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Applications of Big Data in Global Environmental Predictions

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

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 10183

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Special Issue Editor

Dr. Yousuke Yamashita

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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

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Keywords

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

Published Papers (4 papers)

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Research

18 pages, 1020 KiB

Open AccessArticle

Cloud Computing Based on Computational Characteristics for Disaster Monitoring

by Quan Zou, Guoqing Li and Wenyang Yu

Appl. Sci. 2020, 10(19), 6676; https://doi.org/10.3390/app10196676 - 24 Sep 2020

Cited by 8 | Viewed by 1848

Abstract

Resources related to remote-sensing data, computing, and models are scattered globally. The use of remote-sensing images for disaster-monitoring applications is data-intensive and involves complex algorithms. These characteristics make the timely and rapid processing of disaster-monitoring applications challenging and inefficient. Cloud computing provides a dynamically scalable resource over the Internet. The rapid development of cloud computing has led to an increase in the computational performance of data-intensive computing, providing powerful throughput by distributing computation across many distributed computers. However, the use of current cloud computing models in scientific applications using remote-sensing image data has been limited to a single image-processing algorithm rather than a well-established model and method. This poses problems for the development of complex disaster-monitoring applications on cloud platform architectures. For example, distributed computing strategies and remote-sensing image-processing algorithms are highly coupled and not reusable. The aims of this paper are to identify computational characteristics of various disaster-monitoring algorithms and classify them according to different computational characteristics; explore a reusable processing model based on the MapReduce programming model for disaster-monitoring applications; and then establish a programming model for each type of algorithm. This approach provides a simpler programming method for programmers to implement disaster-monitoring applications. Finally, some examples are given to explain the proposed method and test its performance. Full article

(This article belongs to the Special Issue Applications of Big Data in Global Environmental Predictions)

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13 pages, 1601 KiB

Open AccessArticle

Spatial Interpolation of GNSS Troposphere Wet Delay by a Newly Designed Artificial Neural Network Model

by Mahmut Oguz Selbesoglu

Appl. Sci. 2019, 9(21), 4688; https://doi.org/10.3390/app9214688 - 4 Nov 2019

Cited by 12 | Viewed by 2618

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 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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21 pages, 3503 KiB

Open AccessArticle

Evolution of Burned Area in Forest Fires under Climate Change Conditions in Southern Spain Using ANN

by Julio Pérez-Sánchez, Patricia Jimeno-Sáez, Javier Senent-Aparicio, José María Díaz-Palmero and Juan de Dios Cabezas-Cerezo

Appl. Sci. 2019, 9(19), 4155; https://doi.org/10.3390/app9194155 - 3 Oct 2019

Cited by 14 | Viewed by 3334

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 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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18 pages, 3617 KiB

Open AccessArticle

NewApproach to Predict the Motion Characteristics of Single Bubbles in Still Water

by Bin Deng, Ren Jie Chin, Yao Tang, Changbo Jiang and Sai Hin Lai

Appl. Sci. 2019, 9(19), 3981; https://doi.org/10.3390/app9193981 - 23 Sep 2019

Cited by 14 | Viewed by 2059

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 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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