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Virtual weather stations for meteorological data estimations

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Blas Manuel Franco Ortellado at Universidad de Valladolid
Blas Manuel Franco Ortellado
Luis Hernández-Callejo at Universidad de Valladolid
Luis Hernández-Callejo
Luis Manuel Navas Gracia at Universidad de Valladolid
Luis Manuel Navas Gracia
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Abstract and Figures

In this paper, the concept of Virtual Weather Stations (VWS) is introduced. A VWS is an integration of algorithms to download meteorological data, process and use them with the main objective of estimate data in nearby locations with no meteorological stations. To develop the VWS, the performances of different interpolation methods were evaluated to test the accuracy. Daily data from an automatic weather station network, such as precipitation (Precip), air temperature (Temp), air relative humidity, mean wind speed, total solar irradiation, and reference evapotranspiration were interpolated using artificial neural networks (ANNs) with the hardlim, sigmoid, hyperbolic tangent (tanh), softsign, and rectified linear unit (relu) activations functions were employed. To contrast the ANNs interpolations, alternatives methods such as inverse distance weighting, inverse-squared distance weighting, multilinear regression, and random forest regression were used. To validate the models, a randomly selected weather station was removed from the daily datasets, and the interpolated values were compared with the actual station records. Additionally, interpolations in the summer and winter months were performed to check the capability of the models during periods with more extreme phenomena. The results showed that the interpolation methods have an R2 up to 0.98 for variables such as temperatures for the period of 1 year. Meanwhile, during the summer and winter, the models presented lower accuracy. From a practical perspective, the methods here described could be useful to produce meteorological data with the VWS to record temperatures and dose the irrigation in crops.
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ORIGINAL ARTICLE
Virtual weather stations for meteorological data estimations
B. M. Franco
1
•L. Herna
´ndez-Callejo
1
•L. M. Navas-Gracia
1
Received: 12 April 2019 / Accepted: 7 January 2020 / Published online: 18 January 2020
ÓSpringer-Verlag London Ltd., part of Springer Nature 2020
Abstract
In this paper, the concept of Virtual Weather Stations (VWS) is introduced. A VWS is an integration of algorithms to
download meteorological data, process and use them with the main objective of estimate data in nearby locations with no
meteorological stations. To develop the VWS, the performances of different interpolation methods were evaluated to test
the accuracy. Daily data from an automatic weather station network, such as precipitation (Precip), air temperature (Temp),
air relative humidity, mean wind speed, total solar irradiation, and reference evapotranspiration were interpolated using
artificial neural networks (ANNs) with the hardlim, sigmoid, hyperbolic tangent (tanh), softsign, and rectified linear unit
(relu) activations functions were employed. To contrast the ANNs interpolations, alternatives methods such as inverse
distance weighting, inverse-squared distance weighting, multilinear regression, and random forest regression were used. To
validate the models, a randomly selected weather station was removed from the daily datasets, and the interpolated values
were compared with the actual station records. Additionally, interpolations in the summer and winter months were
performed to check the capability of the models during periods with more extreme phenomena. The results showed that the
interpolation methods have an R
2
up to 0.98 for variables such as temperatures for the period of 1 year. Meanwhile, during
the summer and winter, the models presented lower accuracy. From a practical perspective, the methods here described
could be useful to produce meteorological data with the VWS to record temperatures and dose the irrigation in crops.
Keywords Machine learning Neural networks Temperature Relative humidity Evapotranspiration
1 Introduction
The origin of data from different sensors is very common
today. For example, some authors [1] propose merging data
from different medical data. The results obtained by this
work are very good, and the need to work with data from
different sources is evident. Continuing with the medical
application, the quality of service in the data is funda-
mental, and in this sense, some authors [2] work on the
subject, specifically in three specific areas: a novel adaptive
QoS computation algorithm (AQCA); a framework of QoS
computation in medical applications is proposed at physi-
cal, medium access control (MAC) and network layers; and
a QoS computation mechanism with proposed AQCA and
quality of experience (QoE) is developed.
The business world collects large amounts of data, and
in most cases, it does so in a centralized framework. Some
authors [3] propose a decentralized blockchain-enabled
privacy-preserving trajectory data mining framework
where the proprietary of the data rests with the user and not
with the enterprise. The results obtained by the authors are
promising, and they guarantee the privacy of the data.
Agriculture is one of the most sensitive activities to
weather conditions, and the climate change has impacts to
this sector, from food production to economic problems in
agriculture and related industries, making food security a
tangible problem in the next few decades [4,5]. More
information, research, and strategies are needed to mini-
mize the adverse effects of the uncertain scenario. The
knowledge of weather conditions helps to make better
decisions in crop management, elaborate sowing calendars,
pest population models [6], and precision irrigation dosage
&B. M. Franco
blasmanuel.franco@uva.es
L. Herna
´ndez-Callejo
luis.hernandez.callejo@uva.es
L. M. Navas-Gracia
luismanuel.navas@uva.es
1
Department of Agricultural and Forestry Engineering,
University of Valladolid, 34005 Palencia, Spain
123
Neural Computing and Applications (2020) 32:12801–12812
https://doi.org/10.1007/s00521-020-04727-8(0123456789().,-volV)(0123456789().,-volV)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... As part of the VWS development, the performance of ANN models for interpolating each separate meteorological variable (global solar irradiation, maximum, average and minimum temperatures) was evaluated. The performance of the models is compared with those obtained by Franco et al. [11], who proposed the use of a VWS in places where meteorological data are needed, as an alternative to their acquisition, when it is not possible to install a meteorological station. The ANN models, in this case, were used with all the variables of the same place, while in this article, the estimation of each variable (solar irradiation and temperatures) is carried out separately (an ANN model for each meteorological variable). ...
... The results of the ANN models for estimating daily global solar irradiation at the reference station presented in Figure 2a are shown in Table 1. The best result is obtained when using ANN (2-4-1) with RMSE = 1.04 MJ/m 2 , which improves on the best ANN result of Franco et al. [11] for the summer months of 1.63 MJ/m 2 , by using the rectified linear unit activation function. ...
... The results of the ANN models shown in Figure 2b for the estimation of the daily maximum temperature at the reference station, are presented in Table 2. The best result obtained is the ANN (2-4-1) with RMSE = 0.68 • C, which improves the best result of the ANNs Franco et al. [11] for the summer months by 1.28 • C using the sigmoid activation function. ...
Daily Estimation of Global Solar Irradiation and Temperatures Using Artificial Neural Networks through the Virtual Weather Station Concept in Castilla and León, Spain
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In this article, the interpolation of daily data of global solar irradiation, and the maximum, average, and minimum temperatures were measured. These measurements were carried out in the agrometeorological stations belonging to the Agro-climatic Information System for Irrigation (SIAR, in Spanish) of the Region of Castilla and León, in Spain, through the concept of Virtual Weather Station (VWS), which is implemented with Artificial Neural Networks (ANNs). This is serving to estimate data in every point of the territory, according to their geographic coordinates (i.e., longitude and latitude). The ANNs of the Multilayer Feed-Forward Perceptron (MLP) used are daily trained, along with data recorded in 53 agro-meteorological stations, and where the validation of the results is conducted in the station of Tordesillas (Valladolid). The ANN models for daily interpolation were tested with one, two, three, and four neurons in the hidden layer, over a period of 15 days (from 1 to 15 June 2020), with a root mean square error (RMSE, MJ/m2) of 1.23, 1.38, 1.31, and 1.04, respectively, regarding the daily global solar irradiation. The interpolation of ambient temperature also performed well when applying the VWS concept, with an RMSE (°C) of 0.68 for the maximum temperature with an ANN of four hidden neurons, 0.58 for the average temperature with three hidden neurons, and 0.83 for the minimum temperature with four hidden neurons.
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... Although other related models may represent other scales, they represent the same concept of the ANNs developed here and the same purpose of obtaining data for temperature predictions. Franco et al. [27] found that there is a lack of such studies that use ANNs models and that focus on generating data in places where such data is not available, so that they can be used as inputs for other models [25]. However, some interesting studies are presented here. ...
... The methodologies and results of other estimation and prediction models present in various bibliographies [25,[27][28][29][30][31] allow us to consider the results obtained in this study valid and useful. In this specific case, the research has enabled the estimation of urban temperatures within selected urban green spaces in Valladolid, providing insights into their potential for mitigating urban heat. ...
Prediction of Daily Ambient Temperature and Its Hourly Estimation Using Artificial Neural Networks in Urban Allotment Gardens and in an Urban Park in Valladolid, Castilla y León, Spain.
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Cities exemplify the evolving world with changing demographics and climates. Urban green spaces play a crucial role in improving the quality of life of people through their potential to mitigate temperatures. Therefore, comprehending their impact is of para-mount interest. Given the challenges in obtaining temperature data from urban locations, this study develops Artificial Neural Networks (ANNs) to predict daily and hourly temperatures in Valladolid, Spain, with a particular focus on urban allotment gardens and a forested urban park. ANNs were built and evaluated from various combinations of inputs (X), hidden neurons (Y) and outputs (Z) under the practical rule of "making net-works simple, to obtain better results". The best performing model was 6-Y-1 ANN archi-tecture with an impressive result of Root Mean Square Error (RMSE) = 0.42°C in the urban garden called Valle de Arán. However, other five ANN architectures were also tested (7-Y-5; 6-Y-5; 7-Y-1; 3-Y-Z and 2-Y-1). ANNs dedicated to urban temperature analysis hold immense potential for urban planning and research, aiding in under-standing the urban climate, forecasting future temperatures, identifying temperature mitigation strategies and even for managing urban crops
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... These datasets include spatial (latitude, longitude), altitudinal, and temporal nonstationarities and so the values of parameters that are found in such datasets change over space and time. In meteorological datasets, the meteorological parameters' values (such as air temperature, humidity, pressure, etc.) change as the location, altitude, and time of the measurements change [2][3][4]. Air pollution datasets include particulate matter (PM) concentrations of a city over time and ecological datasets include net primary production values of forests or vegetation over time for different altitudes. Analyzing such datasets by considering their spatial, altitudinal, and temporal nonstationarities can reveal valuable information. ...
... .; mÞ and i represent a regression coefficient and an error term, respectively. The calculation formula of b ik is given in Eq. (2). ...
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... The elements in spaces where people connect with each other, such as microclimatic parameters, play a major role in determining individuals' emotional states [20]. However, people are not equally sensitive to physical factors like light, temperature, and noise, and these may influence emotions differently [21]. ...
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Understanding human emotions and behavior in closed environments is essential for creating more empathetic and humane spaces. Environmental factors, such as temperature, noise, and light, play a crucial role in influencing behavior, but individuals' emotional states are equally important and often go unnoticed. Artificial Intelligence of Behavior (AIoB) offers a novel approach that integrates environmental measurements with human emotions to create spatially adaptive processes that can influence behavior. In this article, we present a new human emotion sensor developed using video cameras and implemented on a System on Chip (SoC) development board. Our approach uses Convolutional Neural Networks (CNNs) to recognize the presence of emotions in enclosed spaces and generate parameters that can influence emotional states and behavior within an AIoB system. The research successfully integrates advanced CNN technology into a System on Chip (SoC) platform, allowing for real-time processing of video data. The versatility of utilizing an energy-efficient SoC extends its application to smart environments aimed at improving mental health. By employing algorithms capable of detecting emotional states across various individuals, the study enhances its effectiveness. Additionally, it identifies the best CNN operations tailored to the technical specifications of the devices involved. Thus, The development involves a three-step process: (i) collecting enough data to build a robust model, (ii) training the model and evaluating its performance using test values, and (iii) applying the model on the development board. Our study demonstrates the feasibility of using AIoB to recognize and respond to human emotions in closed areas. By integrating emotional cues with environmental measurements, our system can create more personalized and empathetic spaces that cater to the needs of individuals. Our approach could have significant implications for designing public spaces to promote well-being and emotional satisfaction.
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... Franco et al. [55] found that there is a lack of such studies that use ANNs models and that focus on generating data in places where such data is not available so that they can be used as inputs for other models. In this study, it was identified that there is a lack of scientific evidence that develops ANNs for urban temperatures, mainly focusing on urban green spaces such as urban gardens and urban parks from data obtained in situ for short periods of time. ...
Prediction of Daily Ambient Temperature and Its Hourly Estimation Using Artificial Neural Networks in Urban Allotment Gardens and an Urban Park in Valladolid, Castilla y León, Spain
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  • Dec 2023
Urban green spaces improve quality of life by mitigating urban temperatures. However, there are challenges in obtaining urban data to analyze and understand their influence. With the aim of developing innovative methodologies for this type of research, Artificial Neural Networks (ANNs) were developed to predict daily and hourly temperatures in urban green spaces from sensors placed in situ for 41 days. The study areas were four urban allotment gardens (with dynamic and productive vegetation) and a forested urban park in the city of Valladolid, Spain. ANNs were built and evaluated from various combinations of inputs (X), hidden neurons (Y), and outputs (Z) under the practical rule of "making networks simple, to obtain better results". Seven ANNs architectures were tested: 7-Y-5 (Y = 6, 7,. .. ., 8). The best-performing model was the 6-Y-1 ANN architecture with a Root Mean Square Error (RMSE) of 0.42 • C for the urban garden called Valle de Arán. The results demonstrated that from shorter data points obtained in situ, ANNs predictions achieve acceptable results and reflect the usefulness of the methodology. These predictions were more accurate in urban gardens than in urban parks, where the type of existing vegetation can be a decisive factor. This study can contribute to the development of a sustainable and smart city, and has the potential to be replicated in cities where the influence of urban green spaces on urban temperatures is studied with traditional methodologies.
View
... Franco et al. [55] found that there is a lack of such studies that use ANNs models and that focus on generating data in places where such data is not available so that they can be used as inputs for other models. In this study, it was identified that there is a lack of scientific evidence that develops ANNs for urban temperatures, mainly focusing on urban green spaces such as urban gardens and urban parks from data obtained in situ for short periods of time. ...
Prediction of Daily Ambient Temperature and Its Hourly Estimation Using Artificial Neural Networks in Urban Allotment Gardens and an Urban Park in Valladolid, Castilla y León, Spain
Article
Full-text available
  • Dec 2023
Urban green spaces improve quality of life by mitigating urban temperatures. However, there are challenges in obtaining urban data to analyze and understand their influence. With the aim of developing innovative methodologies for this type of research, Artificial Neural Networks (ANNs) were developed to predict daily and hourly temperatures in urban green spaces from sensors placed in situ for 41 days. The study areas were four urban allotment gardens (with dynamic and productive vegetation) and a forested urban park in the city of Valladolid, Spain. ANNs were built and evaluated from various combinations of inputs (X), hidden neurons (Y), and outputs (Z) under the practical rule of "making networks simple, to obtain better results". Seven ANNs architectures were tested: 7-Y-5 (Y = 6, 7,. .. ., 8). The best-performing model was the 6-Y-1 ANN architecture with a Root Mean Square Error (RMSE) of 0.42 • C for the urban garden called Valle de Arán. The results demonstrated that from shorter data points obtained in situ, ANNs predictions achieve acceptable results and reflect the usefulness of the methodology. These predictions were more accurate in urban gardens than in urban parks, where the type of existing vegetation can be a decisive factor. This study can contribute to the development of a sustainable and smart city, and has the potential to be replicated in cities where the influence of urban green spaces on urban temperatures is studied with traditional methodologies.
View
... Some researchers have confirmed the validity of some machine-learning imputation methods. For instance, random forest (RF) [34] and multi-layer perceptron neural networks (MLP) [35] have the ability to accurately reconstruct meteorological variables, particularly temperature. In the research by Taewon [36], two statistical imputation methods (linear and spline) and three machine-learning methods (multivariate linear regression (MLR), RF, and MLP) interpolated the missing values of seven meteorological variables, respectively. ...
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... The VWS provides the average monthly and annual precipitation (rainfall and snowfall), temperature (average temperature, freezing index, and freeze-thaw days), humidity (maximum and minimum), and wind speed for the sites. The VWS uses up to five nearby weather station data and the 1/R 2 weighting scheme interpolation technique to estimate climatological data without any physical station (Franco et al., 2020). The availability of the VWS data varies widely. ...
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View
... Consequently, RF is used in various prediction models. For example, Franco et al. used RF to validate their data [19], and Shen et al. found that RF outperformed other commonly used classifiers, such as the support vector machine and DT [20]. ...
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RETRACTED ARTICLE: An adaptive QoS computation for medical data processing in intelligent healthcare applications
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Unpredictability by local farmers, insufficiency and seasonality of rainfall have severely affected productivity of rain-fed agriculture in Cheleleka watershed. Thus, irrigated agriculture has a paramount role in lifesaving and insuring food security. Mismanagement of irrigation water influences agricultural productivity and efficiency of water uses. This study aimed to assess performance and potential of traditional surface irrigation schemes and to survey conflicts resulting from irrigation water use and management. Three major rivers in Cheleleka watershed (Wesha, Worka and Wodesa) where traditional irrigation has been widely practiced were selected purposively. The irrigable areas of each river were identified and categorized into upper, middle and lower parts based on its related location in the watershed. The soil samples, infiltration rate, long time meteorology, conveyance efficiency, irrigable areas and discharge at diversion points were analyzed. In addition, 49 households in irrigable areas of the three rivers were interviewed and three focus group discussions were held. About 0.16 and 0.14 m³/s of discharge were recorded in irrigation areas of the middle Wodesa and upper Worka rivers, respectively. The conveyance efficiency of the traditional surface irrigation canals at middle Wodesa and upper Wesha and Worka was less than 50%. The general potentials of surface irrigation water across the three perennial rivers were greater than 23 million cubic meters per year. The moisture stored in root zone or readily available water in existing farmers’ practices was found far beyond the carrying capacity of the soils, especially in middle Wodesa and upper Wesha and Worka areas. Performances of the traditional surface irrigation schemes are very low and had aggravated irrigation water losses and contributed to crop water scarcity and conflicts. The results also showed that farmers in the upper parts of the area and close to the diversion points excessively irrigate the cropland, whereas those far away and in lower watersheds of each river receive irrigation water insufficiently and irregularly. Even though there was irrigation management system/committee, the set principle was not respected.
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  • Jan 2020
  • FUTURE GENER COMP SY
Large volumes of mobility data is collected in various application domains. Enterprise applications are designed on the notion of centralised data control where the proprietary of the data rests with the enterprise and not with the user. This has consequences as evident by the occasional privacy breaches. Trajectory mining is an important data mining problem, however, trajectory data can disclose sensitive location information about users. In this work, we propose a decentralised blockchain-enabled privacy-preserving trajectory data mining framework where the proprietary of the data rests with the user and not with the enterprise. We formalise the privacy preservation in trajectory data mining settings, present a proposal for privacy preservation, and implement the solution as a proof-of-concept. A comprehensive experimental evaluation is conducted to assess the applicability of the system. The results show that the proposed system yields promising results for blockchain-enabled privacy preservation in user trajectory data.
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Artificial Intelligence-Driven Mechanism for Edge Computing-Based Industrial Applications
Article
  • Mar 2019
Due to various limitations i.e., computational complexity and more delay in cloud computing is overtaken by edge computing for efficient and fair resource allocation such as, power and battery lifetime in internet of things (IoT) based industrial applications. In the meantime intelligent and accurate resource management by artificial intelligence (AI) has become the center of attention especially in industrial applications. With the coordination of AI at the edge will remarkably enhance the range and computational speed of IoT based devices in industries. But the challenging issue in these power hungry, short battery lifetime and delay intolerant portable devices is inappropriate and inefficient classical trends of fair resource allotment. Also, it is interpreted through extensive industrial data sets that dynamic wireless channel could not be supported by the typical power saving and battery lifetime techniques for example, predictive transmission power control (PTPC) and Baseline. Thus, this paper proposes i) a forward central dynamic and available approach (FCDAA) by adapting the running time of sensing and transmission processes in IoT-based portable devices ii) a system-level battery model by evaluating the energy dissipation in IoT devices iii) a data reliability model for edge artificial intelligence based IoT devices over hybird TPC and duty-cycle network. Two important cases for instance, static (i.e.,product processing) and dynamic (i.e., vibration and fault diagnosis) are introduced for proper monitoring of industrial platform. Experimental test-bed reveals that proposed FCDAA enhances energy efficiency and battery lifetime at acceptable reliability (∼0.95) by appropriately tuning duty-cycle and TPC unlike conventional methods.
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Towards an Optimal Resource Management for IoT based Green and Sustainable Smart Cities
Article
  • Feb 2019
  • J CLEAN PROD
Internet of Things (IoT) is an emerging technology for the smart city that interconnects various digital devices through Internet, hence, providing multiple innovative facilities from academia to industry and healthcare to business. Smart city is the ubiquitous and a paradigm shift which has revolutionized the entire landscape with the support of information and communication technology (ICT), sensor-enabled IoT devices. For the better and big picture of the entire scenarios with high visibility multimedia (i.e., video, audio, text, and images) transmission is the soul-concept in the smart world, but due to resource-constrained (power hungry and limited battery lifetime) nature of these tiny devices (which are building blocks of smart city) and voluminous amount of the data it is very challenging to openly talk about the sustainable and Green smart city platform. Thus, to remedy these problems two Hybrid Adaptive Bandwidth and Power Algorithm (HABPA), and Delay-tolerant Streaming Algorithm (DSA) are proposed by adopting stored video stream titled, StarWarsIV. Besides, a novel architecture of smart city system is proposed. Experimental results are obtained and analyzed in terms of performance metrics i.e., power drain, battery lifetime, delay, standard deviation and packet loss ratio (PLR) in association to the buffer size. It is concluded that the HABPA (45%,37%,20 ms) significantly optimizes power drain, battery lifetime (37%), standard deviation (3.5 dB), PLR (4.5%) of the IoT-enabled devices with less delay than DSA (43%, 32%,25 ms, 5 dB, 5.75%) and Baseline (42%,28%, 30 ms, 6 dB, 6.53%) respectively during media transmission in smart city.
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An analytic computation-driven algorithm for Decentralized Multicore Systems
Article
  • Feb 2019
  • FUTURE GENER COMP SY
In the modern era, increasing numbers of cores per chip are applied for decentralized systems, but there is not any appropriate symbolic computation approach to construct multicore analytic approximation. Thus, it is essential to develop an efficient, simple and unified way for decentralized Adomian decomposition method to increase the potential speed of the multicore systems. In our paper, we present an innovative parallel algorithm of constructing analytic solutions for nonlinear differential system, which based on the Adomian–Rach double decomposition method and Rach's Adomian polynomials. Based on our algorithm, we further developed a user-friendly Python software package to construct analytic approximations of initial or boundary value problems. Finally, the scope of validity of our Python software package is illustrated by several different types of nonlinear examples. The obtained results demonstrate the effectiveness of our package by compared with exact solution and numeric method, the characteristics of each class of Adomian polynomials and the efficiency of parallel algorithm with multicore processors. We emphasis that the super-linear speedup may happens for the duration of constructing approximate solutions. So, it can be considered as a promising alternative algorithm of decentralized Adomian decomposition method for solving nonlinear problems in science and engineering.
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