Application of artificial neural network and information entropy theory to assess rainfall station distribution: A case study from Colombia
An assessment of the rainfall station distribution in the mountainous area of the Regional Autonomous Corporation of Cundinamarca (CAR, for its acronym in Spanish), Colombia, was conducted by applying concepts from information entropy and artificial neural networks (ANNs). This study was divided int...
- Autores:
-
Garrido Arévalo, Augusto Rafael
Agudelo-Otálora, Luis Mauricio
Obregón-Neira, Nelson
Garrido Arévalo, Víctor Manuel
Quiñones-Bolaños, Edgar Eduardo
Naraei, Parisa
Mehrvar, Mehrab
Bustillo-Lecompte, Ciro Fernando
- 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:20.500.12585/9351
- Palabra clave:
- Hydrology
Rainfall
Artificial neural networks
Information entropy
Clustering process
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc/4.0/
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Application of artificial neural network and information entropy theory to assess rainfall station distribution: A case study from Colombia |
| title |
Application of artificial neural network and information entropy theory to assess rainfall station distribution: A case study from Colombia |
| spellingShingle |
Application of artificial neural network and information entropy theory to assess rainfall station distribution: A case study from Colombia Hydrology Rainfall Artificial neural networks Information entropy Clustering process |
| title_short |
Application of artificial neural network and information entropy theory to assess rainfall station distribution: A case study from Colombia |
| title_full |
Application of artificial neural network and information entropy theory to assess rainfall station distribution: A case study from Colombia |
| title_fullStr |
Application of artificial neural network and information entropy theory to assess rainfall station distribution: A case study from Colombia |
| title_full_unstemmed |
Application of artificial neural network and information entropy theory to assess rainfall station distribution: A case study from Colombia |
| title_sort |
Application of artificial neural network and information entropy theory to assess rainfall station distribution: A case study from Colombia |
| dc.creator.fl_str_mv |
Garrido Arévalo, Augusto Rafael Agudelo-Otálora, Luis Mauricio Obregón-Neira, Nelson Garrido Arévalo, Víctor Manuel Quiñones-Bolaños, Edgar Eduardo Naraei, Parisa Mehrvar, Mehrab Bustillo-Lecompte, Ciro Fernando |
| dc.contributor.author.none.fl_str_mv |
Garrido Arévalo, Augusto Rafael Agudelo-Otálora, Luis Mauricio Obregón-Neira, Nelson Garrido Arévalo, Víctor Manuel Quiñones-Bolaños, Edgar Eduardo Naraei, Parisa Mehrvar, Mehrab Bustillo-Lecompte, Ciro Fernando |
| dc.subject.keywords.spa.fl_str_mv |
Hydrology |
| topic |
Hydrology Rainfall Artificial neural networks Information entropy Clustering process |
| dc.subject.keywords.none.fl_str_mv |
Rainfall Artificial neural networks Information entropy Clustering process |
| description |
An assessment of the rainfall station distribution in the mountainous area of the Regional Autonomous Corporation of Cundinamarca (CAR, for its acronym in Spanish), Colombia, was conducted by applying concepts from information entropy and artificial neural networks (ANNs). This study was divided into two phases: first, a classification of the meteorological stations using two-dimensional self-organizing maps; second, the evaluation of the performance of the ANN by applying concepts of information entropy. Three scenarios were raised for the classification of the meteorological stations by adjusting the number of neurons in the output layer. A high number of neurons in the output layer were obtained, causing the model to over-fit while emphasizing differences amid patterns. When comparing the results of the scenarios, the permanence of certain characteristics and features was found in the system, validating the model classification. Subsequently, the results of the first scenario were used to evaluate the entropy of the historical series. Finally, the results show that the area of study presents a lack of information due to the uncertainty associated with the probabilistic arrangement, which can be corrected with the developed model. Consequently, some recommendations for the redesign of the rainfall are provided |
| publishDate |
2020 |
| dc.date.accessioned.none.fl_str_mv |
2020-08-31T18:25:15Z |
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2020-08-31T18:25:15Z |
| dc.date.issued.none.fl_str_mv |
2020-07-12 |
| dc.date.submitted.none.fl_str_mv |
2020-08-31 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/publishedVersion |
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Artículo |
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publishedVersion |
| dc.identifier.citation.spa.fl_str_mv |
Garrido-Arévalo, A.R.; Agudelo-Otálora, L.M.; Obregón-Neira, N.; Garrido-Arévalo, V.; Quiñones-Bolaños, E.E.; Naraei, P.; Mehrvar, M.; Bustillo-Lecompte, C.F. Application of Artificial Neural Network and Information Entropy Theory to Assess Rainfall Station Distribution: A Case Study from Colombia. Water 2020, 12, 1973. |
| dc.identifier.issn.none.fl_str_mv |
2073-4441 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/9351 |
| dc.identifier.url.none.fl_str_mv |
https://www.mdpi.com/2073-4441/12/7/1973 |
| dc.identifier.doi.none.fl_str_mv |
10.3390/w12071973 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio UTB |
| identifier_str_mv |
Garrido-Arévalo, A.R.; Agudelo-Otálora, L.M.; Obregón-Neira, N.; Garrido-Arévalo, V.; Quiñones-Bolaños, E.E.; Naraei, P.; Mehrvar, M.; Bustillo-Lecompte, C.F. Application of Artificial Neural Network and Information Entropy Theory to Assess Rainfall Station Distribution: A Case Study from Colombia. Water 2020, 12, 1973. 2073-4441 10.3390/w12071973 Universidad Tecnológica de Bolívar Repositorio UTB |
| url |
https://hdl.handle.net/20.500.12585/9351 https://www.mdpi.com/2073-4441/12/7/1973 |
| dc.language.iso.spa.fl_str_mv |
eng |
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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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18 páginas |
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application/pdf |
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Cartagena de Indias |
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Campus Tecnológico |
| dc.publisher.discipline.spa.fl_str_mv |
Ingeniería Electrónica |
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Water; ; Vol. 12 Núm. 7 (2020) |
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Universidad Tecnológica de Bolívar |
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Garrido Arévalo, Augusto Rafaele8e0c761-755b-4e9b-85a3-b64e698faf87Agudelo-Otálora, Luis Mauricioa7432f93-e96a-42ee-a546-b006d5199273Obregón-Neira, Nelsonb5e3f96c-fa5e-4f8d-b82a-dd6a2cbd509bGarrido Arévalo, Víctor Manuel26c9d582-052b-486a-bd3e-e048d3845f74Quiñones-Bolaños, Edgar Eduardoef2f3b45-3841-4354-8dac-e21b2b409e9cNaraei, Parisa3006f074-0174-4cae-9586-64162a540b92Mehrvar, Mehrab6a761188-d36c-45f8-8a5c-c75416b6dff8Bustillo-Lecompte, Ciro Fernando98aa8742-1bf6-48c5-b464-43d875713203Cartagena de Indias2020-08-31T18:25:15Z2020-08-31T18:25:15Z2020-07-122020-08-31Garrido-Arévalo, A.R.; Agudelo-Otálora, L.M.; Obregón-Neira, N.; Garrido-Arévalo, V.; Quiñones-Bolaños, E.E.; Naraei, P.; Mehrvar, M.; Bustillo-Lecompte, C.F. Application of Artificial Neural Network and Information Entropy Theory to Assess Rainfall Station Distribution: A Case Study from Colombia. Water 2020, 12, 1973.2073-4441https://hdl.handle.net/20.500.12585/9351https://www.mdpi.com/2073-4441/12/7/197310.3390/w12071973Universidad Tecnológica de BolívarRepositorio UTBAn assessment of the rainfall station distribution in the mountainous area of the Regional Autonomous Corporation of Cundinamarca (CAR, for its acronym in Spanish), Colombia, was conducted by applying concepts from information entropy and artificial neural networks (ANNs). This study was divided into two phases: first, a classification of the meteorological stations using two-dimensional self-organizing maps; second, the evaluation of the performance of the ANN by applying concepts of information entropy. Three scenarios were raised for the classification of the meteorological stations by adjusting the number of neurons in the output layer. A high number of neurons in the output layer were obtained, causing the model to over-fit while emphasizing differences amid patterns. When comparing the results of the scenarios, the permanence of certain characteristics and features was found in the system, validating the model classification. Subsequently, the results of the first scenario were used to evaluate the entropy of the historical series. Finally, the results show that the area of study presents a lack of information due to the uncertainty associated with the probabilistic arrangement, which can be corrected with the developed model. Consequently, some recommendations for the redesign of the rainfall are provided18 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Water; ; Vol. 12 Núm. 7 (2020)Application of artificial neural network and information entropy theory to assess rainfall station distribution: A case study from Colombiainfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1HydrologyRainfallArtificial neural networksInformation entropyClustering processInvestigadoresCampus TecnológicoIngeniería ElectrónicaZoppou, C. Review of urban storm water models. Environ. Model. Softw. 2001, 16, 195–231. [CrossRef]Daly, C.; Neilson, R.P.; Phillips, D.L. A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteorol. 1994, 33, 140–158. [CrossRef] Water 2020, 12, 1973 17 of 18Johansson, B.; Chen, D. The influence of wind and topography on precipitation distribution in Sweden: Statistical analysis and modelling. Int. J. Climatol. 2003, 23, 1523–1535. [CrossRef]Lin, G.F.; Chen, L.H. Identification of homogeneous regions for regional frequency analysis using the self-organizing map. J. Hydrol. 2006, 324, 1–9. [CrossRef]Rojas-Polanco, M.I.; Mora-Mora, L.E. Optimum design of rainfall network. Rev. For. Venez. 2009, 53, 9–22.Chowdhury, M.; Alouani, A.; Hossain, F. Comparison of ordinary kriging and artificial neural network for spatial mapping of arsenic contamination of groundwater. Stoch. Environ. Res. Risk Assess. 2010, 24, 1–7. [CrossRef]Chen, Y.C.; Wei, C.; Yeh, H.C. Rainfall network design using kriging and entropy. Hydrol. Process. 2008, 22, 340–346. [CrossRef]Karabacak, K.; Cetin, N. Artificial neural networks for controlling wind-PV power systems: A review. Renew. Sustain. Energy Rev. 2014, 29, 804–827. [CrossRef]Dursun, M.; Özden, S. An efficient improved photovoltaic irrigation system with artificial neural network based modeling of soil moisture distribution—A case study in Turkey. Comput. Electron. Agric. 2014, 102, 120–126. [CrossRef]Asimakopoulou, F.E.; Tsekouras, G.J.; Gonos, I.F.; Stathopulos, I.A. Estimation of seasonal variation of ground resistance using Artificial Neural Networks. Electr. Power Syst. Res. 2013, 94, 113–121. [CrossRef]Adib, H.; Haghbakhsh, R.; Saidi, M.; Takassi, M.A.; Sharifi, F.; Koolivand, M.; Rahimpour, M.R.; Keshtkari, S. Modeling and optimization of Fischer-Tropsch synthesis in the presence of Co (III)/Al2O3 catalyst using artificial neural networks and genetic algorithm. J. Nat. Gas Sci. Eng. 2013, 10, 14–24. [CrossRef]Mohajerani, M.; Mehrvar, M.; Ein-Mozaffari, F. Using an external-loop airlift sonophotoreactor to enhance the biodegradability of aqueous sulfadiazine solution. Sep. Purif. Technol. 2012, 90, 173–181. [CrossRef]Fukushima, K. Artificial vision by multi-layered neural networks: Neocognitron and its advances. Neural Netw. 2013, 37, 103–119. [CrossRef]Mallela, U.K.; Upadhyay, A. Buckling load prediction of laminated composite stiffened panels subjected to in-plane shear using artificial neural networks. Thin-Walled Struct. 2016, 102, 158–164. [CrossRef]Turlapaty, A.C.; Anantharaj, V.G.; Younan, N.H.; Joseph Turk, F. Precipitation data fusion using vector space transformation and artificial neural networks. Pattern Recognit. Lett. 2010, 31, 1184–1200. [CrossRef]Liu, Q.J.; Shi, Z.H.; Fang, N.F.; Zhu, H.D.; Ai, L. Modeling the daily suspended sediment concentration in a hyperconcentrated river on the Loess Plateau, China, using the Wavelet-ANN approach. Geomorphology 2013, 186, 181–190. [CrossRef]Garrido-Arévalo, A.R.; Agudelo, L.M.; Obregon, N.; Garrido, V.M. Classification of pluviometric networks located in the region of Bogotá, Colombia using artificial neural networks. J. Phys. Conf. Ser. 2020, 1448. [CrossRef]Kar, A.K.; Lohani, A.K.; Goel, N.K.; Roy, G.P. Rain gauge network design for flood forecasting using multi-criteria decision analysis and clustering techniques in lower Mahanadi river basin, India. J. Hydrol. Reg. Stud. 2015, 4, 313–332. [CrossRef]Wei, C.; Yeh, H.C.; Chen, Y.C. Spatiotemporal Scaling Effect on Rainfall Network Design Using Entropy. Entropy 2014, 16, 4626–4647. [CrossRef]Shannon, C.E. A Mathematical Theory of Communication. Bell Syst. Tech. J. 1948, 27, 379–423. [CrossRef] 21. Vinod, H.D. Maximum entropy ensembles for time series inference in economics. J. Asian Econ. 2006, 17, 955–978. [CrossRef]Noonan, J.P.; Basu, P. On estimation error using maximum entropy density estimates. Kybernetes 2007, 36, 52–64. [CrossRef]Weber, T.C. Maximum entropy modeling of mature hardwood forest distribution in four U.S. states. For. Ecol. Manag. 2011, 261, 779–788. [CrossRef]Payandeh Najafabadi, A.T.; Hatami, H.; Omidi Najafabadi, M. A maximum-entropy approach to the linear credibility formula. Insur. Math. Econ. 2012, 51, 216–221. [CrossRef]Aurbacher, J.; Dabbert, S. Generating crop sequences in land-use models using maximum entropy and Markov chains. Agric. Syst. 2011, 104, 470–479. [CrossRef]Xie, L.; Li, G.; Xiao, M.; Peng, L. Novel classification method for remote sensing images based on information entropy discretization algorithm and vector space model. Comput. Geosci. 2016, 89, 252–259. [CrossRef] Water 2020, 12, 1973 18 of 18Calisto Acosta, O.E. River gauging with one velocity point based on the principle of maximum entropy. Ing. Hidráulica Mex. 2002, 17, 5–19.Dalezios, N.R.; Tyraskis, P.A. Maximum entropy spectra for regional precipitation analysis and forecasting. J. Hydrol. 1989, 109, 25–42. [CrossRef]Mishra, A.K.; Özger, M.; Singh, V.P. An entropy-based investigation into the variability of precipitation. J. Hydrol. 2009, 370, 139–154. [CrossRef]CAR. 2012–2015 Master Plan; Corporacion Autonoma Regional de Cundinamarca (CAR): Bogota, Colombia, 2012.Hurtado-Montoya, A.F.; Mesa-Sánchez, Ó.J. Reanalysis of monthly precipitation fields in Colombian territory. DYNA 2014, 81, 251–258. [CrossRef]CAR. 2016–2019 Master Plan; Corporacion Autonoma Regional de Cundinamarca (CAR): Bogota, Colombia, 2016.OAS. Manual for Design, Installation, Operation and Maintenance of Systems of Flood Early Warning and Online Database; The Organization of American States (OAS), Department of Sustainable Development: Washington, DC, USA, 2010.Wang, W.; Wang, D.; Singh, V.P.; Wang, Y.; Wu, J.; Wang, L.; Zou, X.; Liu, J.; Zou, Y.; He, R. Optimization of rainfall networks using information entropy and temporal variability analysis. J. Hydrol. 2018, 559, 136–155. [CrossRef]CAR. SICLICA—Sistema de Información Climatológica e Hidrológica: Valores Totales Mensuales de Precipitación, Máxima en 24 Horas (mm); Corporacion Autonoma Regional de Cundinamarca (CAR): Bogota, Colombia, 2010.González-Cuéllar, F.; Obregón-Neira, N. Self-organizing maps of Kohonen as a river clustering tool within the methodology for determining regional ecological flows ELOHA. Ing. Univ. 2013, 17, 311–323.Hamzehie, M.E.; Fattahi, M.; Najibi, H.; Van der Bruggen, B.; Mazinani, S. Application of artificial neural networks for estimation of solubility of acid gases (H2S and CO2 ) in 32 commonly ionic liquid and amine solutions. J. Nat. Gas Sci. Eng. 2015, 24, 106–114. [CrossRef]Lohani, A.K.; Kumar, R.; Singh, R.D. Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. J. Hydrol. 2012, 442, 23–35. [CrossRef]Chang, T.K.; Talei, A.; Alaghmand, S.; Ooi, M.P.L. Choice of rainfall inputs for event-based rainfall-runoff modeling in a catchment with multiple rainfall stations using data-driven techniques. J. Hydrol. 2017, 545, 100–108. [CrossRef]González-álvarez, A.; Viloria-Marimón, O.M.; Coronado-Hernández, O.E.; Vélez-Pereira, A.M.; Tesfagiorgis, K.; Coronado-Hernández, J.R. Isohyetal maps of daily maximum rainfall for different return periods for the Colombian Caribbean Region. Water 2019, 11, 358. [CrossRef]Elshorbagy, A.; Corzo, G.; Srinivasulu, S.; Solomatine, D.P. Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology—Part 1: Concepts and methodology. Hydrol. Earth Syst. Sci. 2010, 14, 1931–1941. [CrossRef]Farsadnia, F.; Rostami Kamrood, M.; Moghaddam Nia, A.; Modarres, R.; Bray, M.T.; Han, D.; Sadatinejad, J. Identification of homogeneous regions for regionalization of watersheds by two-level self-organizing feature maps. J. Hydrol. 2014, 509, 387–397. 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