<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.4 20241031//EN"
        "https://jats.nlm.nih.gov/publishing/1.4/JATS-journalpublishing1-4.dtd">
<article  article-type="research-article"        dtd-version="1.4">
            <front>

                <journal-meta>
                                                                <journal-id>saucis</journal-id>
            <journal-title-group>
                                                                                    <journal-title>Sakarya University Journal of Computer and Information Sciences</journal-title>
            </journal-title-group>
                                        <issn pub-type="epub">2636-8129</issn>
                                                                                            <publisher>
                    <publisher-name>Sakarya University</publisher-name>
                </publisher>
                    </journal-meta>
                <article-meta>
                                        <article-id pub-id-type="doi">10.35377/saucis...1626057</article-id>
                                                                <article-categories>
                                            <subj-group  xml:lang="en">
                                                            <subject>Computer Software</subject>
                                                    </subj-group>
                                            <subj-group  xml:lang="tr">
                                                            <subject>Bilgisayar Yazılımı</subject>
                                                    </subj-group>
                                    </article-categories>
                                                                                                                                                        <title-group>
                                                                                                                                                            <article-title>Flood Area Prediction using a Stacked Ensemble of Tree-Based Algorithms</article-title>
                                                                                                    </title-group>
            
                                                    <contrib-group content-type="authors">
                                                                        <contrib contrib-type="author">
                                                                    <contrib-id contrib-id-type="orcid">
                                        https://orcid.org/0000-0001-6056-2476</contrib-id>
                                                                <name>
                                    <surname>Adetunji</surname>
                                    <given-names>Olusogo Julius</given-names>
                                </name>
                                                                    <aff>Olabisi Onabanjo University Ago Iwoye, Nigeria</aff>
                                                            </contrib>
                                                                                </contrib-group>
                        
                                        <pub-date pub-type="pub" iso-8601-date="20250630">
                    <day>06</day>
                    <month>30</month>
                    <year>2025</year>
                </pub-date>
                                        <volume>8</volume>
                                        <issue>2</issue>
                                        <fpage>322</fpage>
                                        <lpage>345</lpage>
                        
                        <history>
                                    <date date-type="received" iso-8601-date="20250125">
                        <day>01</day>
                        <month>25</month>
                        <year>2025</year>
                    </date>
                                                    <date date-type="accepted" iso-8601-date="20250613">
                        <day>06</day>
                        <month>13</month>
                        <year>2025</year>
                    </date>
                            </history>
                                        <permissions>
                    <copyright-statement>Copyright © 2018, Sakarya University Journal of Computer and Information Sciences</copyright-statement>
                    <copyright-year>2018</copyright-year>
                    <copyright-holder>Sakarya University Journal of Computer and Information Sciences</copyright-holder>
                </permissions>
            
                                                                                                                        <abstract><p>Floods cause significant loss of life, property damage, and long-term socioeconomic disruptions, with over 100 annual deaths globally. This research addresses the drawbacks of the existing models, such as overfitting effects, inadequate dataset and limited study areas through the adoption of a stacked ensemble-based model. The model contained five different tree - based models namely hoeffding tree, decision tree, functional tree, reduced error pruning (REP) tree and decision stump algorithms. The model was implemented as a system using MATLAB Simulink, version 2020a on laptop with 4GB Memory. Experimental results indicate that REP Tree performed better than other four individual tree algorithms with accuracy of 98.74%, 97.81% and 97.43% for Dataset A, Dataset B and Dataset C respectively. For Dataset A, stacked ensemble model performed better than single algorithms with accuracy, precision, specificity, f1score and recall of 99.62%, 99.51%, 99.51%, 99.63% and 99.73% respectively. For Dataset B, stacked ensemble model also performed better than single algorithms with accuracy, precision, specificity, f1score and recall of 98.45%, 99.11%, 98.12%, 97.37% and 99.06% respectively. For Dataset C, stacked ensemble model performed better than single algorithms with accuracy, precision, specificity, f1score and recall of 98.75%, 99.25%, 99.64%, 99.90% and 99.24% respectively. Our model’s 99.62% accuracy on Dataset A demonstrates potential for integration with real-time sensor networks, enabling scalable flood early-warning systems in vulnerable regions like Lagos and Kuala Lumpur.</p></abstract>
                                                            
            
                                                                                        <kwd-group>
                                                    <kwd>Ensemble</kwd>
                                                    <kwd>  Flood</kwd>
                                                    <kwd>  Stacked</kwd>
                                                    <kwd>  Tree Based</kwd>
                                            </kwd-group>
                            
                                                                                                                                                    </article-meta>
    </front>
    <back>
                            <ref-list>
                                    <ref id="ref1">
                        <label>1</label>
                        <mixed-citation publication-type="journal">I. M. Magami, S. Yahaya,K. Mohammed, “Causes and consequences of flooding in Nigeria: a review Alternative coagulants for water clarification in low-and middle-income communities View project”, no November 2016, 2014, [Online]. Available at: https://www.researchgate.net/publication/262562763</mixed-citation>
                    </ref>
                                    <ref id="ref2">
                        <label>2</label>
                        <mixed-citation publication-type="journal">V. Nhu, P. T. Ngo, T. D. Pham, J. Dou,X. Song, “A New Hybrid Firefly – PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping”, bll 1–18, 2020.</mixed-citation>
                    </ref>
                                    <ref id="ref3">
                        <label>3</label>
                        <mixed-citation publication-type="journal">O. Petrucci, “Review article: Factors leading to the occurrence of flood fatalities: A systematic review of research papers published between 2010 and 2020”, Nat. Hazards Earth Syst. Sci., vol 22, no 1, bll 71–83, 2022, doi: 10.5194/nhess-22-71-2022.</mixed-citation>
                    </ref>
                                    <ref id="ref4">
                        <label>4</label>
                        <mixed-citation publication-type="journal">A. Arora et al., “Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for fl ood susceptibility prediction mapping in the Middle Ganga Plain , India”, Sci. Total Environ., vol 750, bl 141565, 2021, doi: 10.1016/j.scitotenv.2020.141565.</mixed-citation>
                    </ref>
                                    <ref id="ref5">
                        <label>5</label>
                        <mixed-citation publication-type="journal">S. Žurovec, J.; Čadro, “SOIL-WATER CHARACTERISTIC CURVE AND RETENTION OF WATER FOR DIFFERENT TYPES OF AGRICULTURAL SOILS IN TUZLA CANTON Jasminka Žurovec 1 , Sabrija Čadro 1 Original scientific paper”, vol LXI, no 66, bll 1–6, 2013.</mixed-citation>
                    </ref>
                                    <ref id="ref6">
                        <label>6</label>
                        <mixed-citation publication-type="journal">M. G. Grillakis, A. G. Koutroulis, J. Komma, I. K. Tsanis, W. Wagner,G. Blöschl, “Initial soil moisture effects on flash flood generation – A comparison between basins of contrasting hydro-climatic conditions”, J. Hydrol., vol 541, bll 206–217, 2016, doi: 10.1016/j.jhydrol.2016.03.007.</mixed-citation>
                    </ref>
                                    <ref id="ref7">
                        <label>7</label>
                        <mixed-citation publication-type="journal">A. Rakhim, Nurnawaty, “An Environmental Development Study: The Effect of Vegetation to Reduce Runoff”, IOP Conf. Ser. Earth Environ. Sci., vol 382, no 1, bll 1–6, 2019, doi: 10.1088/1755-1315/382/1/012027.</mixed-citation>
                    </ref>
                                    <ref id="ref8">
                        <label>8</label>
                        <mixed-citation publication-type="journal">J. Geris et al., “Surface water-groundwater interactions and local land use control water quality impacts of extreme rainfall and flooding in a vulnerable semi-arid region of Sub-Saharan Africa”, J. Hydrol., vol 609, no September 2021, bl 127834, 2022, doi: 10.1016/j.jhydrol.2022.127834.</mixed-citation>
                    </ref>
                                    <ref id="ref9">
                        <label>9</label>
                        <mixed-citation publication-type="journal">P. O. Youdeowei, H. O. Nwankwoala,D. D. Desai, “Dam structures and types in Nigeria: Sustainability and effectiveness”, Water Conserv. Manag., vol 3, no 1, bll 20–26, 2019, doi: 10.26480/wcm.01.2019.20.26.</mixed-citation>
                    </ref>
                                    <ref id="ref10">
                        <label>10</label>
                        <mixed-citation publication-type="journal">M. Antonetti, C. Horat, I. V Sideris, M. Zappa, “Ensemble flood forecasting considering dominant runoff processes – Part 1 : Set-up and application to nested basins ( Emme , Switzerland )”, bll 19–40, 2019.</mixed-citation>
                    </ref>
                                    <ref id="ref11">
                        <label>11</label>
                        <mixed-citation publication-type="journal">A. Shirzadi, S. Asadi, H. Shahabi, S. Ronoud, J. J. Clague, “A novel ensemble learning based on Bayesian Belief Network coupled with an extreme learning machine for flash flood susceptibility mapping”, Eng. Appl. Artif. Intell., vol 96, no September, bl 103971, 2020, doi: 10.1016/j.engappai.2020.103971.</mixed-citation>
                    </ref>
                                    <ref id="ref12">
                        <label>12</label>
                        <mixed-citation publication-type="journal">A. V Kalyuzhnaya, A. V Boukhanovsky, “Computational uncertainty management for coastal flood prevention system”, Procedia - Procedia Comput. Sci., vol 51, bll 2317–2326, 2015, doi: 10.1016/j.procs.2015.05.397.</mixed-citation>
                    </ref>
                                    <ref id="ref13">
                        <label>13</label>
                        <mixed-citation publication-type="journal">X. Zhang, E. N. Anagnostou, C. S. Schwartz, “NWP-Based Adjustment of IMERG Precipitation for Flood-Inducing Complex Terrain Storms : Evaluation over CONUS”, Am. Rom. Acad. Arts Sci., 2018, doi: 10.3390/rs10040642.</mixed-citation>
                    </ref>
                                    <ref id="ref14">
                        <label>14</label>
                        <mixed-citation publication-type="journal">S. N. Jonkman, A. Curran, and L. M. Bouwer, “Floods have become less deadly: an analysis of global flood fatalities   1975–2022,” Nat. Hazards, vol. 120, no. 7, pp. 6327–6342, 2024, doi: 10.1007/s11069-024-06444-0.</mixed-citation>
                    </ref>
                                    <ref id="ref15">
                        <label>15</label>
                        <mixed-citation publication-type="journal">Q. B. Pham et al., “Evaluation of various boosting ensemble algorithms for predicting flood hazard susceptibility areas,” Geomatics, Nat. Hazards Risk, vol. 12, no. 1, pp. 2607–2628, 2021, doi: 10.1080/19475705.2021.1968510.</mixed-citation>
                    </ref>
                                    <ref id="ref16">
                        <label>16</label>
                        <mixed-citation publication-type="journal">A. Towfiqul Islam et al., “Flood susceptibility modelling using advanced ensemble machine learning models,” Geosci. Front., vol. 12, no. 3, 2021, doi: 10.1016/j.gsf.2020.09.006.</mixed-citation>
                    </ref>
                                    <ref id="ref17">
                        <label>17</label>
                        <mixed-citation publication-type="journal">O. J. Adetunji, I. A. Adeyanju,A. O. Esan, “Flood Areas Prediction in Nigeria using Artificial Neural Network”, 2023 Int. Conf. Sci. Eng. Bus. Sustain. Dev. Goals, bll 1–6, 2023, doi: 10.1109/SEB-SDG57117.2023.10124629.</mixed-citation>
                    </ref>
                                    <ref id="ref18">
                        <label>18</label>
                        <mixed-citation publication-type="journal">T. Rahman et al., “Flood Prediction Using Ensemble Machine Learning Model,” 2023 5th Int. Conf. Hum. - Comput. Interact. Optim. Robot. Appl. (HORA), IEEE, no. July, 2023, doi: 10.1109/HORA58378.2023.10156673.</mixed-citation>
                    </ref>
                                    <ref id="ref19">
                        <label>19</label>
                        <mixed-citation publication-type="journal">K. R. Oloruntoba, K. Taiwo, and J. B. Agbogun, “Flood Prediction in Nigeria Using Ensemble Machine Learning Techniques,” Ilorin J. Sci., vol. 10, no. 1, 2023, doi: 10.54908/iljs.2023.10.01.004.</mixed-citation>
                    </ref>
                                    <ref id="ref20">
                        <label>20</label>
                        <mixed-citation publication-type="journal">S. Hajji et al., “Enhancing flood prediction through remote sensing, machine learning, and Google Earth Engine,” Front. Water, vol. 7, no. March, 2025, doi: 10.3389/frwa.2025.1514047.</mixed-citation>
                    </ref>
                                    <ref id="ref21">
                        <label>21</label>
                        <mixed-citation publication-type="journal">E. M. Ferrouhi and I. Bouabdallaoui, “A comparative study of ensemble learning algorithms for high-frequency trading,” Sci. African, vol. 24, no. August 2023, p. e02161, 2024, doi: 10.1016/j.sciaf.2024.e02161.</mixed-citation>
                    </ref>
                                    <ref id="ref22">
                        <label>22</label>
                        <mixed-citation publication-type="journal">I. O. Adelekan, “Flood risk management in the coastal city of Lagos, Nigeria”, J. Flood Risk Manag., vol 9, no 3, bll 255–264, 2016, doi: 10.1111/jfr3.12179.</mixed-citation>
                    </ref>
                                    <ref id="ref23">
                        <label>23</label>
                        <mixed-citation publication-type="journal">A. Domeneghetti et al., “Flood risk mitigation in developing countries: Deriving accurate topographic data for remote areas under severe time and economic constraints”, J. Flood Risk Manag., vol 8, no 4, bll 301–314, 2015, doi: 10.1111/jfr3.12095.</mixed-citation>
                    </ref>
                                    <ref id="ref24">
                        <label>24</label>
                        <mixed-citation publication-type="journal">A. O. Julius, A. O. Ayokunle,F. O. Ibrahim, “Early Diabetic Risk Prediction using Machine Learning Classification Techniques”, Int. J. Innov. Sci. Res. Technol., vol 6, no 9, bll 502–507, 2021.</mixed-citation>
                    </ref>
                                    <ref id="ref25">
                        <label>25</label>
                        <mixed-citation publication-type="journal">H. Bao et al., “Coupling ensemble weather predictions based on TIGGE database with Grid-Xinanjiang model for flood forecast”, Adv. Geosci., bll 61–67, 2011, doi: 10.5194/adgeo-29-61-2011.</mixed-citation>
                    </ref>
                                    <ref id="ref26">
                        <label>26</label>
                        <mixed-citation publication-type="journal">E. H. Ighile, H. Shirakawa, en H. Tanikawa, “A Study on the Application of GIS and Machine Learning to Predict Flood Areas in Nigeria”, Sustain., vol 14, no 9, 2022, doi: 10.3390/su14095039.</mixed-citation>
                    </ref>
                                    <ref id="ref27">
                        <label>27</label>
                        <mixed-citation publication-type="journal">S. H. Elsafi, “Artificial Neural Networks ( ANNs ) for flood forecasting at Dongola Station in the River Nile , Sudan Sulafa Hag Elsafi”, ALEXANDRIA Eng. J., 2019, doi: 10.1016/j.aej.2014.06.010.</mixed-citation>
                    </ref>
                                    <ref id="ref28">
                        <label>28</label>
                        <mixed-citation publication-type="journal">J. Wu, H. Liu, G. Wei, T. Song, C. Zhang,H. Zhou, “Flash flood forecasting using support vector regression model in a small mountainous catchment”, Water (Switzerland), vol 11, no 7, 2019, doi: 10.3390/w11071327.</mixed-citation>
                    </ref>
                                    <ref id="ref29">
                        <label>29</label>
                        <mixed-citation publication-type="journal">M. Madhuram, A. Kakar, A. Sharma, S. Chaudhuri, “Flood Prediction and warning system using SVM and ELM models .”, no 4, bll 5366–5369, 2019, doi: 10.35940/ijrte.D7573.118419.</mixed-citation>
                    </ref>
                                    <ref id="ref30">
                        <label>30</label>
                        <mixed-citation publication-type="journal">R. Costache, D. Tien, “Identi fi cation of areas prone to fl ash- fl ood phenomena using multiple- criteria decision-making , bivariate statistics , machine learning and their ensembles”, Sci. Total Environ., vol 712, bl 136492, 2020, doi: 10.1016/j.scitotenv.2019.136492.</mixed-citation>
                    </ref>
                                    <ref id="ref31">
                        <label>31</label>
                        <mixed-citation publication-type="journal">B. T. Pham et al., “Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques”, Geosci. Front., vol 12, no 3, bl 101105, 2021, doi: 10.1016/j.gsf.2020.11.003.</mixed-citation>
                    </ref>
                                    <ref id="ref32">
                        <label>32</label>
                        <mixed-citation publication-type="journal">S. Janizadeh et al., “Prediction Success of Machine Learning Methods for Flash Flood sustainability Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed , Iran”, no September, 2019, doi: 10.3390/su11195426.</mixed-citation>
                    </ref>
                                    <ref id="ref33">
                        <label>33</label>
                        <mixed-citation publication-type="journal">N. M. Nawi, M. Makhtar, M. Z. Salikon,Z. A. Afip, “A comparative analysis of classification techniques on predicting flood risk”, vol 18, no 3, bll 1342–1350, 2020, doi: 10.11591/ijeecs.v18.i3.pp1342-1350.</mixed-citation>
                    </ref>
                                    <ref id="ref34">
                        <label>34</label>
                        <mixed-citation publication-type="journal">N. Razali, S. Ismail,A. Mustapha, “Machine learning approach for flood risks prediction”, vol 9, no 1, bll 73–80, 2020, doi: 10.11591/ijai.v9.i1.pp73-80.</mixed-citation>
                    </ref>
                                    <ref id="ref35">
                        <label>35</label>
                        <mixed-citation publication-type="journal">J. H. Rao, D. Patle,S. K. Sharma, “Remote Sensing and GIS Technique for Mapping Land Use / Land Cover of Kiknari Watershed”, Ind. J. Pure App. Biosci., vol 8, bll 455–463, 2020.</mixed-citation>
                    </ref>
                                    <ref id="ref36">
                        <label>36</label>
                        <mixed-citation publication-type="journal">A. Ali, M. Ahmed, S. Naeem, S. Anam,M. M. Ahmed, “An Unsupervised Machine Learning Algorithms: Comprehensive Review”, Int. J. Comput. Digit. Syst., vol 20, no April, bll 2210–142, 2023, doi: 10.12785/ijcds/130172.</mixed-citation>
                    </ref>
                                    <ref id="ref37">
                        <label>37</label>
                        <mixed-citation publication-type="journal">A. H. Tanim, C. B. McRae, H. Tavakol‐davani, E. Goharian, “Flood Detection in Urban Areas Using Satellite Imagery and Machine Learning”, Water (Switzerland), vol 14, no 7, 2022, doi: 10.3390/w14071140.</mixed-citation>
                    </ref>
                                    <ref id="ref38">
                        <label>38</label>
                        <mixed-citation publication-type="journal">O. J. Adetunji, I. A. Adeyanju, A. O. Esan, A. A. Sobowale, “Flood Image Classification using Convolutional Neural Networks”, ABUAD J. Eng. Res. Dev., vol 6, no 2, bll 113–121, 2023.</mixed-citation>
                    </ref>
                                    <ref id="ref39">
                        <label>39</label>
                        <mixed-citation publication-type="journal">P. Domingos and G. Hulten, “Mining High-Speed Data Streams,” Proc. 6th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, Bost., pp. 71–80, 2000.</mixed-citation>
                    </ref>
                                    <ref id="ref40">
                        <label>40</label>
                        <mixed-citation publication-type="journal">J. Gama, “Functional trees for classification,” Proc. 2001 IEEE Int. Conf. Data Min., pp. 147–154, 2001.</mixed-citation>
                    </ref>
                                    <ref id="ref41">
                        <label>41</label>
                        <mixed-citation publication-type="journal">I. H. Witten, E. Frank, and M. A. Hall, “Data Mining: Practical machine learning tools and techniques,” Morgan Kaufmann Publ. Inc., 2011.</mixed-citation>
                    </ref>
                                    <ref id="ref42">
                        <label>42</label>
                        <mixed-citation publication-type="journal">A. Arabameri et al., “Modeling spatial flood using novel ensemble artificial intelligence approaches in northern Iran,” Remote Sens., vol. 12, no. 20, pp. 1–30, 2020, doi: 10.3390/rs12203423.</mixed-citation>
                    </ref>
                                    <ref id="ref43">
                        <label>43</label>
                        <mixed-citation publication-type="journal">P. G. Sonia Singh, “Comparative Study ID3,CART AND C4.5 Decision Tree Algorithm,” Int. J. Adv. Inf. Sci. Technol., vol. 27, no. 27, p. 98, 2014.</mixed-citation>
                    </ref>
                                    <ref id="ref44">
                        <label>44</label>
                        <mixed-citation publication-type="journal">S. K. Jayanthi and S. Sasikala, “REPTREE CLASSIFIER FOR IDENTIFYING LINK SPAM IN WEB SEARCH ENGINES,” ICTACT J. SOFT Comput., pp. 498–505, 2013, doi: 10.21917/ijsc.2013.0075.</mixed-citation>
                    </ref>
                                    <ref id="ref45">
                        <label>45</label>
                        <mixed-citation publication-type="journal">M. Chiu, Y. Yu, H. . Liaw, and L. Hao, “The use of facial micro-expression state and tree-forest model for predicting conceptual-conflict based conceptual change.Science Education Research,” Engag. Learn. a Sustain. Futur. (ESERA e proceeding, 2016.</mixed-citation>
                    </ref>
                                    <ref id="ref46">
                        <label>46</label>
                        <mixed-citation publication-type="journal">O. J. Adetunji and O. T. Ibitoye, “Development of an Intrusion Detection Model using Long Short Term Memory Algorithm,” 2024 IEEE 5th Int. Conf. Electro-Computing Technol. Humanit., pp. 1–5, 2024, doi: 10.1109/NIGERCON62786.2024.10926945.</mixed-citation>
                    </ref>
                                    <ref id="ref47">
                        <label>47</label>
                        <mixed-citation publication-type="journal">O. T. Ibitoye, A. O. Ojo, I. O. Bisirodipe, M. A. Ogunlade, N. I. Ogbodo, and O. J. Adetunji, “A Deep Learning-Based Autonomous Fire Detection and Suppression Robot,” 2024 IEEE 5th Int. Conf. Electro-Computing Technol. Humanit., pp. 1–4, 2024, doi: 10.1109/NIGERCON62786.2024.10927352.</mixed-citation>
                    </ref>
                                    <ref id="ref48">
                        <label>48</label>
                        <mixed-citation publication-type="journal">W. Dai, Y. Tang, N. Liao, S. Shujie and Z. Cai, “Urban flood prediction using ensemble artificial neural network: an investigation on improving model uncertainty”, Applied Water Science, pp. 1 – 10, 2024, doi.org/10.1007/s13201-024-02201-7</mixed-citation>
                    </ref>
                                    <ref id="ref49">
                        <label>49</label>
                        <mixed-citation publication-type="journal">S. Hajji, Sonia, K. Abdelrahman, A. Boudhar, A. Elaloui, M. Ismaili, M. El Bouzekraoui, M. Chikh Essbiti, A. Kahal, B. Mondal and M. Namous, “ Enhancing flood prediction through remote sensing, machine learning, and Google Earth Engine”, Frontiers in Water, 2025, doi: 10.3389/frwa.2025.1514047</mixed-citation>
                    </ref>
                            </ref-list>
                    </back>
    </article>
