Application of artificial neural networks to frost detection in central Chile using the next day minimum air temperature forecast

ABSTRACT Predicting future climatic events is one of the key issues in many fields, whether in scientific or industrial areas. An artificial neural network (ANN) model, based on a backpropagation type, was developed in this study to predict the minimum air temperature of the following day from meteo...

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Autores principales:	Fuentes,Marcel, Campos,Cristóbal, García-Loyola,Sebastián
Lenguaje:	English
Publicado:	Instituto de Investigaciones Agropecuarias, INIA 2018
Materias:	Artificial neural network (ANN) frost detection next day forecasting
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392018000300327
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spelling	oai:scielo:S0718-583920180003003272018-11-14Application of artificial neural networks to frost detection in central Chile using the next day minimum air temperature forecastFuentes,MarcelCampos,CristóbalGarcía-Loyola,Sebastián Artificial neural network (ANN) frost detection next day forecasting ABSTRACT Predicting future climatic events is one of the key issues in many fields, whether in scientific or industrial areas. An artificial neural network (ANN) model, based on a backpropagation type, was developed in this study to predict the minimum air temperature of the following day from meteorological data using air temperature, relative humidity, radiation, precipitation, and wind direction and speed to detect the occurrence of radiative frost events. The configuration of the next day ANN prediction system allows operating with low-power computing machines; it is able to generate early warnings that can lead to the development of effective strategies to reduce crop damage, lower quality, and losses in agricultural production. This paper presents a procedural approach to an ANN, which was trained, validated, and tested in 10 meteorological stations in central Chile for approximately 8 yr (2010-2017). The overall mean results were classified by a confusion matrix and showed good performance in predicting minimum temperature with a mean square error (MSE) of 2.99 °C for the network, 1.71 °C for training, 1.77 °C for validation, and 1.74 °C for the testing processes. Frost detection results had an appropriate 98% overall mean accuracy (ACC), 86% sensitivity (TPR), and 2% error rate (ER). Differences and errors in frost detection can be attributed to several factors that are mainly associated with the accuracy of the sensors meteorological stations, local climatic and geographic conditions, and the number of parameters that enter in the ANN training processes.info:eu-repo/semantics/openAccessInstituto de Investigaciones Agropecuarias, INIAChilean journal of agricultural research v.78 n.3 20182018-09-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392018000300327en10.4067/S0718-58392018000300327
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Artificial neural network (ANN) frost detection next day forecasting
spellingShingle	Artificial neural network (ANN) frost detection next day forecasting Fuentes,Marcel Campos,Cristóbal García-Loyola,Sebastián Application of artificial neural networks to frost detection in central Chile using the next day minimum air temperature forecast
description	ABSTRACT Predicting future climatic events is one of the key issues in many fields, whether in scientific or industrial areas. An artificial neural network (ANN) model, based on a backpropagation type, was developed in this study to predict the minimum air temperature of the following day from meteorological data using air temperature, relative humidity, radiation, precipitation, and wind direction and speed to detect the occurrence of radiative frost events. The configuration of the next day ANN prediction system allows operating with low-power computing machines; it is able to generate early warnings that can lead to the development of effective strategies to reduce crop damage, lower quality, and losses in agricultural production. This paper presents a procedural approach to an ANN, which was trained, validated, and tested in 10 meteorological stations in central Chile for approximately 8 yr (2010-2017). The overall mean results were classified by a confusion matrix and showed good performance in predicting minimum temperature with a mean square error (MSE) of 2.99 °C for the network, 1.71 °C for training, 1.77 °C for validation, and 1.74 °C for the testing processes. Frost detection results had an appropriate 98% overall mean accuracy (ACC), 86% sensitivity (TPR), and 2% error rate (ER). Differences and errors in frost detection can be attributed to several factors that are mainly associated with the accuracy of the sensors meteorological stations, local climatic and geographic conditions, and the number of parameters that enter in the ANN training processes.
author	Fuentes,Marcel Campos,Cristóbal García-Loyola,Sebastián
author_facet	Fuentes,Marcel Campos,Cristóbal García-Loyola,Sebastián
author_sort	Fuentes,Marcel
title	Application of artificial neural networks to frost detection in central Chile using the next day minimum air temperature forecast
title_short	Application of artificial neural networks to frost detection in central Chile using the next day minimum air temperature forecast
title_full	Application of artificial neural networks to frost detection in central Chile using the next day minimum air temperature forecast
title_fullStr	Application of artificial neural networks to frost detection in central Chile using the next day minimum air temperature forecast
title_full_unstemmed	Application of artificial neural networks to frost detection in central Chile using the next day minimum air temperature forecast
title_sort	application of artificial neural networks to frost detection in central chile using the next day minimum air temperature forecast
publisher	Instituto de Investigaciones Agropecuarias, INIA
publishDate	2018
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392018000300327
work_keys_str_mv	AT fuentesmarcel applicationofartificialneuralnetworkstofrostdetectionincentralchileusingthenextdayminimumairtemperatureforecast AT camposcristobal applicationofartificialneuralnetworkstofrostdetectionincentralchileusingthenextdayminimumairtemperatureforecast AT garcialoyolasebastian applicationofartificialneuralnetworkstofrostdetectionincentralchileusingthenextdayminimumairtemperatureforecast
_version_	1714205379514073088

Application of artificial neural networks to frost detection in central Chile using the next day minimum air temperature forecast

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