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Recent Topics of Climate Vulnerability: Statistics, Machine Learning, and Data...
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Recent Topics of Climate Vulnerability: Statistics, Machine Learning, and Data Science, from Theory to Application

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Climatology".

Deadline for manuscript submissions: closed (9 December 2022) | Viewed by 17817

Special Issue Editors

Dr. Rezzy Eko Caraka

E-Mail Website
Guest Editor
1. The National Research and Innovation Agency (BRIN), Jakarta Pusat 10340, Indonesia
2. Laboratory Hierarchical Likelihood, Department of Statistics, Seoul National University, Seoul 08826, Korea
Interests: machine learning; SDGs; large-scale optimization; fast computing; spatial statistics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Youngjo Lee

E-Mail Website
Guest Editor
Laboratory Hierarchical Likelihood, Department of Statistics, Seoul National University, Seoul 08826, Korea
Interests: extension and application of hierarchical GLMs and (hierarchical or h) likelihood theory; software development for random effect models; their applications to genetics; image and quality improvements
Special Issues, Collections and Topics in MDPI journals
Dr. Toni Toharudin

E-Mail Website
Guest Editor
Department of Statistics, Padjadjaran University, Jawa Barat 45363, Indonesia
Interests: statistics; social research; structural equation model
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rung-Ching Chen

E-Mail Website
Guest Editor
Department of Information Management, Chaoyang University of Technology, Taichung 413310, Taiwan
Interests: network technology; pattern recognition and machine learning; IoT and data analysis; applications of artificial intelligence; deep learning; generative adversarial networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Heri Kuswanto

E-Mail Website
Guest Editor
Department of Statistics, Institut Teknologi Sepuluh Nopember, Jawa Timur 60111, Republic of Indonesia
Interests: extreme weather and climate; statistical computing; data science
Prof. Dr. Maengseok Noh

E-Mail Website
Guest Editor
Department of Statistics, Pukyong National University, Busan 608737, Korea
Interests: statistics; machine learning; data science

Special Issue Information

Dear Colleagues,

We invite researchers to contribute original research and review articles dealing with climate vulnerability and how statistics, machine learning, and data science can be used as tools to provide good solutions to penta-helix collaboration.

Topics of interest include but are not limited to:

  • Measuring climate vulnerability, pollution, air quality, and environmental issues;
  • Implementation of recent statistics, machine learning, and data science in climate issues;
  • Integrating statistics in penta-helix collaboration;
  • Addressing the implication of climate change to economics, socio-culture, gender equality, and other SDG topics.

Dr. Rezzy Eko Caraka
Prof. Dr. Youngjo Lee
Dr. Toni Toharudin
Prof. Dr. Rung-Ching Chen
Prof. Dr. Heri Kuswanto
Prof. Dr. Maengseok Noh
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. Atmosphere 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 2400 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

  • climate vulnerability
  • machine learning
  • data science
  • statistics
  • sustainable development goals
  • disaster risk
  • pollution

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

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23 pages, 27485 KiB  
Article
Urban Wind Corridors Analysis via Network Theory
by Ido Nevat and Ayu Sukma Adelia
Atmosphere 2023, 14(3), 572; https://doi.org/10.3390/atmos14030572 - 16 Mar 2023
Cited by 1 | Viewed by 1855
Abstract
We develop a new model for urban wind corridors analysis and detection of urban wind ventilation potential based on concepts and principles of network theory. Our approach is based solely on data extracted from spatial urban features that are easily obtained from a [...] Read more.
We develop a new model for urban wind corridors analysis and detection of urban wind ventilation potential based on concepts and principles of network theory. Our approach is based solely on data extracted from spatial urban features that are easily obtained from a 3D model of the city. Once the spatial features have been extracted, we embed them onto a graph topology. This allows us to use theories and techniques of network theory, and in particular graph theory. Utilizing such techniques, we perform end-to-end network flow analysis of the wind potential across the city and, in particular, estimate the locations, strengths, and paths of the wind corridors. To calibrate our model, we use a dataset generated by a meso-scale climate model and estimate the model parameters by projecting the wind vector field of the climate model onto a graph, thus providing a meaningful comparison of the two models under a new metric. We illustrate our modeling approach on the city of Singapore and explain how the results are useful for climate-informed urban design. Full article
(This article belongs to the Special Issue Recent Topics of Climate Vulnerability: Statistics, Machine Learning, and Data Science, from Theory to Application)
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15 pages, 11199 KiB  
Article
Evolving Hybrid Generalized Space-Time Autoregressive Forecasting with Cascade Neural Network Particle Swarm Optimization
by Toni Toharudin, Rezzy Eko Caraka, Hasbi Yasin and Bens Pardamean
Atmosphere 2022, 13(6), 875; https://doi.org/10.3390/atmos13060875 - 27 May 2022
Cited by 3 | Viewed by 2468
Abstract
Background: The generalized space-time autoregressive (GSTAR) model is one of the most widely used models for modeling and forecasting time series and location data. Methods: In the GSTAR model, there is an assumption that the research locations are heterogeneous. In addition, the differences [...] Read more.
Background: The generalized space-time autoregressive (GSTAR) model is one of the most widely used models for modeling and forecasting time series and location data. Methods: In the GSTAR model, there is an assumption that the research locations are heterogeneous. In addition, the differences between these locations are shown in the form of a weighting matrix. The novelty of this paper is that we propose the hybrid time-series model of GSTAR uses the cascade neural network and obtains the best parameters from particle swarm optimization. Results and conclusion: This hybrid model provides a high accuracy value for forecasting PM2.5, PM10, NOx, and SO2 with high accuracy forecasting, which is justified by a mean absolute percentage error (MAPE) accuracy of around 0.01%. Full article
(This article belongs to the Special Issue Recent Topics of Climate Vulnerability: Statistics, Machine Learning, and Data Science, from Theory to Application)
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24 pages, 7868 KiB  
Article
Malaysia PM10 Air Quality Time Series Clustering Based on Dynamic Time Warping
by Fatin Nur Afiqah Suris, Mohd Aftar Abu Bakar, Noratiqah Mohd Ariff, Mohd Shahrul Mohd Nadzir and Kamarulzaman Ibrahim
Atmosphere 2022, 13(4), 503; https://doi.org/10.3390/atmos13040503 - 22 Mar 2022
Cited by 20 | Viewed by 4645
Abstract
Air quality monitoring is important in the management of the environment and pollution. In this study, time series of PM10 from air quality monitoring stations in Malaysia were clustered based on similarity in terms of time series patterns. The identified clusters were [...] Read more.
Air quality monitoring is important in the management of the environment and pollution. In this study, time series of PM10 from air quality monitoring stations in Malaysia were clustered based on similarity in terms of time series patterns. The identified clusters were analyzed to gain meaningful information regarding air quality patterns in Malaysia and to identify characterization for each cluster. PM10 time series data from 5 July 2017 to 31 January 2019, obtained from the Malaysian Department of Environment and Dynamic Time Warping as the dissimilarity measure were used in this study. At the same time, k-Means, Partitioning Around Medoid, agglomerative hierarchical clustering, and Fuzzy k-Means were the algorithms used for clustering. The results portray that the categories and activities of locations of the monitoring stations do not directly influence the pattern of the PM10 values, instead, the clusters formed are mainly influenced by the region and geographical area of the locations. Full article
(This article belongs to the Special Issue Recent Topics of Climate Vulnerability: Statistics, Machine Learning, and Data Science, from Theory to Application)
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28 pages, 1133 KiB  
Article
Urban Climate Risk Mitigation via Optimal Spatial Resource Allocation
by Ido Nevat and Muhammad Omer Mughal
Atmosphere 2022, 13(3), 439; https://doi.org/10.3390/atmos13030439 - 8 Mar 2022
Cited by 3 | Viewed by 2064
Abstract
Decision makers (DMs) who are involved in urban planning are often required to allocate finite resources (say, money) to improve outdoor thermal comfort (OTC) levels in a region (e.g., city, canton, country). In this paper, for the first time, we address the following [...] Read more.
Decision makers (DMs) who are involved in urban planning are often required to allocate finite resources (say, money) to improve outdoor thermal comfort (OTC) levels in a region (e.g., city, canton, country). In this paper, for the first time, we address the following two questions, which are directly related to this requirement: (1) How can the statistical properties of the spatial risk profile of an urban area from an OTC perspective be quantified, no matter which OTC index the DM chooses to use? (2) Given the risk profile, how much and where should the DM allocate the finite resources to improve the OTC levels? We answer these fundamental questions by developing a new and rigorous mathematical framework as well as a new class of models for spatial risk models. Our approach is based on methods from machine learning: first, a surrogate model of the OTC index that provides both accuracy and mathematical tractability is developed via regression analysis. Next, we incorporate the imperfect climate model and derive the statistical properties of the OTC index. We present the concept of spatio-temporal aggregate risk (STAR) measures and derive their statistical properties. Finally, building on our derivations, we develop a new algorithm for spatial resource allocation, which is useful for DMs and is based on modern portfolio theory. We implemented the tool and used it to illustrate its operation on a practical case of the large-scale area of Singapore using a WRF climate model. Full article
(This article belongs to the Special Issue Recent Topics of Climate Vulnerability: Statistics, Machine Learning, and Data Science, from Theory to Application)
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21 pages, 2847 KiB  
Article
Statistical Modeling of RPCA-FCM in Spatiotemporal Rainfall Patterns Recognition
by Siti Mariana Che Mat Nor, Shazlyn Milleana Shaharudin, Shuhaida Ismail, Sumayyah Aimi Mohd Najib, Mou Leong Tan and Norhaiza Ahmad
Atmosphere 2022, 13(1), 145; https://doi.org/10.3390/atmos13010145 - 16 Jan 2022
Cited by 6 | Viewed by 2160
Abstract
This study was conducted to identify the spatiotemporal torrential rainfall patterns of the East Coast of Peninsular Malaysia, as it is the region most affected by the torrential rainfall of the Northeast Monsoon season. Dimension reduction, such as the classical Principal Components Analysis [...] Read more.
This study was conducted to identify the spatiotemporal torrential rainfall patterns of the East Coast of Peninsular Malaysia, as it is the region most affected by the torrential rainfall of the Northeast Monsoon season. Dimension reduction, such as the classical Principal Components Analysis (PCA) coupled with the clustering approach, is often applied to reduce the dimension of the data while simultaneously performing cluster partitions. However, the classical PCA is highly insensitive to outliers, as it assigns equal weights to each set of observations. Hence, applying the classical PCA could affect the cluster partitions of the rainfall patterns. Furthermore, traditional clustering algorithms only allow each element to exclusively belong to one cluster, thus observations within overlapping clusters of the torrential rainfall datasets might not be captured effectively. In this study, a statistical model of torrential rainfall pattern recognition was proposed to alleviate these issues. Here, a Robust PCA (RPCA) based on Tukey’s biweight correlation was introduced and the optimum breakdown point to extract the number of components was identified. A breakdown point of 0.4 at 85% cumulative variance percentage efficiently extracted the number of components to avoid low-frequency variations or insignificant clusters on a spatial scale. Based on the extracted components, the rainfall patterns were further characterized based on cluster solutions attained using Fuzzy C-means clustering (FCM) to allow data elements to belong to more than one cluster, as the rainfall data structure permits this. Lastly, data generated using a Monte Carlo simulation were used to evaluate the performance of the proposed statistical modeling. It was found that the proposed RPCA-FCM performed better using RPCA-FCM compared to the classical PCA coupled with FCM in identifying the torrential rainfall patterns of Peninsular Malaysia’s East Coast. Full article
(This article belongs to the Special Issue Recent Topics of Climate Vulnerability: Statistics, Machine Learning, and Data Science, from Theory to Application)
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Review

Jump to: Research

31 pages, 2765 KiB  
Review
Parameter Optimisation-Based Hybrid Reference Evapotranspiration Prediction Models: A Systematic Review of Current Implementations and Future Research Directions
by Hadeel E. Khairan, Salah L. Zubaidi, Yousif Raad Muhsen and Nadhir Al-Ansari
Atmosphere 2023, 14(1), 77; https://doi.org/10.3390/atmos14010077 - 30 Dec 2022
Cited by 18 | Viewed by 2690
Abstract
A hybrid machine learning (ML) model is becoming a common trend in predicting reference evapotranspiration (ETo) research. This study aims to systematically review ML models that are integrated with meta-heuristic algorithms (i.e., parameter optimisation-based hybrid models, OBH) for predicting ETo data. Over five [...] Read more.
A hybrid machine learning (ML) model is becoming a common trend in predicting reference evapotranspiration (ETo) research. This study aims to systematically review ML models that are integrated with meta-heuristic algorithms (i.e., parameter optimisation-based hybrid models, OBH) for predicting ETo data. Over five years, from 2018–2022, the articles published in three reliable databases, including Web of Science, ScienceDirect, and IEEE Xplore, were considered. According to the protocol search, 1485 papers were selected. After three filters were applied, the final set contained 33 papers related to the nominated topic. The final set of papers was categorised into five groups. The first group, swarm intelligence-based algorithms, had the highest proportion of papers, (23/33) and was superior to all other algorithms. The second group (evolution computation-based algorithms), third group (physics-based algorithms), fourth group (hybrid-based algorithms), and fifth group (reviews and surveys) had (4/33), (1/33), (2/33), and (3/33), respectively. However, researchers have not treated OBH models in much detail, and there is still room for improvement by investigating both newly single and hybrid meta-heuristic algorithms. Finally, this study hopes to assist researchers in understanding the options and gaps in this line of research. Full article
(This article belongs to the Special Issue Recent Topics of Climate Vulnerability: Statistics, Machine Learning, and Data Science, from Theory to Application)
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