Pub Date : 2021-11-03DOI: 10.1109/RPIC53795.2021.9648425
A. Faramiñán, M. F. Degano, Facundo Carmona, Paula Olivera Rodriguez
An important issue for agricultural planning is to estimate evapotranspiration accurately due to its fundamental role in the sustainable use of water resources. In this sense, it is essential to have reliable and precise evapotranspiration measurements to improve models or products, mainly related to predicting droughts. The main objective of the present study is to evaluate the Support Vector Machine Regression’s (SVR) potential to estimate the actual evapotranspiration (ETa) through a NASA-Power dataset in the Pampean Region of Argentina. The results obtained were compared with ETa values (water balance), based on information from 12 agro-meteorological stations (1983 – 2012). After training and validating the SVR algorithm, we observed statistical mean errors of 0.39 ± 0.07 mm/d, 0.54 ± 0.09 mm/d, and 0.67 ± 0.07 for the MAE, RMSE, and R2, respectively. The results show the feasibility of applying machine learning algorithms for obtaining ETa values in agricultural plains without agro-meteorological data.
{"title":"Estimation of actual evapotranspiration using NASA-POWER data and Support Vector Machine","authors":"A. Faramiñán, M. F. Degano, Facundo Carmona, Paula Olivera Rodriguez","doi":"10.1109/RPIC53795.2021.9648425","DOIUrl":"https://doi.org/10.1109/RPIC53795.2021.9648425","url":null,"abstract":"An important issue for agricultural planning is to estimate evapotranspiration accurately due to its fundamental role in the sustainable use of water resources. In this sense, it is essential to have reliable and precise evapotranspiration measurements to improve models or products, mainly related to predicting droughts. The main objective of the present study is to evaluate the Support Vector Machine Regression’s (SVR) potential to estimate the actual evapotranspiration (ETa) through a NASA-Power dataset in the Pampean Region of Argentina. The results obtained were compared with ETa values (water balance), based on information from 12 agro-meteorological stations (1983 – 2012). After training and validating the SVR algorithm, we observed statistical mean errors of 0.39 ± 0.07 mm/d, 0.54 ± 0.09 mm/d, and 0.67 ± 0.07 for the MAE, RMSE, and R2, respectively. The results show the feasibility of applying machine learning algorithms for obtaining ETa values in agricultural plains without agro-meteorological data.","PeriodicalId":299649,"journal":{"name":"2021 XIX Workshop on Information Processing and Control (RPIC)","volume":"207 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114018092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-03DOI: 10.1109/RPIC53795.2021.9648422
A. Ferral, A. Gili, V. Andreo, A. Germãn, G. Beltramone, M. Bonansea, Sofía Paná, M. Scavuzzo
Urban localities are mainly covered by concrete and asphalt paving material, which are impermeable surfaces with higher heat absorption capacity and a lower albedo, thus absorbing more radiation compared to the surrounding countryside. The urban surface heat island effect is described as a higher surface temperature in cities compared to a cooler temperature in surrounding areas. Canopy layer urban heat island (HI) are typically detected by in situ sensors at standard (screen-level) meteorological height. Ont he other hand, thermal remote sensors observe the surface urban heat island index (SUHI). The aim of this work is to calculate the spatial distribution of the SUHI index in Cordoba city and in its metropolitan area, and to analyse its relationship with different land covers using satellite information. Cordoba city, located in the central region of Argentina, is the second most populated city in the country. A LANDSAT-8 image of the study area was used to calculate urban heat island index, UHII, and SUHI. Urban and Non-urban region were defined and compared. It was observed that the same type of land use has significant different temperature mean value depending on whether it is located on an urban island or in a rural or open environment.
{"title":"Calculation of surface urban heat index from LANDSAT-8 TIRS data and its relation with land cover","authors":"A. Ferral, A. Gili, V. Andreo, A. Germãn, G. Beltramone, M. Bonansea, Sofía Paná, M. Scavuzzo","doi":"10.1109/RPIC53795.2021.9648422","DOIUrl":"https://doi.org/10.1109/RPIC53795.2021.9648422","url":null,"abstract":"Urban localities are mainly covered by concrete and asphalt paving material, which are impermeable surfaces with higher heat absorption capacity and a lower albedo, thus absorbing more radiation compared to the surrounding countryside. The urban surface heat island effect is described as a higher surface temperature in cities compared to a cooler temperature in surrounding areas. Canopy layer urban heat island (HI) are typically detected by in situ sensors at standard (screen-level) meteorological height. Ont he other hand, thermal remote sensors observe the surface urban heat island index (SUHI). The aim of this work is to calculate the spatial distribution of the SUHI index in Cordoba city and in its metropolitan area, and to analyse its relationship with different land covers using satellite information. Cordoba city, located in the central region of Argentina, is the second most populated city in the country. A LANDSAT-8 image of the study area was used to calculate urban heat island index, UHII, and SUHI. Urban and Non-urban region were defined and compared. It was observed that the same type of land use has significant different temperature mean value depending on whether it is located on an urban island or in a rural or open environment.","PeriodicalId":299649,"journal":{"name":"2021 XIX Workshop on Information Processing and Control (RPIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129854449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-03DOI: 10.1109/RPIC53795.2021.9648460
Steven Martinez Vargas, C. Delrieux, A. Vitale, Katy Lorena Blanco Monroy
We trained and analyzed the behavior and robustness of two regression models, Random Forest and Support Vector Machine, with aerial hyperspectral images and echosounder measurements in an area of the Bahia Blanca estuary (Buenos Aires, Argentina) to generate a dense bathymetric map. This region of the estuary is characterized by high sediment transport, which makes its waters turbid, which makes bathymetric-optical estimates difficult. Images of 24 NIR and visible spectral bands acquired using a hyperspectral camera on board a UAV were used, together with 100 bathymetric data points surveyed with a sonar sensor on board a USV in an area of approximately 800 m2. The best model was Random Forest with a coefficient determination of 0.815 (for the test data), an RSME = 0.160 m, and an absolute mean error less than 1.3%. We performed ablation tests to evaluate the robustness of the models and using SHAP values we determined the bands with the highest incidence in the model. The results allow for dense and accurate reconstructions of the underwater profile in shallow and muddy regions of the Bahia Blanca estuary, showing the feasibility of merging hyperspectral images with sonar data in turbid shallow waters.
{"title":"Regression Models for Dense Bathymetry in Turbid Coastal Areas","authors":"Steven Martinez Vargas, C. Delrieux, A. Vitale, Katy Lorena Blanco Monroy","doi":"10.1109/RPIC53795.2021.9648460","DOIUrl":"https://doi.org/10.1109/RPIC53795.2021.9648460","url":null,"abstract":"We trained and analyzed the behavior and robustness of two regression models, Random Forest and Support Vector Machine, with aerial hyperspectral images and echosounder measurements in an area of the Bahia Blanca estuary (Buenos Aires, Argentina) to generate a dense bathymetric map. This region of the estuary is characterized by high sediment transport, which makes its waters turbid, which makes bathymetric-optical estimates difficult. Images of 24 NIR and visible spectral bands acquired using a hyperspectral camera on board a UAV were used, together with 100 bathymetric data points surveyed with a sonar sensor on board a USV in an area of approximately 800 m2. The best model was Random Forest with a coefficient determination of 0.815 (for the test data), an RSME = 0.160 m, and an absolute mean error less than 1.3%. We performed ablation tests to evaluate the robustness of the models and using SHAP values we determined the bands with the highest incidence in the model. The results allow for dense and accurate reconstructions of the underwater profile in shallow and muddy regions of the Bahia Blanca estuary, showing the feasibility of merging hyperspectral images with sonar data in turbid shallow waters.","PeriodicalId":299649,"journal":{"name":"2021 XIX Workshop on Information Processing and Control (RPIC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130648881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-03DOI: 10.1109/RPIC53795.2021.9648515
Omelio Barba Leal, Fabián Rinalde, Jorge Cogo, J. P. Pascual
Wireless Local Area Network (WLAN) devices and C-band weather radars share the same frequency band. When WLAN devices transmit at the same or close frequency as weather radar the resulting interference can severely limit the usefulness of radar images.This paper proposes an algorithm for WLAN packets detection in weather radar data based on the location of the preamble of these packets, taking advantage of its deterministic structure. The theoretical guidelines that define the detection algorithm and the reference signal are presented. The performance of the detector is evaluated using numerical simulations and is tested with real data obtained from the Argentinian weather radar RMA1, located in Córdoba city. The probability of detection obtained when processing radar data is consistent with the numerical simulations results and with the predictions of the theoretical model.
{"title":"WLAN Signal Detection in Weather Radar Data","authors":"Omelio Barba Leal, Fabián Rinalde, Jorge Cogo, J. P. Pascual","doi":"10.1109/RPIC53795.2021.9648515","DOIUrl":"https://doi.org/10.1109/RPIC53795.2021.9648515","url":null,"abstract":"Wireless Local Area Network (WLAN) devices and C-band weather radars share the same frequency band. When WLAN devices transmit at the same or close frequency as weather radar the resulting interference can severely limit the usefulness of radar images.This paper proposes an algorithm for WLAN packets detection in weather radar data based on the location of the preamble of these packets, taking advantage of its deterministic structure. The theoretical guidelines that define the detection algorithm and the reference signal are presented. The performance of the detector is evaluated using numerical simulations and is tested with real data obtained from the Argentinian weather radar RMA1, located in Córdoba city. The probability of detection obtained when processing radar data is consistent with the numerical simulations results and with the predictions of the theoretical model.","PeriodicalId":299649,"journal":{"name":"2021 XIX Workshop on Information Processing and Control (RPIC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115526550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-03DOI: 10.1109/RPIC53795.2021.9648503
D. M. Petruzzi, Celeste Cebedio, L. Rabioglio
Understanding the characteristics of the channel is a key factor in the design of a communication system. In particular, the underwater channel has the disadvantage of having great variability in relatively short periods of time. This requires having to have a method that allows obtaining the parameters that are essential when optimizing communication, so that it is adaptive. This article describes and applies a characterization technique for environmental noise measurement in shallow waters off the coast of Mar del Plata. The result showed the degree of closeness between the probability density function (pdf) of the aforementioned noise with respect to that of a Gaussian distribution.
{"title":"Characterization of Ambient Noise Underwater in the coastal area of Mar del Plata","authors":"D. M. Petruzzi, Celeste Cebedio, L. Rabioglio","doi":"10.1109/RPIC53795.2021.9648503","DOIUrl":"https://doi.org/10.1109/RPIC53795.2021.9648503","url":null,"abstract":"Understanding the characteristics of the channel is a key factor in the design of a communication system. In particular, the underwater channel has the disadvantage of having great variability in relatively short periods of time. This requires having to have a method that allows obtaining the parameters that are essential when optimizing communication, so that it is adaptive. This article describes and applies a characterization technique for environmental noise measurement in shallow waters off the coast of Mar del Plata. The result showed the degree of closeness between the probability density function (pdf) of the aforementioned noise with respect to that of a Gaussian distribution.","PeriodicalId":299649,"journal":{"name":"2021 XIX Workshop on Information Processing and Control (RPIC)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132087595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-03DOI: 10.1109/RPIC53795.2021.9648493
Diana C. Vertiz del Valle, L. Carolina Carrere, C. Tabernig
In a brain-computer interface (BCI), the first module after the user, is the amplifier to acquire electroencephalography (EEG) signals. These are usually multichannel amplifiers which include pre-condition of the EEG signal for further processing. To develop BCI based on BCI2000, there are many data acquisition modules as contributions from users of BCI2000. These contributions support EEG amplifiers that are not easily available for Latin American countries, limiting their accessibility by patients and health institutions, which are BCI’s final users. This article presents the design and evaluation of a new signal source module for BCI2000 to connect a biopotential amplifier from the open-source BioAmp project. This new signal source module embedded in BCI2000 allows the acquisition, visualization of EEG signals and their storing for later processing. Its implementation in the clinical context will allow the advancement in translational research in the BCI field.
{"title":"A new signal source module for BCI2000 interface to an open-source multichannel acquisition system","authors":"Diana C. Vertiz del Valle, L. Carolina Carrere, C. Tabernig","doi":"10.1109/RPIC53795.2021.9648493","DOIUrl":"https://doi.org/10.1109/RPIC53795.2021.9648493","url":null,"abstract":"In a brain-computer interface (BCI), the first module after the user, is the amplifier to acquire electroencephalography (EEG) signals. These are usually multichannel amplifiers which include pre-condition of the EEG signal for further processing. To develop BCI based on BCI2000, there are many data acquisition modules as contributions from users of BCI2000. These contributions support EEG amplifiers that are not easily available for Latin American countries, limiting their accessibility by patients and health institutions, which are BCI’s final users. This article presents the design and evaluation of a new signal source module for BCI2000 to connect a biopotential amplifier from the open-source BioAmp project. This new signal source module embedded in BCI2000 allows the acquisition, visualization of EEG signals and their storing for later processing. Its implementation in the clinical context will allow the advancement in translational research in the BCI field.","PeriodicalId":299649,"journal":{"name":"2021 XIX Workshop on Information Processing and Control (RPIC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130818117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-03DOI: 10.1109/RPIC53795.2021.9648414
Germn I. Brunini, D. Velis, Juan I. Sabbione
We design a deep neural network (DNN) and train it to invert the focal mechanism of microseismic events that occur during a hydraulic fracture treatment of unconventional reservoirs. For the testing, we generate synthetic microseismic events in anisotropic 3D media and consider a realistic dual-well monitoring scenario. We show that for this geometry a trained DNN can successfully retrieve the six independent elements of the moment tensor. We statistically analyze the correlation coefficients and relative errors of the results and demonstrate that the moment tensor can be accurately estimated using the proposed DNN, providing a reliable alternative to other conventional inversion techniques.
{"title":"Seismic Moment Tensor Inversion in Anisotropic Media using Deep Neural Networks","authors":"Germn I. Brunini, D. Velis, Juan I. Sabbione","doi":"10.1109/RPIC53795.2021.9648414","DOIUrl":"https://doi.org/10.1109/RPIC53795.2021.9648414","url":null,"abstract":"We design a deep neural network (DNN) and train it to invert the focal mechanism of microseismic events that occur during a hydraulic fracture treatment of unconventional reservoirs. For the testing, we generate synthetic microseismic events in anisotropic 3D media and consider a realistic dual-well monitoring scenario. We show that for this geometry a trained DNN can successfully retrieve the six independent elements of the moment tensor. We statistically analyze the correlation coefficients and relative errors of the results and demonstrate that the moment tensor can be accurately estimated using the proposed DNN, providing a reliable alternative to other conventional inversion techniques.","PeriodicalId":299649,"journal":{"name":"2021 XIX Workshop on Information Processing and Control (RPIC)","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114968026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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