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Research and applications of artificial neural networks in spatial analysis: Review

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The conducted review presents the possibility of using artificial neural networks in sectors related to environmental protection, agriculture, forestry, land uses, groundwater and bathymetric. Today there is a lot of research in these areas with different research methodologies. The result is the improvement of decision-making processes, design, and prediction of certain events that, with appropriate intervention, can prevent severe consequences for society. The review shows the capabilities to optimize and automate the processes of modeling urban and land dynamics. It examines the forecasts of assessment of the damage caused by natural phenomena. Detection of environmental changes via the analysis of certain time intervals and classification of objects on the basis of different images is presented. The practical aspects of this work include the ability to choose the correct artificial neural network model depending on the complexity of the problem. This factor is a novel element since previously reviewed articles did not encounter a study of the correlation between the chosen model or algorithm, depending on the use case or area of the problem. This article seeks to outline the reason for the interest in artificial intelligence. Its purpose is to find answers to the following questions: How can artificial neural networks be used for spatial analysis? What does the implementation of detailed algorithms depend on? It is proved that an artificial intelligence approach can be an effective and powerful tool in various domains where spatial aspects are important.
Rocznik
Strony
35--45
Opis fizyczny
Bibliogr. 53 poz., rys., tab.
Twórcy
  • Maritime University of Szczecin 1-2 Wały Chrobrego St., 70-500 Szczecin, Poland
Bibliografia
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  • 3. Bragagnolo, L., da Silva, R.V. & Grzybowski, J.M.V. (2020) Landslide susceptibility mapping with r.landslide: A free open-source GIS-integrated tool based on artificial neural networks. Environmental Modelling & Software 123, 104565, doi: 10.1016/j.envsoft.2019.104565.
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  • 6. Collin, A., Etienne, S. & Feunteun, E. (2017) VHR coastal bathymetry using WorldView-3: colour versus learner. Remote Sensing Letters 8(11), pp. 1072–1081, doi: 10.1080/2150704X.2017.1354261.
  • 7. da Silva, R.D., Galvão, L.S., dos Santos, J.R., de J. Silva, C.V. & de Moura, Y.M. (2014) Spectral/textural attributes from ALI/EO-1 for mapping primary and secondary tropical forests and studying the relationships with biophysical parameters. GIScience & Remote Sensing 51(6), pp. 677–694, doi: 10.1080/15481603.2014.972866.
  • 8. Elshazly, R.E., Armanuos, A.M., Zeidan, B.A. & Elshemy, M. (2021) Evaluating remote sensing approaches for mapping the bathymetry of Lake Manzala, Egypt. Euro-Mediterranean Journal for Environmental Integration 6, 77, doi: 10.1007/s41207-021-00285-0.
  • 9. Fischer, M.M. (2006) Spatial Analysis and GeoComputation. Berlin, Heidelberg: Springer.
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  • 17. Khairunniza-Bejo, S., Mustaffha, S. & Ismail, W.I.W. (2014) Application of artificial neural network in predicting crop yield: A review. Journal of Food Science and Engineering 4, 1–9.
  • 18. Kia, M.B., Pirasteh, S., Pradhan, B., Mahmus, A.R., Sulaiman, W.N.A. & Moradi, A. (2012) An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental Earth Sciences 67, pp. 251–264, doi: 10.1007/s12665-011-1504-z.
  • 19. Kogut, T. & Slowik, A. (2021) Classification of airborne laser bathymetry data using artificial neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, pp. 1959–1966, doi: 10.1109/ JSTARS.2021.3050799.
  • 20. Kogut, T., Tomczak, A., Słowik, A. & Oberski, T. (2022) Seabed modelling by means of airborne laser bathymetry data and imbalanced learning for offshore mapping. Sensors 22, 3121, doi: 10.3390/s22093121.
  • 21. Kolar, P., Benavidez, P. & Jamshidi, M. (2020) Survey of datafusion techniques for laser and vision based sensor integration for autonomous navigation. Sensors 20(8), 2180, doi: 10.3390/s20082180.
  • 22. Lee, S., Ryu, J.-H., Min, K. & Won, J.-S. (2003) Landslide susceptibility analysis using GIS and artificial neural network. Earth Surface Processes and Landforms 28(12), pp. 1361–1376, doi: 10.1002/esp.593.
  • 23. Lovedee-Turner, M. & Murphy, D. (2018) Application of machine learning for the spatial analysis of binaural room impulse responses. Applied Sciences 8(1), 105, doi: 10.3390/app8010105.
  • 24. Machiwal, D., Cloutier, V., Güler, C. & Kazakis, N. (2018) A review of GIS-integrated statistical techniques for groundwater quality evaluation and protection. Environmental Earth Sciences 77, 681, doi: 10.1007/s12665-018- 7872-x.
  • 25. Makboul, O., Negm, A., Mesbah, S. & Mohasseb, M. (2017) Performance assessment of ANN in estimating remotely sensed extracted bathymetry. Case study: Eastern Harbor of Alexandria. Procedia Engineering 181, pp. 912– 919, doi: 10.1016/j.proeng.2017.02.486.
  • 26. Mas, J.F. & Flores, J.J. (2008) The application of artificial neural networks to the analysis of remotely sensed data. International Journal of Remote Sensing 29(3), pp. 617–663, doi: 10.1080/01431160701352154.
  • 27. Mas, J.F., Puig, H., Palacio, J.L. & Sosa-López, A. (2004) Modelling deforestation using GIS and artificial neural networks. Environmental Modelling & Software 19(5), pp. 461–471, doi: 10.1016/S1364-8152(03)00161-0.
  • 28. Mollalo, A., Mao, L., Rashidi, P. & Glass, G.E. (2019) A GIS-based artificial neural network model for spatial distribution of tuberculosis across the continental United States. International Journal of Environmental Research and Public Health 16(1), 157, doi: 10.3390/ijerph16010157.
  • 29. Moses, S.A., Janaki, L., Joseph, S., Gomathi, J.P. & Joseph, J. (2013) Lake bathymetry from Indian Remote Sensing (P6-LISS III) satellite imagery using artificial neural network model. Lakes & Reservoirs: Research & Management 18(2), pp. 145–153, doi: 10.1111/lre.12027.
  • 30. Mustafa, H.M., Mustapha, A., Hayder, G. & Salisu, A. (2021) Applications of IoT and artificial intelligence in water quality monitoring and prediction: A review. 6th International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, pp. 968–975, doi: 10.1109/ ICICT50816.2021.9358675.
  • 31. Nagamani, P.V., Chauhan, P., Sanwlani, N. & Ali, M.M. (2012). Artificial neural network (ANN) based inversion of benthic substrate bottom type and bathymetry in optically shallow waters – Initial model results. Journal of The Indian Society of Remote Sensing 40, pp. 137–143, doi: 10.1007/ s12524-011-0142-y.
  • 32. Niedbała, G., Piekutowska, M., Weres, J., Korzeniewicz, R., Witaszek, K., Adamski, M., Pilarski, K., CzechowskaKosacka, A. & Krysztofiak-Kaniewska, A. (2019) Application of artificial neural networks for yield modeling of winter rapeseed based on combined quantitative and qualitative data. Agronomy 9(12), 781, doi: 10.3390/agronomy 9120781.
  • 33. Nikparvar, B. & Thill, J.-C.F. (2021) Machine learning of spatial data. ISPRS International Journal of Geo-Information 10(9), 600, doi: 10.3390/ijgi10090600.
  • 34. Niu, J., Tang, W., Xu, F., Zhou, X. & Song, Y. (2016) Global research on artificial intelligence from 1990–2014: Spatially-explicit bibliometric analysis. ISPRS International Journal of Geo-Information 5(5), 66, doi: 10.3390/ ijgi5050066.
  • 35. Osowski, S. (2013) Sieci neuronowe do przetwarzania informacji. Oficyna Wydawnicza Politechniki Warszawskiej.
  • 36. Pham, B.T., Bui, D.T., Prakash, I. & Dholakia, M.B. (2017) Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. CATENA 149, 1, pp. 52–63, doi: 10.1016/j.catena.2016.09.007.
  • 37. Pijanowski, B.C., Brown, D.G., Shellito, B.A. & Manik, G.A. (2002) Using neural networks and GIS to forecast land use changes: a land transformation model. Computers, Environment and Urban Systems 26(6), pp. 553–575, doi: 10.1016/S0198-9715(01)00015-1.
  • 38. Pradhan, B., Lee, S. & Buchroithner, M.F. (2010) AGISbased back-propagation neural network model and its crossapplication and validation for landslide susceptibility analyses. Computers, Environment and Urban Systems 34(3), pp. 216–235, doi: 10.1016/j.compenvurbsys.2009.12.004.
  • 39. Rajaee, T., Khani, S. & Ravansalar, M. (2020) Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: A review. Chemometrics and Intelligent Laboratory Systems 200, 103978, doi: 10.1016/j. chemolab.2020.103978.
  • 40. Reades, J., De Souza, J. & Hubbard, P. (2019) Understanding urban gentrification through machine learning. Urban Studies 56(5), pp. 922–942, doi: 10.1177/0042098018789054.
  • 41. Rizeei, H.M., Pradhan, B., Saharkhiz, M.A. & Lee, S. (2019) Groundwater aquifer potential modeling using an ensemble multi-adoptive boosting logistic regression technique. Journal of Hydrology 579, 124172, doi: 10.1016/j.jhydrol.2019. 124172.
  • 42. Rumbayan, M., Abudureyimu, A. & Nagasaka, K. (2012) Mapping of solar energy potential in Indonesia using artificial neural network and geographical information system. Renewable and Sustainable Energy Reviews 16(3), pp. 1437–1449, doi: 10.1016/j.rser.2011.11.024.
  • 43. Saha, S., Paul, G.C., Pradhan, B., Maulud, K.N.A. & Alamri, A.M. (2021) Integrating multilayer perceptron neural nets with hybrid ensemble classifiers for deforestation probability assessment in Eastern India. Geomatics, Natural Hazards and Risk 12(1), doi: 10.1080/19475705.2020.1860139.
  • 44. Sahoo, B. & Bhaskaran, P.K. (2019) Prediction of storm surge and coastal inundation using artificial neural network – A case study for 1999 Odisha Super Cyclone. Weather and Climate Extremes 23, 100196, doi: 10.1016/j. wace.2019.100196.
  • 45. Samaras, S., Diamantidou, E., Ataloglou, D., Sakellariou, N., Vafeiadis, A., Magoulianitis, V., Lalas, A., Dimou, A., Zarpalas, D., Votis, K., Daras, P. & Tzovaras, D. (2019) Deep learning on multi-sensor data for counter UAV applications – A systematic review. Sensors 19(22), 4837, doi: 10.3390/s19224837.
  • 46. Sun, Z., Zhou, W., Ding, C. & Xia, M. (2022) Multi-resolution transformer network for building and road segmentation of remote sensing image. ISPRS International Journal of Geo-Information 11(3), 165, doi: 10.3390/ijgi11030165.
  • 47. Tamiru, H. & Dinka, M.O. (2021) Artificial intelligence in geospatial analysis for flood vulnerability assessment: A case of dire Dawa Watershed, Awash Basin, Ethiopia. The Scientific World Journal 2021, 6128609, doi: 10.1155/2021/6128609.
  • 48. Wang, J., Gao, H. & Xin, J. (2010) Application of artificial neural networks and GIS in urban earthquake disaster mitigation. ICICTA’10: Proceedings of the International Conference on Intelligent Computation Technology and Automation 01, pp. 726–729, doi: 10.1109/ICICTA.2010.409.
  • 49. Włodarczyk-Sielicka, M. & Lubczonek, J. (2019) The use of an artificial neural network to process hydrographic big data during surface modeling. Computers 8(1), 26, doi: 10.3390/computers8010026.
  • 50. Wu, N. & Silva, E. (2010) Artificial intelligence solutions for urban land dynamics: A review. Journal of Planning Literature 24(3), pp. 246–265, doi: 10.1177/0885412210361571.
  • 51. Yilmaz, I. (2009) A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks. Bulletin of Engineering Geology and the Environment 68, pp. 297–306, doi: 10.1007/s10064-009-0185-2.
  • 52. Zheng, K., Li, J., Ding, L., Yang, J., Zhang, X. & Zhang, X. (2021) Cloud and snow segmentation in satellite images using an encoder-decoder deep convolutional neural networks. ISPRS International Journal of Geo-Information 10(7), 462, doi: 10.3390/ijgi10070462.
  • 53. Zhu, L., Huang, L., Fan, L., Huang, J., Huang, F., Chen, J., Zhang, Z. & Wang, Y. (2020) Landslide susceptibility prediction modeling based on remote sensing and a novel deep learning algorithm of a cascade-parallel recurrent neural network. Sensors 20(6), 1576, doi: 10.3390/s20061576.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-03e64592-c9ea-4259-abce-8721afdc0770
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