Special Issue "Applications of Big Data in Global Environmental Predictions"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences and Geography".

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editor

Dr. Yousuke Yamashita
Website
Guest Editor
National Institute for Environmental Studies, Ibaraki 305-0053, Japan
Interests: environmental prediction; atmospheric science; numerical modeling; machine-learning

Special Issue Information

Dear Colleagues,

Predictions of the global environment are a significant topic of environmental sciences, which aims to improve our knowledge of adaptation strategies for the future environment as well as improving prediction skill and mitigation strategies of environmental problems such as climate change, air pollution, and ocean acidification. The applications of global environmental predictions have recently improved in connection with “big data” such as data science, machine learning, and numerical modeling. By considering these recent improvements, this Special Issue invites papers that use big data to solve the problems related to global environmental prediction. Studies from specific areas of environmental science and interdisciplinary studies, covering, e.g., atmospheric science, biology, chemistry, ecology, and oceanography, are highly welcome.

Dr. Yousuke Yamashita
Guest Editor

Manuscript Submission Information

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Keywords

  • global environment
  • big data
  • data science
  • machine-learning
  • numerical modeling
  • prediction skill
  • interdisciplinary study

Published Papers (3 papers)

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Research

Open AccessArticle
Spatial Interpolation of GNSS Troposphere Wet Delay by a Newly Designed Artificial Neural Network Model
Appl. Sci. 2019, 9(21), 4688; https://doi.org/10.3390/app9214688 - 04 Nov 2019
Cited by 1
Abstract
Global Navigation Satellite System (GNSS) signals arrive at the Earth in a nonlinear and slightly curved way due to the refraction effect caused by the troposphere. The troposphere delay of the GNSS signal consists of hydrostatic and wet parts. In particular, tropospheric wet [...] Read more.
Global Navigation Satellite System (GNSS) signals arrive at the Earth in a nonlinear and slightly curved way due to the refraction effect caused by the troposphere. The troposphere delay of the GNSS signal consists of hydrostatic and wet parts. In particular, tropospheric wet delay prediction and interpolation are more difficult than those of the dry component due to the rapid temporal and spatial variation of the water vapor content. Wet delay estimation and interpolation with a sufficient accuracy is an important issue for all parameters obtained by GNSS positioning techniques. In particular, in real-time positioning applications, errors caused by interpolation of the wet troposphere delay are reflected in the height component of about 1 to 2 cm. Furthermore, the amount of water vapor in the troposphere is very important information in weather forecast applications obtained as a function of wet delay. Therefore, real-time monitoring of the troposphere can be achieved with a higher resolution and accuracy by utilizing neural network models for interpolation of the wet tropospheric delay. In addition, in the absence of the GNSS station, wet delays can be interpolated by means of the surrounding stations to the desired location. In this study, a back propagation artificial neural network (BPNN) model based on meteorological parameters obtained from The New Austrian Meteorological Measuring Network (TAWES) was used to interpolate wet troposphere delay. Analysis was carried out at 40 reference stations of the Echtzeit Positionierung Austria (EPOSA) GNSS Network covering the whole of Austria. The interpolation of zenith wet delays based on the artificial neural network was performed by using latitude, longitude, altitude and meteorological parameters (temperature, pressure, weighted mean temperature, and water vapor pressure). These parameters were then subtracted from the artificial neural network model one by one and six different artificial neural networks were designed. In addition, the linear interpolation method (LIN) and inverse distance weighted interpolation method (IDW) were used as conventional interpolation methods. In order to investigate the effect of the network density on interpolation methods, three networks, including 40, 30, and 20 reference stations, were formed and the increased distance effect on interpolation methods was evaluated. In addition, analyses were conducted in winter, spring, and summer to evaluate the seasonal effects on interpolation methods. According to the statistical analysis, the root mean square error (RMSE) values of the IDW, LIN, and BPNN methods were found to be 12.6, 13.4, and 5.9 mm, respectively. Full article
(This article belongs to the Special Issue Applications of Big Data in Global Environmental Predictions)
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Open AccessArticle
Evolution of Burned Area in Forest Fires under Climate Change Conditions in Southern Spain Using ANN
Appl. Sci. 2019, 9(19), 4155; https://doi.org/10.3390/app9194155 - 03 Oct 2019
Abstract
Wildfires in Mediterranean regions have become a serious problem, and it is currently the main cause of forest loss. Numerous prediction methods have been applied worldwide to estimate future fire activity and area burned in order to provide a stable basis for future [...] Read more.
Wildfires in Mediterranean regions have become a serious problem, and it is currently the main cause of forest loss. Numerous prediction methods have been applied worldwide to estimate future fire activity and area burned in order to provide a stable basis for future allocation of fire-fighting resources. The present study investigated the performance of an artificial neural network (ANN) in burned area size prediction and to assess the evolution of future wildfires and the area concerned under climate change in southern Spain. The study area comprised 39.41 km2 of land burned from 2000 to 2014. ANNs were used in two subsequential phases: classifying the size of the wildfires and predicting the burned surface for fires larger than 30,000 m2. Matrix of confusion and 10-fold cross-validations were used to evaluate ANN classification and mean absolute deviation, root mean square error, mean absolute percent error and bias, which were the metrics used for burned area prediction. The success rate achieved was above 60–70% depending on the zone. An average temperature increase of 3 °C and a 20% increase in wind speed during 2071–2100 results in a significant increase of the number of fires, up to triple the current figure, resulting in seven times the average yearly burned surface depending on the zone and the climate change scenario. Full article
(This article belongs to the Special Issue Applications of Big Data in Global Environmental Predictions)
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Open AccessArticle
NewApproach to Predict the Motion Characteristics of Single Bubbles in Still Water
Appl. Sci. 2019, 9(19), 3981; https://doi.org/10.3390/app9193981 - 23 Sep 2019
Abstract
Under the action of gravity, buoyancy, and surface tension, bubbles generated by wave breaking will rupture and polymerize, causing the occurrence of high-speed jets and strong turbulence in nearby water bodies, which in turn affects sea–air exchange, sediment transport, and pollutant movement. These [...] Read more.
Under the action of gravity, buoyancy, and surface tension, bubbles generated by wave breaking will rupture and polymerize, causing the occurrence of high-speed jets and strong turbulence in nearby water bodies, which in turn affects sea–air exchange, sediment transport, and pollutant movement. These interactions are closely related to the shape and velocity changes in single bubbles. Therefore, understanding the motion characteristics of single bubbles is essential. In this research, a large number of experiments were carried out to serve this purpose. The experimental data were used to develop three machine learning models for the bubble final velocity, bubble drag coefficient, and bubble shape, respectively. The performance of the feed forward back propagation neural network (FBNN) models for the final velocity and drag coefficient were evaluated. The coefficient of determination (R2) and root mean squared error (RMSE) value of final velocity prediction model was recorded at 0.83 and 0.0518, respectively. Meanwhile, for the drag coefficient prediction model, the values are 0.92 for R2 and 0.1534 for RMSE. The models can provide a more accurate output if compared to that from the empirical formulas. K-nearest neighbours (KNN), logistic regression, and random forest were applied as the algorithm while developing the bubble shape classification model. The best performance is achieved by the logistic regression. Full article
(This article belongs to the Special Issue Applications of Big Data in Global Environmental Predictions)
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