014Studies of HIV reverse transcriptase inhibitors","volume":"72","author":"Xu","year":"2004","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_69","unstructured":"Rulequest (2015, September 20). An Overview of Cubist. Available online: http:\/\/www.rulequest.com\/cubistwinhtml."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"25","DOI":"10.2747\/1548-1603.47.1.25","article-title":"The Vegetation Outlook (VegOut): A New Method for Predicting Vegetation Seasonal Greenness","volume":"47","author":"Tadesse","year":"2010","journal-title":"GIScience Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"16","DOI":"10.2747\/1548-1603.45.1.16","article-title":"The Vegetation Drought Response Index (VegDRI): A New Integrated Approach for Monitoring Drought Stress in Vegetation","volume":"45","author":"Brown","year":"2008","journal-title":"GIScience Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Tadesse, T., Demisse, G., Zaitchik, B., and Dinku, T. (2014). Satellite-based hybrid drought monitoring tool for prediction of vegetation condition in Eastern Africa: A case study for Ethiopia. Water Resour. Res., 50.","DOI":"10.1002\/2013WR014281"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"4032","DOI":"10.1109\/TGRS.2013.2279020","article-title":"Drought Prediction System for Improved Climate Change Mitigation","volume":"52","author":"Berhan","year":"2014","journal-title":"IEEE Transs Geosci. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.chemolab.2008.06.003","article-title":"Regression rules as a tool for predicting soil properties from infrared reflectance spectroscopy","volume":"94","author":"Minasny","year":"2008","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.ejps.2007.03.004","article-title":"Investigation of an artificial intelligence technology-Model trees Novel applications for an immediate release tablet formulation database","volume":"31","author":"Shao","year":"2007","journal-title":"Eur. J. Pharm. Sci."},{"key":"ref_76","first-page":"815","article-title":"Split selection methods for classification trees","volume":"7","author":"Loh","year":"1997","journal-title":"Stat. Sin."},{"key":"ref_77","unstructured":"Quinlan, J.R. (1992, January 16\u201318). Learning with Continuous Classes. Proceedings of the AI 92 (Adams & Sterling, Eds.), Hobart, Australia."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/S0004-3702(03)00019-5","article-title":"Possibilistic instance-based learning","volume":"148","author":"Hullermeier","year":"2003","journal-title":"Artif. Intell."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1023\/A:1006538427943","article-title":"Lazy Learning","volume":"11","author":"Aha","year":"1997","journal-title":"Artif. Intell. Rev."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/BF00153759","article-title":"Instance-based learning algorithms","volume":"6","author":"Aha","year":"1991","journal-title":"Mach. Learn."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1177\/875647939000600106","article-title":"Interpretation of the correlation coefficient: A basic review","volume":"6","author":"Taylor","year":"1990","journal-title":"J. Diagn. Med. Sonogr."},{"key":"ref_82","unstructured":"Witten, I., Frank, E., and Hall, M.A. (2011). Data Mining: Practical Machine Learning Tools and Techniques, Elsevier. [2nd ed.]."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.geoderma.2004.06.007","article-title":"Australia-wide predictions of soil properties using decision trees","volume":"124","author":"Henderson","year":"2005","journal-title":"Geoderma"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.ijforecast.2006.03.001","article-title":"Another look at measures of forecast accuracy","volume":"22","author":"Hyndman","year":"2006","journal-title":"Int. J. Forecast."},{"key":"ref_85","first-page":"279","article-title":"Cautionary note about R 2","volume":"39","author":"Kvalseth","year":"1985","journal-title":"Am. Stat."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1723","DOI":"10.1002\/joc.1623","article-title":"Trends and spatial distribution of annual and seasonal rainfall in Ethiopia","volume":"28","author":"Cheung","year":"2008","journal-title":"Int. J. Climatol."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1175\/MWR3304.1","article-title":"Predictability of June\u2013September Rainfall in Ethiopia","volume":"135","author":"Korecha","year":"2006","journal-title":"Mon. Weather Rev."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s00703-005-0127-x","article-title":"Characterization and variability of Kiremt rainy season over Ethiopia","volume":"89","author":"Segele","year":"2005","journal-title":"Meteorol. Atmos. Phys."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1002\/joc.1052","article-title":"Recent Changes In Rainfall and Rainy Days In Ethiopia","volume":"24","author":"Seleshi","year":"2004","journal-title":"Int. J. Climatol."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging Predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Oza, N.C. (2004). Ensemble Data Mining Methods.","DOI":"10.4018\/978-1-59140-557-3.ch085"},{"key":"ref_92","unstructured":"Fortmann-Roe, S. (2016, October 14). Understanding the Bias-Variance Tradeoff. Available online: http:\/\/scott.fortmann-roe.com\/docs\/BiasVariance.html."},{"key":"ref_93","unstructured":"Ruefenacht, B., Hoppus, A., Caylor, J., Nowak, D., Walton, J., Yang, L., and Koeln, G. (2002). Analysis of Canopy Cover and Impervious Surface Cover of Zone 41, San Dimas Technology & Development Center."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/8\/3\/79\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:41:17Z","timestamp":1760208077000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/8\/3\/79"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,7,3]]},"references-count":93,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2017,9]]}},"alternative-id":["info8030079"],"URL":"https:\/\/doi.org\/10.3390\/info8030079","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,7,3]]}}}