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Groundwater for Sustainable Development 2021, 12, 100536. Multi-temporal analysis of land use land cover interference in environmental fragility in a Mesozoic basin, southeastern Brazil.
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Erica Zanardo Oliveira-Andreoli, Mayra Cristina Prado de Moraes, Alexandre da Silva Faustino, Anaí Floriano Vasconcelos, Carlos Wilmer Costa, Luiz Eduardo Moschini, Edson Augusto Melanda, Eliane Aparecida Justino, José Augusto Di Lollo, Reinaldo Lorandi.Integrating random forests and propagation models for high-resolution noise mapping. Brook, Hugh Davies, Sophie Goudreau, Audrey Smargiassi. Ying Liu, Tor Oiamo, Daniel Rainham, Hong Chen, Marianne Hatzopoulou, Jeffrey R.Longitudinal and Life Course Studies 2021, 12 The Prospective Epidemiological Research Studies in IrAN (PERSIAN) Birth Cohort protocol: rationale, design and methodology. Mohammad Javad Zare Sakhvidi, Navid Danaei, Payam Dadvand, Amir Houshang Mehrparvar, Motahar Heidari-Beni, Shamsollah Nouripour, Habib Nikukar, Seyede Shahrbanoo Daniali, Elham Saffarieh, Mahmood Noorishadkam, Mohammad Mehdi Amin, Majid Mirmohammadkhani, Mohammad Hassan Lotfi, Ahmad Vaez, Seyyed Jalil Mirmohammadi, Elaheh Zarean, Mahdieh Mojibian, Mahin Hashemipour, Omid Yaghini, Mohammad Sadegh Rezai, Ali Esmaeili, Alireza Fahimzad, Hamid Hakimi, Mohammad Reza Navaeifar, Hamid Ostad Ebrahimi, Hossein Poustchi, Reza Malekzadeh, Roya Kelishadi.Modelling the spatial pattern of heatwaves in the city of Bern using a land use regression approach. Moritz Burger, Moritz Gubler, Andreas Heinimann, Stefan Brönnimann.Journal of Exposure Science & Environmental Epidemiology 2021, 356 Predicting traffic noise using land-use regression-a scalable approach. Jeroen Staab, Arthur Schady, Matthias Weigand, Tobia Lakes, Hannes Taubenböck.Traffic Noise Modelling Using Land Use Regression Model Based on Machine Learning, Statistical Regression and GIS. Ahmed Abdulkareem Ahmed Adulaimi, Biswajeet Pradhan, Subrata Chakraborty, Abdullah Alamri.Land use regression modelling of community noise in São Paulo, Brazil. Michelle Raess, Alexandra Brentani, Bartolomeu Ledebur de Antas de Campos, Benjamin Flückiger, Kees de Hoogh, Günther Fink, Martin Röösli.Developing land use regression models for environmental science research using the XLUR tool – More than a one-trick pony. Validating two geospatial models of continental-scale environmental sound levels. Spatial and sociodemographic determinants of community loudness perception. Environmental Science & Technology 2012, 46 Modeling Environmental Impacts of Urban Expansion: A Systematic Method for Dealing with Uncertainties. Environmental Science & Technology 2020, 54 Predicting Fine Spatial Scale Traffic Noise Using Mobile Measurements and Machine Learning. Xiaozhe Yin, Masoud Fallah-Shorshani, Rob McConnell, Scott Fruin, Meredith Franklin.This article is cited by 47 publications. As the LUNOS facilitates a better understanding of the association between land use and urban environmental noise in comparison to conventional methods, it can be regarded as a promising tool for noise prediction for planning purposes and aid smart decision-making. The results suggest that a linear model performs better in fitting monitoring data, and there is no significant difference of the LUNOS’s outputs when applied to different spatial scales. The LUNOS model describes noise as a dependent variable of surrounding various land areas via a regressive function. This paper, for the first time, introduces a land use regression method, which has been applied for simulating urban air quality for a decade, to construct an urban noise model (LUNOS) in Dalian Municipality, Northwest China. Therefore, these conventional methods can hardly forecast urban noise at a given outlook of development layout. Most existing efforts, including experiment-based models, statistical models, and noise mapping, however, have limited capacity to explain the association between urban growth and corresponding noise change. Forecasting and preventing urban noise pollution are major challenges in urban environmental management.