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Spatiotemporal Prediction and Simulation Methods at the Nexus of Statistical Physics, Spatial Statistics and Machine Learning

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Statistical Physics".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 18211

Special Issue Editors

Dr. Dionissios T. Hristopulos

E-Mail Website
Guest Editor
School of Electrical & Computer Engineering, Technical University of Crete, 73100 Crete, Greece
Interests: statistical physics; space–time statistics; machine learning; hydrology; climate change; brain connectivity
Special Issues, Collections and Topics in MDPI journals
Dr. Emmanouil Varouchakis

E-Mail Website
Guest Editor
School of Mineral Resources Engineering, Technical University of Crete, 73100 Crete, Greece
Interests: space–time geostatistics; geosciences; stochastic methods; water resources; groundwater
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The connection between statistical mechanics and predictive statistical modeling was established in the seminal works of E. T. Jaynes several decades ago. However, the predictive modeling of complex space–time processes still provides exciting research problems for the scientific community. Addressing such problems involves developing first-principles approaches and parsimonious statistical models, finding practical solutions (theoretical or computational) for prediction and simulation, discovering pathways of information flow, downscaling Earth observation data to higher resolutions, and efficiently processing big spatiotemporal data sets.  Solutions to these problems are important for improved prediction of the state of environmental processes, climate change, the economy, and the way our brains process information, to name just a few among many applications.

In a broad sense, data-driven prediction and simulation provide quantitative probabilistic estimates of a physical process (or several interacting processes) at spatial locations and/or times where observations are unavailable, based on existing data. The aim of this Special Issue is to explore inter-disciplinary predictive approaches for spatiotemporal systems which combine ideas from statistical physics, space–time statistics, as well as statistical and machine learning.

Methodological, computational, and application-oriented contributions that advance the state of the art are suitable. Inter-disciplinary studies that lead to improved understanding and modeling flexibility as well as studies that provide enhanced predictive capabilities for space–time processes are also welcome. Application topics of interest include, but are not limited to, hydrological processes, epidemiology, environmental flows, climate, ecological processes, wind and solar energy, and analysis of brain signals.

Dr. Dionissios T. Hristopulos
Dr. Emmanouil Varouchakis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • complex space–time processes
  • non-gaussian, nonlinear dependence
  • big space–time data
  • statistical learning
  • deep learning neural networks
  • geostatistics
  • space–time correlations
  • applications of statistical physics to space-time prediction
  • climate change
  • entropy-based methods of causality analysis
  • space–time connectivity of complex systems

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Published Papers (7 papers)

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Research

23 pages, 35184 KiB  
Article
Multivariate Modeling for Spatio-Temporal Radon Flux Predictions
by Sandra De Iaco, Claudia Cappello, Antonella Congedi and Monica Palma
Entropy 2023, 25(7), 1104; https://doi.org/10.3390/e25071104 - 24 Jul 2023
Cited by 1 | Viewed by 1565
Abstract
Nowadays, various fields in environmental sciences require the availability of appropriate techniques to exploit the information given by multivariate spatial or spatio-temporal observations. In particular, radon flux data which are of high interest to monitor greenhouse gas emissions and to assess human exposure [...] Read more.
Nowadays, various fields in environmental sciences require the availability of appropriate techniques to exploit the information given by multivariate spatial or spatio-temporal observations. In particular, radon flux data which are of high interest to monitor greenhouse gas emissions and to assess human exposure to indoor radon are determined by the deposit of uranium and radio (precursor elements). Furthermore, they are also affected by various atmospheric variables, such as humidity, temperature, precipitation and evapotranspiration. To this aim, a significant role can be recognized to the tools of multivariate geostatistics which supports the modeling and prediction of variables under study. In this paper, the spatio-temporal distribution of radon flux densities over the Veneto Region (Italy) and its estimation at unsampled points in space and time are discussed. In particular, the spatio-temporal linear coregionalization model is identified on the basis of the joint diagonalization of the empirical covariance matrices evaluated at different spatio-temporal lags and is used to produce predicted radon flux maps for different months. Probability maps, that the radon flux density in the upcoming months is greater than three historical statistics, are then built. This might be of interest especially in summer months when the risk of radon exhalation is higher. Moreover, a comparison with respect to alternative models in the univariate and multivariate context is provided. Full article
(This article belongs to the Special Issue Spatiotemporal Prediction and Simulation Methods at the Nexus of Statistical Physics, Spatial Statistics and Machine Learning)
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17 pages, 1044 KiB  
Article
A Novel Trajectory Feature-Boosting Network for Trajectory Prediction
by Qingjian Ni, Wenqiang Peng, Yuntian Zhu and Ruotian Ye
Entropy 2023, 25(7), 1100; https://doi.org/10.3390/e25071100 - 23 Jul 2023
Viewed by 2160
Abstract
Trajectory prediction is an essential task in many applications, including autonomous driving, robotics, and surveillance systems. In this paper, we propose a novel trajectory prediction network, called TFBNet (trajectory feature-boosting network), that utilizes trajectory feature boosting to enhance prediction accuracy. TFBNet operates by [...] Read more.
Trajectory prediction is an essential task in many applications, including autonomous driving, robotics, and surveillance systems. In this paper, we propose a novel trajectory prediction network, called TFBNet (trajectory feature-boosting network), that utilizes trajectory feature boosting to enhance prediction accuracy. TFBNet operates by mapping the original trajectory data to a high-dimensional space, analyzing the change rules of the trajectory in this space, and finally aggregating the trajectory goals to generate the final trajectory. Our approach presents a new perspective on trajectory prediction. We evaluate TFBNet on five real-world datasets and compare it to state-of-the-art methods. Our results demonstrate that TFBNet achieves significant improvements in the ADE (average displacement error) and FDE (final displacement error) indicators, with increases of 46% and 52%, respectively. These results validate the effectiveness of our proposed approach and its potential to improve the performance of trajectory prediction models in various applications. Full article
(This article belongs to the Special Issue Spatiotemporal Prediction and Simulation Methods at the Nexus of Statistical Physics, Spatial Statistics and Machine Learning)
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16 pages, 2903 KiB  
Article
Flight Delay Regression Prediction Model Based on Att-Conv-LSTM
by Jingyi Qu, Min Xiao, Liu Yang and Wenkai Xie
Entropy 2023, 25(5), 770; https://doi.org/10.3390/e25050770 - 8 May 2023
Cited by 5 | Viewed by 3184
Abstract
Accurate prediction results can provide an excellent reference value for the prevention of large-scale flight delays. Most of the currently available regression prediction algorithms use a single time series network to extract features, with less consideration of the spatial dimensional information contained in [...] Read more.
Accurate prediction results can provide an excellent reference value for the prevention of large-scale flight delays. Most of the currently available regression prediction algorithms use a single time series network to extract features, with less consideration of the spatial dimensional information contained in the data. Aiming at the above problem, a flight delay prediction method based on Att-Conv-LSTM is proposed. First, in order to fully extract both temporal and spatial information contained in the dataset, the long short-term memory network is used for getting time characteristics, and a convolutional neural network is adopted for obtaining spatial features. Then, the attention mechanism module is added to improve the iteration efficiency of the network. Experimental results show that the prediction error of the Conv-LSTM model is reduced by 11.41 percent compared with the single LSTM, and the prediction error of the Att-Conv-LSTM model is reduced by 10.83 percent compared with the Conv-LSTM. It is proven that considering spatio-temporal characteristics can obtain more accurate prediction results in the flight delay problem, and the attention mechanism module can also effectively improve the model performance. Full article
(This article belongs to the Special Issue Spatiotemporal Prediction and Simulation Methods at the Nexus of Statistical Physics, Spatial Statistics and Machine Learning)
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32 pages, 22806 KiB  
Article
Spatial Prediction of Current and Future Flood Susceptibility: Examining the Implications of Changing Climates on Flood Susceptibility Using Machine Learning Models
by Navid Mahdizadeh Gharakhanlou and Liliana Perez
Entropy 2022, 24(11), 1630; https://doi.org/10.3390/e24111630 - 10 Nov 2022
Cited by 14 | Viewed by 3353
Abstract
The main aim of this study was to predict current and future flood susceptibility under three climate change scenarios of RCP2.6 (i.e., optimistic), RCP4.5 (i.e., business as usual), and RCP8.5 (i.e., pessimistic) employing four machine learning models, including Gradient Boosting Machine (GBM), Random [...] Read more.
The main aim of this study was to predict current and future flood susceptibility under three climate change scenarios of RCP2.6 (i.e., optimistic), RCP4.5 (i.e., business as usual), and RCP8.5 (i.e., pessimistic) employing four machine learning models, including Gradient Boosting Machine (GBM), Random Forest (RF), Multilayer Perceptron Neural Network (MLP-NN), and Naïve Bayes (NB). The study was conducted for two watersheds in Canada, namely Lower Nicola River, BC and Loup, QC. Three statistical metrics were used to validate the models: Receiver Operating Characteristic Curve, Figure of Merit, and F1-score. Findings indicated that the RF model had the highest accuracy in providing the flood susceptibility maps (FSMs). Moreover, the provided FSMs indicated that flooding is more likely to occur in the Lower Nicola River watershed than the Loup watershed. Following the RCP4.5 scenario, the area percentages of the flood susceptibility classes in the Loup watershed in 2050 and 2080 have changed by the following percentages from the year 2020 and 2050, respectively: Very Low = −1.68%, Low = −5.82%, Moderate = +6.19%, High = +0.71%, and Very High = +0.6% and Very Low = −1.61%, Low = +2.98%, Moderate = −3.49%, High = +1.29%, and Very High = +0.83%. Likewise, in the Lower Nicola River watershed, the changes between the years 2020 and 2050 and between the years 2050 and 2080 were: Very Low = −0.38%, Low = −0.81%, Moderate = −0.95%, High = +1.72%, and Very High = +0.42% and Very Low = −1.31%, Low = −1.35%, Moderate = −1.81%, High = +2.37%, and Very High = +2.1%, respectively. The impact of climate changes on future flood-prone places revealed that the regions designated as highly and very highly susceptible to flooding, grow in the forecasts for both watersheds. The main contribution of this study lies in the novel insights it provides concerning the flood susceptibility of watersheds in British Columbia and Quebec over time and under various climate change scenarios. Full article
(This article belongs to the Special Issue Spatiotemporal Prediction and Simulation Methods at the Nexus of Statistical Physics, Spatial Statistics and Machine Learning)
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45 pages, 4928 KiB  
Article
Machine Learning Methods for Multiscale Physics and Urban Engineering Problems
by Somya Sharma, Marten Thompson, Debra Laefer, Michael Lawler, Kevin McIlhany, Olivier Pauluis, Dallas R. Trinkle and Snigdhansu Chatterjee
Entropy 2022, 24(8), 1134; https://doi.org/10.3390/e24081134 - 16 Aug 2022
Viewed by 2303
Abstract
We present an overview of four challenging research areas in multiscale physics and engineering as well as four data science topics that may be developed for addressing these challenges. We focus on multiscale spatiotemporal problems in light of the importance of understanding the [...] Read more.
We present an overview of four challenging research areas in multiscale physics and engineering as well as four data science topics that may be developed for addressing these challenges. We focus on multiscale spatiotemporal problems in light of the importance of understanding the accompanying scientific processes and engineering ideas, where “multiscale” refers to concurrent, non-trivial and coupled models over scales separated by orders of magnitude in either space, time, energy, momenta, or any other relevant parameter. Specifically, we consider problems where the data may be obtained at various resolutions; analyzing such data and constructing coupled models led to open research questions in various applications of data science. Numeric studies are reported for one of the data science techniques discussed here for illustration, namely, on approximate Bayesian computations. Full article
(This article belongs to the Special Issue Spatiotemporal Prediction and Simulation Methods at the Nexus of Statistical Physics, Spatial Statistics and Machine Learning)
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13 pages, 462 KiB  
Article
Estimation of the Covariance Matrix in Hierarchical Bayesian Spatio-Temporal Modeling via Dimension Expansion
by Bin Sun and Yuehua Wu
Entropy 2022, 24(4), 492; https://doi.org/10.3390/e24040492 - 31 Mar 2022
Cited by 1 | Viewed by 1864
Abstract
Ozone concentrations are key indicators of air quality. Modeling ozone concentrations is challenging because they change both spatially and temporally with complicated structures. Missing data bring even more difficulties. One of our interests in this paper is to model ozone concentrations in a [...] Read more.
Ozone concentrations are key indicators of air quality. Modeling ozone concentrations is challenging because they change both spatially and temporally with complicated structures. Missing data bring even more difficulties. One of our interests in this paper is to model ozone concentrations in a region in the presence of missing data. We propose a method without any assumptions on the correlation structure to estimate the covariance matrix through a dimension expansion method for modeling the semivariograms in nonstationary fields based on the estimations from the hierarchical Bayesian spatio-temporal modeling technique (Le and Zidek). Further, we apply an entropy criterion (Jin et al.) based on a predictive model to decide if new stations need to be added. This entropy criterion helps to solve the environmental network design problem. For demonstration, we apply the method to the ozone concentrations at 25 stations in the Pittsburgh region studied. The comparison of the proposed method and the one is provided through leave-one-out cross-validation, which shows that the proposed method is more general and applicable. Full article
(This article belongs to the Special Issue Spatiotemporal Prediction and Simulation Methods at the Nexus of Statistical Physics, Spatial Statistics and Machine Learning)
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21 pages, 1723 KiB  
Article
Spatial Modeling of Precipitation Based on Data-Driven Warping of Gaussian Processes
by Vasiliki D. Agou, Andrew Pavlides and Dionissios T. Hristopulos
Entropy 2022, 24(3), 321; https://doi.org/10.3390/e24030321 - 23 Feb 2022
Cited by 10 | Viewed by 2393
Abstract
Modeling and forecasting spatiotemporal patterns of precipitation is crucial for managing water resources and mitigating water-related hazards. Globally valid spatiotemporal models of precipitation are not available. This is due to the intermittent nature, non-Gaussian distribution, and complex geographical dependence of precipitation processes. Herein [...] Read more.
Modeling and forecasting spatiotemporal patterns of precipitation is crucial for managing water resources and mitigating water-related hazards. Globally valid spatiotemporal models of precipitation are not available. This is due to the intermittent nature, non-Gaussian distribution, and complex geographical dependence of precipitation processes. Herein we propose a data-driven model of precipitation amount which employs a novel, data-driven (non-parametric) implementation of warped Gaussian processes. We investigate the proposed warped Gaussian process regression (wGPR) using (i) a synthetic test function contaminated with non-Gaussian noise and (ii) a reanalysis dataset of monthly precipitation from the Mediterranean island of Crete. Cross-validation analysis is used to establish the advantages of non-parametric warping for the interpolation of incomplete data. We conclude that wGPR equipped with the proposed data-driven warping provides enhanced flexibility and—at least for the cases studied– improved predictive accuracy for non-Gaussian data. Full article
(This article belongs to the Special Issue Spatiotemporal Prediction and Simulation Methods at the Nexus of Statistical Physics, Spatial Statistics and Machine Learning)
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