Modelagem da propensão à antropização no Morro São Pedro (Porto Alegre/RS) com Aprendizado de Máquina: Uma abordagem híbrida baseada em árvores
DOI:
https://doi.org/10.22456/1807-9806.146128Palabras clave:
planejamento territorial, ecossistemas, recursos naturaisResumen
O mapeamento da antropização é essencial para compreender as mudanças ambientais e facilitar o planejamento territorial. Nesta pesquisa, o problema central foi analisar os impactos da atividade humana no Morro São Pedro, em Porto Alegre. O objetivo principal foi identificar como a paisagem local foi alterada ao longo do tempo. Para tal, foram utilizados três algoritmos de aprendizagem automática: Parallel Random Forest (PRF), Regularized Random Forest (RRF) e XGBoost. Os resultados indicaram que o modelo PRF foi o mais preciso entre os testados. Essa descoberta sugere que a presença de rodovias é um fator primário que influencia as mudanças no ambiente natural. A proximidade dessas estradas destacou-se como o fator mais significativo em todos os modelos, sugerindo que a construção e o uso de rodovias desempenham um papel fundamental na transformação da região. Em conclusão, os resultados deste estudo destacam a importância de incorporar os impactos das rodovias no planejamento territorial e ambiental, particularmente no que diz respeito à conservação dos ecossistemas e à gestão dos recursos naturais.
Descargas
Citas
Bag, R., Mondal, I., Dehbozorgi, M., Bank, S.P., Das, D.N., Bandyopadhyay, J., Pham, Q.B., Al-Quraishi, A.M.F. & Nguyen, X.C. 2022. Modelling and mapping of soil erosion susceptibility using machine learning in a tropical hot sub-humid environment. Journal of Cleaner Production, 364: 132428. https://doi.org/10.1016/j.jclepro.2022.132428
Band, S.S., Janizadeh, S.,Pal, S.C., Saha, A., Chakrabortty, R., Melesse, A.M. & Mosavi, A. 2020. Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms. Remote Sensing, 12(21): 3568. https://doi.org/10.3390/rs12213568
Bertrand, G. 1984. Les géographes français et leurs paysages. Annales de Géographie, 93(516): 218-229.
Budholiya, K., Shrivastava, S.K. & Sharma, V. 2022. An optimized XGBoost based diagnostic system for effective prediction of heart disease. Journal of King Saud University - Computer and Information Sciences, 34(7): 4514-4523. https://doi.org/10.1016/j.jksuci.2020.10.013
Cao, J., Zhang, Z., Du, J., Zhang, L., Song, Y. & Sun, G. 2020. Multi-geohazards susceptibility mapping based on machine learning - a case study in Jiuzhaigou, China. Natural Hazards, 102(3): 851-871. https://doi.org/10.1007/s11069-020-03927-8
Chen, T. & Guestrin, C. 2016. XGBoost: A Scalable Tree Boosting System. In: THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE, 2016. Anais... New York, ACM, p. 785-794.
Christofidis, D., Assumpção, R.S.F.V. & Kligerman, D.C. 2019. A evolução histórica da drenagem urbana: da drenagem tradicional à sintonia com a natureza. Saúde Debate, 43(3): 94-108. https://doi.org/10.1590/0103-11042019S307
Christofoletti, A. 1999. Modelagem de sistemas ambientais. São Paulo: Edgard Blucher, 256p.
Coelho, M.C.N. 2001. Impactos ambientais em áreas urbanas – teorias, conceitos e métodos de pesquisa. In: Guerra, A.J.T.; Cunha, S.B. (org.). Impactos ambientais urbanos no Brasil. Rio de Janeiro: Bertrand Brasil, p. 19-45.
Darabi, H., Rahmati, O., Naghibi, S.A., Mohammadi, F., Ahmadisharaf, E., Kalantari, Z. & Bui, D.T. 2021. Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood. Geocarto International, 37(19): 5716-5741. https://doi.org/10.1080/10106049.2021.1920629
Deng, H. & Runger, G. 2012. Feature selection via regularized trees. In: INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, 2012, Brisbane, Austrália. Anais... New York, IEEE.
Deng, H. & Runger, G. 2013. Gene selection with guided regularized random forest. Pattern Recognition, 46(12): 3483-3489. https://doi.org/10.1016/j.patcog.2013.05.018
Dormann, C.F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J.R.G., Gruber, B., Lafourcade, B., Leitão, P.J., Münkemüller, T., McClean, C., Osborne, P.E., Reineking, B., Schröder, B., Skidmore, A.K., Zurell, D. & Lautenbach, S. 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1): 27-46. https://doi.org/10.1111/j.1600-0587.2012.07348.x
Eilola, S., Jaalama, K., Kangassalo, P., Nummi, P., Staffans, A. & Fagerholm, N. 2023. 3D Visualisations for Communicative Urban and Landscape Planning: What Systematic Mapping of Academic Literature Can Tell Us of Their Potential? Landscape Urban Planning, 234: 104716. https://doi.org/10.1016/j.landurbplan.2023.104716
Elbeltagi, A., Pande, C.B., Kouadri, S. & Islam, A.R.M.T. 2022. Applications of various data-driven models for the prediction of groundwater quality index in the Akot basin, Maharashtra, India. Environmental Science and Pollution Research, 29: 17591–17605. https://doi.org/10.1007/s11356-021-17064-7
Fenta, A.A., Tsunekawa, A., Haregeweyn, N., Poesen, J., Tsubo, M., Borrelli, P., Panagos, P., Vanmaercke, M., Broeckx, J., Yasuda, H., Kawai, T. & Kurosaki, Y. 2020. Land susceptibility to water and wind erosion risks in the East Africa region. Science of the Total Environment, 703: 135016. https://doi.org/10.1016/j.scitotenv.2019.135016
García-Ayllón, S. 2017. Diagnosis of Complex Coastal Ecological Systems: Environmental GIS Analysis of a Highly Stressed Mediterranean Lagoon through Spatiotemporal Indicators. Ecological Indicators, 83: 451-462. https://doi.org/10.1016/j.ecolind.2017.08.015
García-Ayllón, S. 2018. Predictive Diagnosis of Agricultural Periurban Areas Based on Territorial Indicators: Comparative Landscape Trends of the So-Called “Orchard of Europe”. Sustainability, 10(6): 1820. https://doi.org/10.3390/su10061820
Goerl, R.F., Michel, G.P. & Kobiyama, M. 2017. Mapeamento de áreas suscetíveis à inundação com o modelo HAND e análise do seu desempenho em diferentes resoluções espaciais. Revista Brasileira de Cartografia, 69(1): 61-69. https://doi.org/10.14393/rbcv69n1-44032
Hasenack, H. et al. 2008. Diagnóstico ambiental de porto alegre: geologia, solos, drenagem, vegetação/ocupação e paisagem. Porto Alegre, Secretaria Municipal do Meio Ambiente.
Hosseiny, H., Nazari, F., Smith, V. & Nataraj, C. 2020. A framework for modeling flood depth using a hybrid of hydraulics and machine learning. Scientific Reports, 10: 8222. https://doi.org/10.1038/s41598-020-65232-5
IBGE. Instituto Brasileiro de Geografia e Estatística. 2010. Censo 2010 mostra as características territoriais dos aglomerados subnormais e suas diferenças das demais áreas das cidades. Disponível em: https://agenciadenoticias.ibge.gov.br/agencia-sala-de-imprensa/2013-agencia-de-noticias/releases/14504-asi-censo-2010-mostra-as-caracteristicas-territoriais-dos-aglomerados-subnormais-e-suas-diferencas-das-demais-areas-das-cidades. Acesso em: 15 set. 2024.
IBGE. Instituto Brasileiro de Geografia e Estatística. 2022. Censo 2022. Disponível em: https://basedosdados.org/dataset/08a1546e-251f-4546-9fe0-b1e6ab2b203d?table=c5c43530-0c76-4226-b67c-9e0d73b66d12. Acesso em: 12 set. 2024.
Instituto Nacional de Pesquisas Espaciais (INPE). 2024. Projeto de Monitoramento do Desmatamento na Amazônia Legal por Satélite (PRODES). São José dos Campos, SP: INPE. Disponível em: http://www.obt.inpe.br/prodes/. Acesso em: 15 jan. 2025.
Janizadeh, S., Vafakhah, M., Kapelan, Z. & Mobarghaee Dinan, N. 2022. Hybrid XGBoost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling. Geocarto International, 37(25): 8273-8292. https://doi.org/10.1080/10106049.2021.1996641
Jasiewicz, J. & Stepinski, T. F. 2013. Geomorphons a Pattern Recognition Approach to Classification and Mapping of Landforms. Geomorphology, 182: 147-156. https://doi.org/10.1016/j.geomorph.2012.11.005
Ji, M. et al. 2019. An improved gradient boosting decision tree algorithm. In: CHINESE AUTOMATION CONGRESS (CAC), 2019, Hangzhou. Anais... IEEE, p. 446-451.
Kadavi, P.R., Lee, C-W. & Lee, S. 2018. Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping. Remote Sensing, 10(8): 1252. https://doi.org/10.3390/rs10081252
Kang, L., Jiang, S., Hu, X. & Li, C. 2019. Evaluation of return period and risk in bivariate non-stationary flood frequency analysis. Water, 11(1): 79. https://doi.org/10.3390/w11010079
Linh, N.T.T., Pandey, M., Janizadeh, S., Bhunia, G.S., Norouzi, A., Ali, S., Pham, Q.B., Anh, D.T., & Ahmadi, K. 2022. Flood susceptibility modeling based on new hybrid intelligence model: optimization of XGBoost model using GA metaheuristic algorithm. Advances in Space Research, 69(9): 3301-3318. https://doi.org/10.1016/j.asr.2022.02.027
Mohammed, I., Marshall, M., Bie, K., Estes, L., & Nelson, A. 2020. A blended census and multiscale remote sensing approach to probabilistic cropland mapping in complex landscapes. ISPRS Journal of Photogrammetry and Remote Sensing, 161: 233-245. https://doi.org/10.1016/j.isprsjprs.2020.01.024
Moroz-Caccia Gouveia, I.C. & Ross, J.L.S. 2019. Fragilidade Ambiental: uma proposta de aplicação de Geomorphons para a variável relevo. Revista do Departamento de Geografia, 37: 123-136. https://doi.org/10.11606/rdg.v37i0.151030
Motevalli, A., Naghibi, A. S., Hashemi, H., Berndtsson, R., Pradhan, B. & Gholami, V. 2019. Inverse method using boosted regression tree and k-nearest neighbor to quantify effects of point and non-point source nitrate pollution in groundwater. Journal of Cleaner Production, 228: 1248-1263. https://doi.org/10.1016/j.jclepro.2019.04.293
Moura, N., Hasenack, H. & Silva, L. 2013. Mapa geomorfológico dos municípios de Porto Alegre, Viamão e Alvorada-RS. Porto Alegre: UFRGS-IB-Centro de Ecologia, 1 v.
Mousavi, S.M., Golkarian, A., Amir Naghibi, S., Kalantar, B. & Pradhan, B. 2017. GIS-based groundwater spring potential mapping using data mining boosted regression tree and probabilistic frequency ratio models in Iran. AIMS Geosciences, 3(1): 91-115. https://doi.org/10.3934/geosci.2017.1.91
Nhu, V.H., Mohammadi, A., Shahabi, H., Ahmad, B.B., Al-Ansari, N., Shirzadi, A., Clague, J.J., Jaafari, A., Chen, W. & Nguyen, H. 2020. Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment. International Journal of Environmental Research and Public Health, 17(14):4933. https://doi.org/10.3390/ijerph17144933
Nsengiyumva, J.B. & Valentino, R. 2020. Predicting landslide susceptibility and risks using GIS-based machine learning simulations, case of upper Nyabarongo catchment. Geomatics, Natural Hazards and Risk, 11(1): 1250-1277. https://doi.org/10.1080/19475705.2020.1785555
Palmer, J. F. 2019. The Contribution of a GIS-Based Landscape Assessment Model to a Scientifically Rigorous Approach to Visual Impact Assessment. Landscape and Urban Planning, 189: 80-90. https://doi.org/10.1016/j.landurbplan.2019.03.005
Phan, A., Ha, D.N., Man, C.D., Nguyen, T.T., Bui, H.Q. & Nguyen, T.T.N. 2019. Rapid assessment of flood inundation and damaged rice area in Red River Delta from Sentinel 1A imagery. Remote Sensing, 11(17): 2034. https://doi.org/10.3390/rs11172034
Prasad, M., Dubey, C.P., Joshi, K.B., & Tiwari, V.M. 2021. Crustal density and susceptibility structure beneath Achankovil shear zone, India. Lithosphere, n. Special (6): 6017801. https://doi.org/10.2113/2021/6017801
Rahmati, O., Tahmasebipour, N., Haghizadeh, A., Pourghasemi, H.R. & Feizizadeh, B. 2017. Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion. Geomorphology, (298): 118-137. https://doi.org/10.1016/j.geomorph.2017.09.006
Rambo, B. 2000. A Fisionomia do Rio Grande do Sul: Ensaio de Monografia. São Leopoldo, Editora UNISINOS.
Rennó, C., Nobre, A.D., Cuartas, L. A., Soares, J.V., Hodnett, M.G. & Tomasella, J. 2008. HAND, a new terrain descriptor using SRTM-DEM: Mapping terra-firme rainforest environments in Amazonia. Remote Sensing of Environment, 112(9): 3469-3481. https://doi.org/10.1016/j.rse.2008.03.018
Saha, S., Roy, J., Pradhan, B. & Hembram, T.K. 2021. Hybrid ensemble machine learning approaches for landslide susceptibility mapping using different sampling ratios at East Sikkim Himalayan, India. Advances in Space Research, 68(7): 2819-2840. https://doi.org/10.1016/j.asr.2021.05.018
Sakaa, B., Elbeltagi, A., Boudibi, S., Chaffaï, H., Islam, A.B.M.T., Kulimushi, L.C., Choudhari, P., Hani, A., Brouziyne, Y. & Wong Y.J. 2022. Water quality index modeling using random forest and improved SMO algorithm for support vector machine in Saf-Saf river basin. Environmental Science and Pollution Research, 29(32): 48491-48508. https://doi.org/10.1007/s11356-022-18644-x
Shahzad, N., Ding, X., & Abbas, S. 2022. A comparative assessment of machine learning models for landslide susceptibility mapping in the rugged terrain of northern Pakistan. Applied Sciences, 12(5): 2280. https://doi.org/10.3390/app12052280
Shuster, W.D., Bonta, J., Thurston, H., Warnemuende, E., & Smith, D.R. 2005. Impacts of impervious surface on watershed hydrology: A review. Urban Water Journal, 2(4): 263-275. https://doi.org/10.1080/15730620500386529
Silva, L.F. & Bacani, V.M. 2017. Análise da fragilidade ambiental e das áreas de preservação permanente da bacia hidrográfica do córrego fundo, município de Aquidauana-MS. Caderno de Geografia, 27(50): 119-137. https://doi.org/10.5752/p.2318-2962.2017v27n49p264
SMAMUS. Secretaria Municipal do Meio Ambiente, Urbanismo e Sustentabilidade da Prefeitura de Porto Alegre. 2022. Mapas Digitais da SMAMUS. Disponível em: https://prefeitura.poa.br/carta-de-servicos/mapasdigitais-da-smamus. Acesso em: 10 jun. 2025.
Sotchava, V.B. 1977. O Estudo de Geossistemas. São Paulo, 16p. Métodos em Questão, 16. Instituto de Geografia, Universidade de São Paulo.
Souza, A.C. & Vieira, R.S. 2018. Mapeamento da vulnerabilidade ambiental por antropização em uma bacia hidrográfica do Semiárido brasileiro. Revista Brasileira de Geografia Física, 11(4): 1198-1215.
Tehrany, M.S., Shabani, F. & Jebur, M.N. 2017. GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques. Geomatics, Natural Hazards and Risk, 8(2): 1538-1561. https://doi.org/10.1080/19475705.2017.1362038
Tuv, E., Borisov, A., Runger, G. & Torkkola, K. 2009. Feature selection with ensembles, artificial variables, and redundancy elimination. Journal of Machine Learning Research, 10(45): 1341-1366.
Wang, Z., Lai, C., Chen, X., Yang, B., Zhao, S. & Bai, X., 2015. Flood hazard risk assessment model based on random forest. Journal of Hydrology, 527: 1130-1141. https://doi.org/10.1016/j.jhydrol.2015.06.008
Whang, S.E. & Lee, Jae-Gil. 2020. Data collection and quality challenges for deep learning. Proceedings of the VLDB Endowment, 13(12): 3429-3432. https://doi.org/10.14778/3415478.3415562
Wilcoxon, F. 1945. Some uses of statistics in plant pathology. Biometrics Bulletin, 1(4): 41-45. https://doi.org/10.2307/3002011
Xie, W., Li, X., Jian, W., Yang, Y., Liu, H., Robledo, L.F. & Nie, W. 2021. A Novel Hybrid Method for Landslide Susceptibility Mapping-Based GeoDetector and Machine Learning Cluster: A Case of Xiaojin County, China. ISPRS International Journal of Geo-Information, 10(2): 93. https://doi.org/10.3390/ijgi10020093
Yang, H., Leng, Q., Xiao, Y. & Chen, W. 2022. Investigating the Impact of Urban Landscape Composition and Configuration on PM2.5 Concentration under the LCZ Scheme: A Case Study in Nanchang, China. Sustainable Cities and Society, 84: 104006. https://doi.org/10.1016/j.scs.2022.104006
Zhang, G., Wang, M. & Liu, K. 2019. Forest Fire Susceptibility Modeling Using a Convolutional Neural Network for Yunnan Province of China. International Journal of Disaster Risk Science, 10: 386-403. https://doi.org/10.1007/s13753-019-00233-1
Zhang, X., Wang, X., Zhang, C. & Zhai, J. 2022. Development of a Cross-Scale Landscape Infrastructure Network Guided by the New Jiangnan Watertown Urbanism: A Case Study of the Ecological Green Integration Demonstration Zone in the Yangtze River Delta, China. Ecological Indicators, 143: 109317. https://doi.org/10.1016/j.ecolind.2022.109317
Zhu, L. & Huang, J. 2006. GIS-based logistic regression method for landslide susceptibility mapping in regional scale. Journal of Zhejiang University-SCIENCE A, 7(12): 2007-2017. https://doi.org/10.1631/jzus.2006.A2007
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
