Classification of pluviometric networks located in the region of Bogotá, Colombia using artificial neural networks
This work presents a methodology for the classification of pluviometric networks using artificial neural networks. For this, the network of stations registered in the Corporación Autónoma Regional de Cundinamarca, Colombia, was analyzed. The network studied consists of 182 stations for the measureme...
- Autores:
-
Garrido Arévalo, Augusto Rafael
Agudelo, L M
Obregon, N
Garrido Arévalo, Víctor Manuel
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9386
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9386
https://iopscience.iop.org/article/10.1088/1742-6596/1448/1/012008
- Palabra clave:
- Stream Flow
Flood Forecasting
Water Tables
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc/4.0/
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Classification of pluviometric networks located in the region of Bogotá, Colombia using artificial neural networks |
| title |
Classification of pluviometric networks located in the region of Bogotá, Colombia using artificial neural networks |
| spellingShingle |
Classification of pluviometric networks located in the region of Bogotá, Colombia using artificial neural networks Stream Flow Flood Forecasting Water Tables |
| title_short |
Classification of pluviometric networks located in the region of Bogotá, Colombia using artificial neural networks |
| title_full |
Classification of pluviometric networks located in the region of Bogotá, Colombia using artificial neural networks |
| title_fullStr |
Classification of pluviometric networks located in the region of Bogotá, Colombia using artificial neural networks |
| title_full_unstemmed |
Classification of pluviometric networks located in the region of Bogotá, Colombia using artificial neural networks |
| title_sort |
Classification of pluviometric networks located in the region of Bogotá, Colombia using artificial neural networks |
| dc.creator.fl_str_mv |
Garrido Arévalo, Augusto Rafael Agudelo, L M Obregon, N Garrido Arévalo, Víctor Manuel |
| dc.contributor.author.none.fl_str_mv |
Garrido Arévalo, Augusto Rafael Agudelo, L M Obregon, N Garrido Arévalo, Víctor Manuel |
| dc.subject.keywords.spa.fl_str_mv |
Stream Flow Flood Forecasting Water Tables |
| topic |
Stream Flow Flood Forecasting Water Tables |
| description |
This work presents a methodology for the classification of pluviometric networks using artificial neural networks. For this, the network of stations registered in the Corporación Autónoma Regional de Cundinamarca, Colombia, was analyzed. The network studied consists of 182 stations for the measurement of precipitation and it has a historical series that goes, in some cases, from 1931 to the present. For the classification, three scenarios called types were proposed, in which the number of neurons in the output layer was varied. It was significant that when comparing the results of the different types, the permanence of certain features in the classification was found, indicating the validity of the classification. |
| publishDate |
2020 |
| dc.date.accessioned.none.fl_str_mv |
2020-09-10T21:24:45Z |
| dc.date.available.none.fl_str_mv |
2020-09-10T21:24:45Z |
| dc.date.issued.none.fl_str_mv |
2020 |
| dc.date.submitted.none.fl_str_mv |
2020-09-09 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_8544 |
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info:eu-repo/semantics/publishedVersion |
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Otro |
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publishedVersion |
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Garrido-Arévalo, A. R., Agudelo, L. M., Obregon, N., & Garrido, V. M. (2020). Classification of pluviometric networks located in the region of bogotá, colombia using artificial neural networks. Paper presented at the Journal of Physics: Conference Series, , 1448(1) doi:10.1088/1742-6596/1448/1/0120088 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/9386 |
| dc.identifier.url.none.fl_str_mv |
https://iopscience.iop.org/article/10.1088/1742-6596/1448/1/012008 |
| dc.identifier.doi.none.fl_str_mv |
10.1088/1742-6596/1448/1/012008 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Garrido-Arévalo, A. R., Agudelo, L. M., Obregon, N., & Garrido, V. M. (2020). Classification of pluviometric networks located in the region of bogotá, colombia using artificial neural networks. Paper presented at the Journal of Physics: Conference Series, , 1448(1) doi:10.1088/1742-6596/1448/1/0120088 10.1088/1742-6596/1448/1/012008 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/9386 https://iopscience.iop.org/article/10.1088/1742-6596/1448/1/012008 |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
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http://purl.org/coar/access_right/c_abf2 |
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http://creativecommons.org/licenses/by-nc/4.0/ |
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info:eu-repo/semantics/openAccess |
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Atribución-NoComercial 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc/4.0/ Atribución-NoComercial 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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7 páginas |
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application/pdf |
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Bogotá |
| dc.publisher.place.spa.fl_str_mv |
Cartagena de Indias |
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Journal of Physics: Conference Series 1448 (2020) 012008 |
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Universidad Tecnológica de Bolívar |
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Garrido Arévalo, Augusto Rafaelb441a02c-d3dc-4213-a2e7-8fed65792790Agudelo, L Mde109e28-f563-475d-820c-04ac71e8348dObregon, Nda60858b-7373-46ea-99e2-bbf32eb11bfbGarrido Arévalo, Víctor Manuel5c72390f-bbbf-414d-bd59-09c2e872bf1dBogotá2020-09-10T21:24:45Z2020-09-10T21:24:45Z20202020-09-09Garrido-Arévalo, A. R., Agudelo, L. M., Obregon, N., & Garrido, V. M. (2020). Classification of pluviometric networks located in the region of bogotá, colombia using artificial neural networks. Paper presented at the Journal of Physics: Conference Series, , 1448(1) doi:10.1088/1742-6596/1448/1/0120088https://hdl.handle.net/20.500.12585/9386https://iopscience.iop.org/article/10.1088/1742-6596/1448/1/01200810.1088/1742-6596/1448/1/012008Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThis work presents a methodology for the classification of pluviometric networks using artificial neural networks. For this, the network of stations registered in the Corporación Autónoma Regional de Cundinamarca, Colombia, was analyzed. The network studied consists of 182 stations for the measurement of precipitation and it has a historical series that goes, in some cases, from 1931 to the present. For the classification, three scenarios called types were proposed, in which the number of neurons in the output layer was varied. It was significant that when comparing the results of the different types, the permanence of certain features in the classification was found, indicating the validity of the classification.7 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Journal of Physics: Conference Series 1448 (2020) 012008Classification of pluviometric networks located in the region of Bogotá, Colombia using artificial neural networksinfo:eu-repo/semantics/lectureinfo:eu-repo/semantics/publishedVersionOtrohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_8544Stream FlowFlood ForecastingWater TablesCartagena de IndiasInvestigadoresGontijo W 2007 Avaliação e redimensionamento de redes para o monitoramento fluviométrico utilizando o método sharp e o conceito de entropía (Brasilia: Universidad de Brasilia) p 52Asimakopoulou F, Tsekouras G, Gonos I and Stathopulos I 2013 Estimation of seasonal variation of ground resistance using artificial neural networks Electric Power Systems Research 94 113Kheirkhah A, Azadeh A, Saberi M, Azaron M and Shakouri H 2013 Improved estimation of electricity demand function by using of artificial neural network, principal component analysis and data envelopment analysis Computers & Industrial Engineering 64 425Adib H, Haghbakhsh R, Saidi M, Takassi M, Sharifi F, Koolivand M, Rahimpour M and Keshtkar S 2013 Modeling and optimization of FischereTropsch synthesis in the presence of Co (III)/Al2O3 catalyst using artificial neural networks and genetic algorithm Journal of Natural Gas Science and Engineering 10 14Fukushima K 2013 Artificial vision by multi-layered neural networks: Neocognitron and its advances Neural Networks 37 103Taormina R, Chau K and Sethi R 2012 Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon Engineering Applications of Artificial Intelligence 25 1670Mishra N, Soni H, Sharma S and Upadhyay A 2018 Development and analysis of artificial neural network models for rainfall prediction by using time-series data International Journal of Intelligent Systems and Applications 11 16Ainslie B, Reuten D, Le N and Zidek J 2009 Application of an entropy-based Bayesian optimization technique to the redesign of an existing monitoring network for single air pollutants Journal of Environmental Management 90 2715Puangthongthub S, Wangwongwatana S, Kamens R and Serre M 2007 Modeling the space/time distribution of particulate matter in Thailand and optimizing its monitoring network Atmospheric Environment 41 7788Turlapaty A, Anantharaj V, Younan N and Turk F 2010 Precipitation data fusion using vector space transformation and artificial neural networks Pattern Recognition Letters 31 1184Kim J and Pachepsky Y 2010 Reconstructing missing daily precipitation data using regression trees and artificial neural networks for SWAT streamflow simulation Journal of Hydrology 394 305Liu Q, Shi Z, Fang N, Zhu H and Ai L 2013 Modeling the daily suspended sediment concentration in a hyperconcentrated river on the Loess Plateau, China, using the Wavelet–ANN approach Geomorphology 186 181Lin G and Chen L 2006 Identification of homogeneous regions for regional frequency analysis using the self-organizing map Journal of Hydrology 324(4) 1Chowdhury M, Alouani A and Hossain F 2010 Comparison of ordinary kriging and artificial neural network for spatial mapping of arsenic contamination of groundwater Stochastic Environmental Research and Risk Assessment 24(1) 1Mishra A and Coulibaly P 2009 Hydrometric network evaluation for Canadian watersheds Journal of Hydrology 380(3-4) 420Lin G and Chen L 2005 Identification of homogeneous regions for regional frequency analysis using the self-organizing map Journal of Hydrology 324(1) 1González F 2012 Agrupación ecohidrológica de corrientes en la cuenca Magdalena-Cauca dentro del marco 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