{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T11:05:06Z","timestamp":1779793506710,"version":"3.53.1"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:00:00Z","timestamp":1775779200000},"content-version":"vor","delay-in-days":9,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100005417","name":"Universiti Teknologi Malaysia","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005417","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s10489-026-07221-1","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T09:17:20Z","timestamp":1775812640000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning-based 2D and 3D traffic noise modelling in campus environments: a framework for vertical fa\u00e7ade exposure assessment"],"prefix":"10.1007","volume":"56","author":[{"given":"Khaled Yousef","family":"Almansi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Uznir","family":"Ujang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suhaibah","family":"Azri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nevil","family":"Wickramathilaka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"key":"7221_CR1","doi-asserted-by":"publisher","unstructured":"Abdur-Rouf K, Shaaban K (2022) Development of prediction models of transportation noise for roundabouts and signalized intersections. Transportation Research Part D: Transport and Environment. https:\/\/doi.org\/10.1016\/j.trd.2022.103174","DOI":"10.1016\/j.trd.2022.103174"},{"key":"7221_CR2","doi-asserted-by":"publisher","unstructured":"Adulaimi AAA, Pradhan B, Chakraborty S, Alamri A (2021) Traffic noise modelling using land use regression model based on machine learning, statistical regression and GIS. Energies. https:\/\/doi.org\/10.3390\/en14165095","DOI":"10.3390\/en14165095"},{"key":"7221_CR3","doi-asserted-by":"publisher","unstructured":"Ahmed AA, Pradhan B (2019) Vehicular traffic noise prediction and propagation modelling using neural networks and geospatial information system. Environ Monit Assess. https:\/\/doi.org\/10.1007\/s10661-019-7333-3","DOI":"10.1007\/s10661-019-7333-3"},{"key":"7221_CR4","doi-asserted-by":"publisher","first-page":"107375","DOI":"10.1109\/ACCESS.2021.3100855","volume":"9","author":"AA Ahmed","year":"2021","unstructured":"Ahmed AA, Pradhan B, Chakraborty S, Alamri A, Lee CW (2021) An optimized deep neural network approach for vehicular traffic noise trend modeling. IEEE Access 9:107375\u2013107386","journal-title":"IEEE Access"},{"key":"7221_CR5","doi-asserted-by":"publisher","unstructured":"Al-Shargabi AA, Almhafdy A, AlSaleem SS, Berardi U, Ali AAMM (2023) Optimizing regression models for predicting noise pollution caused by road traffic. Sustainability. https:\/\/doi.org\/10.3390\/su151310020","DOI":"10.3390\/su151310020"},{"key":"7221_CR6","first-page":"1777","volume":"25","author":"YH Ali","year":"2022","unstructured":"Ali YH, Rashid RA, Hamid SZA (2022) A machine learning for environmental noise classification in smart cities. Indones J Electr Eng Comput Sci 25:1777\u20131786","journal-title":"Indones J Electr Eng Comput Sci"},{"key":"7221_CR7","doi-asserted-by":"publisher","unstructured":"Alvares-Sanches T, Osborne PE, White PR (2021) Mobile surveys and machine learning can improve urban noise mapping: Beyond A-weighted measurements of exposure. Sci Total Environ. https:\/\/doi.org\/10.1016\/j.scitotenv.2021.145600","DOI":"10.1016\/j.scitotenv.2021.145600"},{"key":"7221_CR8","doi-asserted-by":"publisher","first-page":"2957","DOI":"10.1007\/s11069-022-05793-y","volume":"116","author":"HE Aydin","year":"2023","unstructured":"Aydin HE, Iban MC (2023) Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations. Nat Hazards 116:2957\u20132991","journal-title":"Nat Hazards"},{"key":"7221_CR9","unstructured":"Azari SH (2023) 3D geospatial data requirements for simulating noise using the Nord2000 model. Case study of the impact of building"},{"key":"7221_CR10","doi-asserted-by":"publisher","unstructured":"Chen S, He P, Yu B, Wei D, Chen Y (2024) The challenge of noise pollution in high-density urban areas: Relationship between 2D\/3D urban morphology and noise perception. Build Environ. https:\/\/doi.org\/10.1016\/j.buildenv.2024.111313","DOI":"10.1016\/j.buildenv.2024.111313"},{"key":"7221_CR11","first-page":"785","volume":"13\u201317\u2013Augu","author":"T Chen","year":"2016","unstructured":"Chen T, Guestrin C (2016) XGBoost: A scalable tree boosting system. Proc ACM SIGKDD Int Conf Knowl Discov Data Min 13\u201317\u2013Augu:785\u2013794","journal-title":"Proc ACM SIGKDD Int Conf Knowl Discov Data Min"},{"key":"7221_CR12","doi-asserted-by":"publisher","unstructured":"Chen Z, Fan W (2021) A freeway travel time prediction method based on an xgboost model. Sustainability. https:\/\/doi.org\/10.3390\/su13158577","DOI":"10.3390\/su13158577"},{"key":"7221_CR13","doi-asserted-by":"publisher","unstructured":"\u00c7olakkad\u0131o\u011flu D, Y\u00fccel M, Kahveci B, Ayd\u0131nol \u00d6 (2018) Determination of noise pollution on university campuses: a case study at \u00c7ukurova University campus in Turkey. Environ Monit Assess. https:\/\/doi.org\/10.1007\/s10661-018-6568-8","DOI":"10.1007\/s10661-018-6568-8"},{"key":"7221_CR14","doi-asserted-by":"publisher","first-page":"39948","DOI":"10.1007\/s11356-021-17577-1","volume":"29","author":"A Debnath","year":"2022","unstructured":"Debnath A, Singh PK, Banerjee S (2022) Vehicular traffic noise modelling of urban area\u2014a contouring and artificial neural network based approach. Environ Sci Pollut Res 29:39948\u201339972","journal-title":"Environ Sci Pollut Res"},{"key":"7221_CR15","doi-asserted-by":"publisher","unstructured":"Dong W, Huang Y, Lehane B, Ma G (2020) XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring. Automation in Construction. https:\/\/doi.org\/10.1016\/j.autcon.2020.103155","DOI":"10.1016\/j.autcon.2020.103155"},{"key":"7221_CR16","doi-asserted-by":"publisher","unstructured":"Fallah-Shorshani M, Yin X, McConnell R, Fruin S, Franklin M (2022) Estimating traffic noise over a large urban area: An evaluation of methods. Environ Int. https:\/\/doi.org\/10.1016\/j.envint.2022.107583","DOI":"10.1016\/j.envint.2022.107583"},{"key":"7221_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.eiar.2014.09.014","volume":"51","author":"PEK Fiedler","year":"2015","unstructured":"Fiedler PEK, Zannin PHT (2015) Evaluation of noise pollution in urban traffic hubs-Noise maps and measurements. Environ. Impact Assess. Rev. 51:1\u20139","journal-title":"Environ. Impact Assess. Rev."},{"key":"7221_CR18","doi-asserted-by":"publisher","unstructured":"Garg N, Maji S (2014) A critical review of principal traffic noise models: Strategies and implications. Environ Impact Assess Rev. https:\/\/doi.org\/10.1016\/j.eiar.2014.02.001","DOI":"10.1016\/j.eiar.2014.02.001"},{"key":"7221_CR19","doi-asserted-by":"publisher","first-page":"2529","DOI":"10.1016\/j.jenvman.2010.07.011","volume":"91","author":"S Givargis","year":"2010","unstructured":"Givargis S, Karimi H (2010) A basic neural traffic noise prediction model for Tehran\u2019s roads. J Environ Manage 91:2529\u20132534","journal-title":"J Environ Manage"},{"key":"7221_CR20","doi-asserted-by":"publisher","unstructured":"Guo M, Ni MY, Shyu RJ, Ji JS, Huang J (2023) Automated simulation for household road traffic noise exposure: Application and field evaluation in a high-density city. Comput Environ Urban Syst. https:\/\/doi.org\/10.1016\/j.compenvurbsys.2023.102000","DOI":"10.1016\/j.compenvurbsys.2023.102000"},{"key":"7221_CR21","doi-asserted-by":"publisher","first-page":"10","DOI":"10.26480\/gwk.02.2017.10.14","volume":"1","author":"H Halim","year":"2017","unstructured":"Halim H, Abdullah R, Mohd Nor MJ, Abdul Aziz H, Abd Rahman N (2017) Comparison between measured traffic noise in Klang Valley, Malaysia and existing prediction models. Eng Herit J 1:10\u201314","journal-title":"Eng Herit J"},{"key":"7221_CR22","first-page":"26","volume":"49","author":"WM Hameed","year":"2022","unstructured":"Hameed WM, Ali NA (2022) Comparison of seventeen missing value imputation techniques. J Hunan Univ Nat Sci 49:26\u201336","journal-title":"J Hunan Univ Nat Sci"},{"key":"7221_CR23","first-page":"145","volume":"77","author":"Z Haron","year":"2015","unstructured":"Haron Z, Han LM, Darus N, Lee YL, Jahya Z, Abdul Hamid MF, Yahya K, Shek PN (2015) A preliminary study of environmental noise in public university. Jurnal Teknol 77:145\u2013151","journal-title":"Jurnal Teknol"},{"key":"7221_CR24","doi-asserted-by":"publisher","unstructured":"Huang X, Liu J, Meng Z (2022) Application of University campus noise map based on noise propagation model: a case in Guangxi University. Sustainability. https:\/\/doi.org\/10.3390\/su14148613","DOI":"10.3390\/su14148613"},{"key":"7221_CR25","unstructured":"Iannone G, Guarnaccia C, Quartieri J (2011) Noise fundamental diagram deduced by traffic dynamics. Recent Res. Geogr. Geol. Energy, Environ. Biomed. - Proc. 4th WSEAS Int. Conf. EMESEG\u201911, 2nd Int. Conf. WORLD-GEO\u201911, 5th Int. Conf. EDEB\u201911 501\u2013507"},{"key":"7221_CR26","doi-asserted-by":"publisher","unstructured":"Ibili F, Owolabi AO, Ackaah W, Massaquoi AB (2022) Statistical modelling for urban roads traffic noise levels. Scientific African. https:\/\/doi.org\/10.1016\/j.sciaf.2022.e01131","DOI":"10.1016\/j.sciaf.2022.e01131"},{"key":"7221_CR27","doi-asserted-by":"publisher","first-page":"3887","DOI":"10.1007\/s00500-022-07592-w","volume":"27","author":"B Irmak","year":"2023","unstructured":"Irmak B, Karakoyun M, G\u00fclc\u00fc \u015e (2023) An improved butterfly optimization algorithm for training the feed-forward artificial neural networks. Soft Comput 27:3887\u20133905","journal-title":"Soft Comput"},{"key":"7221_CR28","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.apacoust.2016.04.003","volume":"111","author":"R Kalaiselvi","year":"2016","unstructured":"Kalaiselvi R, Ramachandraiah A (2016) Honking noise corrections for traffic noise prediction models in heterogeneous traffic conditions like India. Appl Acoust 111:25\u201338","journal-title":"Appl Acoust"},{"key":"7221_CR29","unstructured":"Indonesia KMI (2019) DEKKO SL 130 Sound Level Meter. URL https:\/\/www.karyamandiri-instrument.com\/product\/dekko-sl-130-sound-level-meter-3801097 (accessed 11.3.26)"},{"key":"7221_CR30","doi-asserted-by":"publisher","unstructured":"Khan D, Burdzik R (2023) Measurement and analysis of transport noise and vibration: A review of techniques, case studies, and future directions. Measurement. https:\/\/doi.org\/10.1016\/j.measurement.2023.113354","DOI":"10.1016\/j.measurement.2023.113354"},{"key":"7221_CR31","first-page":"1","volume":"8","author":"V Kumar","year":"2023","unstructured":"Kumar V, Ahirwarv AV, Prasad AD (2023) Monitoring and mapping noise levels of university campus in central part of India. J Air Pollut Heal 8:1\u201312","journal-title":"J Air Pollut Heal"},{"key":"7221_CR32","unstructured":"Lau A, Lee Y, Dawson B, Mackenzie N (2014) Noise modelling of road intersections. INTERNOISE 2014\u201343rd Int. Congr. Noise Control Eng. Improv. World Through Noise Control"},{"key":"7221_CR33","doi-asserted-by":"publisher","first-page":"39","DOI":"10.5539\/mas.v5n3p39","volume":"5","author":"DU Lawal","year":"2011","unstructured":"Lawal DU, Matori AN, Balogun AL (2011) A geographic information system and multi-criteria decision analysis in proposing new recreational park sites in Universiti Teknologi Malaysia. Mod Appl Sci 5:39\u201355","journal-title":"Mod Appl Sci"},{"key":"7221_CR34","doi-asserted-by":"publisher","unstructured":"Li Z (2022) Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost. Comput Environ Urban Syst. https:\/\/doi.org\/10.1016\/j.compenvurbsys.2022.101845","DOI":"10.1016\/j.compenvurbsys.2022.101845"},{"key":"7221_CR35","unstructured":"Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. Adv Neural Inf Process Syst 2017-Decem, 4766\u20134775"},{"key":"7221_CR36","doi-asserted-by":"publisher","unstructured":"Meller G, de Louren\u00e7o WM, de Melo VSG, de Campos Grigoletti G (2023) Use of noise prediction models for road noise mapping in locations that do not have a standardized model: a short systematic review. Environ Monit Assess. https:\/\/doi.org\/10.1007\/s10661-023-11268-9","DOI":"10.1007\/s10661-023-11268-9"},{"key":"7221_CR37","doi-asserted-by":"publisher","unstructured":"Mustapha IB, Abdulkareem M, Jassam TM, AlAteah AH, Al-Sodani KAA, Al-Tholaia MMH, Nabus H, Alih SC, Abdulkareem Z, Ganiyu A (2024) Comparative analysis of gradient-boosting ensembles for estimation of compressive strength of quaternary blend concrete. Int J Concr Struct Mater. https:\/\/doi.org\/10.1186\/s40069-023-00653-w","DOI":"10.1186\/s40069-023-00653-w"},{"key":"7221_CR38","doi-asserted-by":"publisher","first-page":"147","DOI":"10.11113\/ijbes.v6.n1-2.393","volume":"6","author":"PG Nejad","year":"2019","unstructured":"Nejad PG, Ahmad A, Zen IS (2019) Assessment of the interpolation techniques on traffic noise pollution mapping for the campus environment sustainability. Int J Built Environ Sustain 6:147\u2013159","journal-title":"Int J Built Environ Sustain"},{"key":"7221_CR39","doi-asserted-by":"publisher","unstructured":"Nourani V, G\u00f6k\u00e7eku\u015f H, Umar IK (2020) Artificial intelligence based ensemble model for prediction of vehicular traffic noise. Environ Res. https:\/\/doi.org\/10.1016\/j.envres.2019.108852","DOI":"10.1016\/j.envres.2019.108852"},{"key":"7221_CR40","doi-asserted-by":"publisher","DOI":"10.3390\/urbansci8010013","volume":"8","author":"E Othman","year":"2024","unstructured":"Othman E, Cibili\u0107 I, Poslon\u010dec-Petri\u0107 V, Saadallah D (2024) Investigating noise mapping in cities to associate noise levels with sources of noise using crowdsourcing applications. Urban Sci 8:13","journal-title":"Urban Sci"},{"key":"7221_CR41","doi-asserted-by":"publisher","unstructured":"Pan J, He Y, Ma W, An S, Li L, Huang D, Jia D (2025) Machine learning-enhanced 3D GIS urban noise mapping with multi-modal factors. ISPRS International Journal of Geo-Information. https:\/\/doi.org\/10.3390\/ijgi14060223","DOI":"10.3390\/ijgi14060223"},{"key":"7221_CR42","doi-asserted-by":"publisher","unstructured":"Pradhan B, Abdulkareem A, Aldulaimi A, Gite S, Alamri A, Mukhopadhyay SC (2024) Machine learning-based GIS model for 2D and 3D vehicular noise modelling in a data-scarce environment. International Journal on Smart Sensing and Intelligent Systems. https:\/\/doi.org\/10.2478\/ijssis-2024-0022","DOI":"10.2478\/ijssis-2024-0022"},{"key":"7221_CR43","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.trd.2016.05.007","volume":"47","author":"EE Qui\u00f1ones-Bola\u00f1os","year":"2016","unstructured":"Qui\u00f1ones-Bola\u00f1os EE, Bustillo-Lecompte CF, Mehrvar M (2016) A traffic noise model for road intersections in the city of Cartagena de Indias, Colombia. Transp Res Part D Transp Environ 47:149\u2013161","journal-title":"Transp Res Part D Transp Environ"},{"key":"7221_CR44","doi-asserted-by":"publisher","first-page":"257","DOI":"10.2495\/SDP-V3-N3-257-271","volume":"3","author":"HN Rajakumara","year":"2008","unstructured":"Rajakumara HN, Mahalinge Gowda RM (2008) Road traffic noise prediction models: a review. Int J Sustain Dev Plann 3:257\u2013271","journal-title":"Int J Sustain Dev Plann"},{"key":"7221_CR45","doi-asserted-by":"publisher","first-page":"51","DOI":"10.46300\/9104.2023.17.8","volume":"17","author":"D Rossi","year":"2023","unstructured":"Rossi D, Mascolo A, Guarnaccia C (2023) Road traffic noise predictions by means of L10 modelling with a multilinear regression calibrated on simulated data. Int J Mech 17:51\u201356","journal-title":"Int J Mech"},{"key":"7221_CR46","doi-asserted-by":"publisher","unstructured":"Salleh S, Ujang U, Azri S (2021) Virtual 3d campus for universiti Teknologi Malaysia (Utm). ISPRS International Journal of Geo-Information. https:\/\/doi.org\/10.3390\/ijgi10060356","DOI":"10.3390\/ijgi10060356"},{"key":"7221_CR47","doi-asserted-by":"crossref","unstructured":"Shapley LS (1953) A value for n-person games.","DOI":"10.1515\/9781400881970-018"},{"key":"7221_CR48","doi-asserted-by":"publisher","unstructured":"Tashakor S, Chamani A, Moshtaghie M (2023) Noise pollution prediction and seasonal comparison in urban parks using a coupled GIS- artificial neural network model. Environ Monit Assess. https:\/\/doi.org\/10.1007\/s10661-022-10858-3","DOI":"10.1007\/s10661-022-10858-3"},{"key":"7221_CR49","doi-asserted-by":"crossref","unstructured":"Taud H, Mas JF (2018) Multilayer Perceptron (MLP). Geomatic approaches for modeling land change scenarios. pp 451\u2013455","DOI":"10.1007\/978-3-319-60801-3_27"},{"key":"7221_CR50","volume":"33","author":"SK Tiwari","year":"2024","unstructured":"Tiwari SK, Kumaraswamidhas LA, Patel R, Garg N, Vallisree S (2024) Traffic noise measurement, mapping, and modeling using soft computing techniques for mid-sized smart Indian city. Measurement: Sensors 33:101203","journal-title":"Measurement: Sensors"},{"key":"7221_CR51","doi-asserted-by":"crossref","unstructured":"Tiwari SK, Kumaraswamidhas LA, Prince K, Rehman M (2023) A hybrid deep leaning model for prediction and parametric sensitivity analysis of noise annoyance. Environ. Sci. Pollut. Res 30:49666\u201349684","DOI":"10.1007\/s11356-023-25509-4"},{"key":"7221_CR52","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1016\/j.scitotenv.2014.08.060","volume":"505","author":"AJ Torija","year":"2015","unstructured":"Torija AJ, Ruiz DP (2015) A general procedure to generate models for urban environmental-noise pollution using feature selection and machine learning methods. Sci Total Environ 505:680\u2013693","journal-title":"Sci Total Environ"},{"key":"7221_CR53","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1016\/j.renene.2021.07.085","volume":"179","author":"P Trizoglou","year":"2021","unstructured":"Trizoglou P, Liu X, Lin Z (2021) Fault detection by an ensemble framework of Extreme Gradient Boosting (XGBoost) in the operation of offshore wind turbines. Renew Energy 179:945\u2013962","journal-title":"Renew Energy"},{"key":"#cr-split#-7221_CR54.1","unstructured":"Ujang MU, Dzulkefley NQ, Azri S, Salleh S (2022) Three-dimensional"},{"key":"#cr-split#-7221_CR54.2","doi-asserted-by":"crossref","unstructured":"(3D) noise pollution visualization via 3D city modelling. In: SpringerMU Ujang, NQ Dzulkefley, S Azri, S SallehApplication of Remote Sensing and GIS in Natural Resources and Built, 2023\u2022Springer. pp. 375-390","DOI":"10.1007\/978-3-031-14096-9_18"},{"key":"7221_CR55","doi-asserted-by":"publisher","unstructured":"Umar IK, Adamu M, Mostafa N, Riaz MS, Haruna SI, Hamza MF, Ahmed OS, Azab M (2024) The state-of-the-art in the application of artificial intelligence-based models for traffic noise prediction: a bibliographic overview. Cogent Engineering. https:\/\/doi.org\/10.1080\/23311916.2023.2297508","DOI":"10.1080\/23311916.2023.2297508"},{"key":"7221_CR56","doi-asserted-by":"publisher","unstructured":"Wickramathilaka N, Ujang U, Azri S, Choon TL (2023) Three-dimensional visualisation of traffic noise based on the Henk de-Klujijver model. Noise Mapping. https:\/\/doi.org\/10.1515\/noise-2022-0170","DOI":"10.1515\/noise-2022-0170"},{"key":"7221_CR57","doi-asserted-by":"publisher","unstructured":"Yang C, Chen M, Yuan Q (2021) The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis. Accid Anal Prev. https:\/\/doi.org\/10.1016\/j.aap.2021.106153","DOI":"10.1016\/j.aap.2021.106153"},{"key":"7221_CR58","doi-asserted-by":"publisher","first-page":"12860","DOI":"10.1021\/acs.est.0c01987","volume":"54","author":"X Yin","year":"2020","unstructured":"Yin X, Fallah-Shorshani M, McConnell R, Fruin S, Franklin M (2020) Predicting fine spatial scale traffic noise using mobile measurements and machine learning. Environ Sci Technol 54:12860\u201312869","journal-title":"Environ Sci Technol"},{"key":"7221_CR59","doi-asserted-by":"publisher","unstructured":"Yun KK, Yoon SW, Won D (2021) Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process. Expert Systems with Applications. https:\/\/doi.org\/10.1016\/j.eswa.2021.115716","DOI":"10.1016\/j.eswa.2021.115716"},{"key":"7221_CR60","unstructured":"Zamingard Rouzbahani A, Vafaeinejad AR (2020) Traffic Noise Mapping in Urban 3D Area by Using GIS and CORTN Model [WWW Document]. URL https:\/\/scholar.google.com\/scholar?hl=en&as_sdt=0%2C5&q=Traffic+Noise+Mapping+in+Urban+3D+Area+by+Using+GIS+and+CORTN+Model&btnG= (accessed 2.4.24)"},{"key":"7221_CR61","doi-asserted-by":"publisher","DOI":"10.3390\/app11062714","author":"X Zhang","year":"2021","unstructured":"Zhang X, Kuehnelt H, De Roeck W (2021) Traffic noise prediction applying multivariate bi-directional recurrent neural network. Appl Sci 11(6):2714.\u00a0https:\/\/doi.org\/10.3390\/app11062714","journal-title":"Applied Sciences"},{"key":"7221_CR62","doi-asserted-by":"publisher","unstructured":"Zhang Y, Zhao H, Li Y, Long Y, Liang W (2023) Predicting highly dynamic traffic noise using rotating mobile monitoring and machine learning method. Environ Res. https:\/\/doi.org\/10.1016\/j.envres.2023.115896","DOI":"10.1016\/j.envres.2023.115896"},{"key":"7221_CR63","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.apacoust.2017.06.025","volume":"127","author":"WJ Zhao","year":"2017","unstructured":"Zhao WJ, Liu EX, Poh HJ, Wang B, Gao SP, Png CE, Li KW, Chong SH (2017) 3D traffic noise mapping using unstructured surface mesh representation of buildings and roads. Appl Acoust 127:297\u2013304","journal-title":"Appl Acoust"},{"key":"7221_CR64","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1016\/j.jrmge.2021.06.012","volume":"13","author":"X Zhu","year":"2021","unstructured":"Zhu X, Chu J, Wang K, Wu S, Yan W, Chiam K (2021) Prediction of rockhead using a hybrid N-XGBoost machine learning framework. J Rock Mech Geotech Eng 13:1231\u20131245","journal-title":"J Rock Mech Geotech Eng"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-026-07221-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-026-07221-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-026-07221-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T10:46:50Z","timestamp":1779792410000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-026-07221-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":65,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["7221"],"URL":"https:\/\/doi.org\/10.1007\/s10489-026-07221-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"31 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"199"}}