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Mathematics
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Advanced Statistical Techniques in Oceans and Climate Research
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Advanced Statistical Techniques in Oceans and Climate Research

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: 30 May 2024 | Viewed by 6283

Special Issue Editors

Prof. Dr. Zhihua Zhang

E-Mail Website
Guest Editor
School of Mathematics, Shandong University, Jinan 250100, China
Interests: big data; fractals; climate change; environmental evolution
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. M. James C. Crabbe

E-Mail Website
Guest Editor
Wolfson College, Oxford University, Oxford OX2 6UD, UK
Interests: statistical models; climate change; mathematical biology; marine ecosystem
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Earth’s climate and oceans are complex, multidimensional, multiscale stochastic processes in which different physical processes interact on different temporal and spatial scales. Statisticians are working to extract meaningful information from huge amounts of Earth observational and simulation data using various statistical techniques (e.g., EOF, DEA, EVA, CCA, SSA, PCA, MCMC, DFA, IPTA, Bayesian decisions, downscaling analyses, data assimilation, wavelet/framelet, spectral analyses, information entropy, and stochastic networks). Rapid advances in statistical techniques have reached all aspects of atmospheric, oceanic, and climate sciences. At present, the ensemble statistical learning techniques are being developed to deal with the emerging big Earth datasets, which cannot be analyzed deeply using classic statistical techniques due to the size, variety, and dynamic nature of big data. In this Special Issue, we aim to collect recent results on developing and applying advanced statistical techniques to reveal trends and patterns of climate/ocean evolution, determine the statistical correlation between climate/ocean systems and ecosystems, and evaluate the uncertainty of climate/ocean models, as well as estimate anthropogenic carbon emissions and land/marine carbon sinks.

Prof. Dr. Zhihua Zhang
Prof. Dr. M. James C. Crabbe
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. Mathematics is an international peer-reviewed open access semimonthly 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

  • interdisciplinary statistical methods
  • climatic time series analysis
  • oceanic time series analysis
  • hydrological time series analysis
  • statistical learning/machine learning
  • data assimilation and downscaling

Published Papers (4 papers)

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Research

18 pages, 7226 KiB  
Article
Optimization Hybrid of Multiple-Lag LSTM Networks for Meteorological Prediction
by Lin Zhu, Zhihua Zhang, M. James C. Crabbe and Lipon Chandra Das
Mathematics 2023, 11(22), 4603; https://doi.org/10.3390/math11224603 - 10 Nov 2023
Viewed by 1242
Abstract
Residences in poor regions always depend on rain-fed agriculture, so they urgently need suitable tools to make accurate meteorological predictions. Unfortunately, meteorological observations in these regions are usually sparse and irregularly distributed. Conventional LSTM networks only handle temporal sequences and cannot utilize the [...] Read more.
Residences in poor regions always depend on rain-fed agriculture, so they urgently need suitable tools to make accurate meteorological predictions. Unfortunately, meteorological observations in these regions are usually sparse and irregularly distributed. Conventional LSTM networks only handle temporal sequences and cannot utilize the links of meteorological variables among stations. GCN-LSTM networks only capture local spatial structures through the simple structures of fixed adjacency matrices, and the CNN-LSTM can only mine gridded meteorological observations for further predictions. In this study, we propose an optimization hybrid of multiple-lag LSTM networks for meteorological predictions. Our model can make full use of observed data at partner stations under different time-lag windows and strong links among the local observations of meteorological variables to produce future predictions. Numerical experiments on the meteorological predictions of Bangladesh demonstrate that our networks are superior to the classic LSTM and its variants GCN-LSTM and CNN-LSTM, as well as the SVM and DT. Full article
(This article belongs to the Special Issue Advanced Statistical Techniques in Oceans and Climate Research)
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13 pages, 376 KiB  
Article
Development of Grey Machine Learning Models for Forecasting of Energy Consumption, Carbon Emission and Energy Generation for the Sustainable Development of Society
by Akash Saxena, Ramadan A. Zeineldin and Ali Wagdy Mohamed
Mathematics 2023, 11(6), 1505; https://doi.org/10.3390/math11061505 - 20 Mar 2023
Cited by 5 | Viewed by 1362
Abstract
Energy is an important denominator for evaluating the development of any country. Energy consumption, energy production and steps towards obtaining green energy are important factors for sustainable development. With the advent of forecasting technologies, these factors can be accessed earlier, and the planning [...] Read more.
Energy is an important denominator for evaluating the development of any country. Energy consumption, energy production and steps towards obtaining green energy are important factors for sustainable development. With the advent of forecasting technologies, these factors can be accessed earlier, and the planning path for sustainable development can be chalked out. Forecasting technologies pertaining to grey systems are in the spotlight due to the fact that they do not require many data points. In this work, an optimized model with grey machine learning architecture of a polynomial realization was employed to predict power generation, power consumption and CO2 emissions. A nonlinear kernel was taken and optimized with a recently published algorithm, the augmented crow search algorithm (ACSA), for prediction. It was found that as compared to conventional grey models, the proposed framework yields better results in terms of accuracy. Full article
(This article belongs to the Special Issue Advanced Statistical Techniques in Oceans and Climate Research)
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10 pages, 1844 KiB  
Article
The Hybrid of Multilayer Perceptrons: A New Geostatistical Tool to Generate High-Resolution Climate Maps in Developing Countries
by Yue Han, Zhihua Zhang and Fekadu Tadege Kobe
Mathematics 2023, 11(5), 1239; https://doi.org/10.3390/math11051239 - 04 Mar 2023
Cited by 2 | Viewed by 990
Abstract
The ability to produce high-resolution climate maps is crucial for assessing climate change impacts and mitigating climate disasters and risks in developing countries. Mainstream geostatistical downscaling techniques use spatial interpolation or multi-linear regression models to produce high-resolution climate maps in data-scarce regions. Since [...] Read more.
The ability to produce high-resolution climate maps is crucial for assessing climate change impacts and mitigating climate disasters and risks in developing countries. Mainstream geostatistical downscaling techniques use spatial interpolation or multi-linear regression models to produce high-resolution climate maps in data-scarce regions. Since global climate evolution is a nonlinear process governed by complex physical principles, these linear downscaling techniques cannot achieve the desired accuracy. Moreover, these techniques cannot utilize different resolution data as model inputs. In this study, we developed a hybrid of multilayer perceptrons that could couple high-resolution topographic data with sparse climate observation data well and then generate high-resolution climate maps. To test the performance of our tool, we generated high-resolution precipitation and air temperature maps using sparse observation data from 21 meteorological stations in Ethiopia. The accuracy of the high-resolution climate maps generated using our hybrid of MLPs clearly outperformed those using a multi-linear regression model or a pure MLP. Full article
(This article belongs to the Special Issue Advanced Statistical Techniques in Oceans and Climate Research)
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20 pages, 8898 KiB  
Article
Quantifying Intra-Catchment Streamflow Processes and Response to Climate Change within a Climatic Transitional Zone: A Case Study of Buffalo Catchment, Eastern Cape, South Africa
by Solomon Temidayo Owolabi, Johanes A. Belle and Sonwabo Mazinyo
Mathematics 2022, 10(16), 3003; https://doi.org/10.3390/math10163003 - 19 Aug 2022
Cited by 1 | Viewed by 1675
Abstract
The complexity of streamflow processes inhibits significant information about catchment performance and its sensitivity to climate change. Little is known about the severity of climate change within the coastal area of the monsoon–subtropical zone of climatic transition. This study advances a quasi-local scale [...] Read more.
The complexity of streamflow processes inhibits significant information about catchment performance and its sensitivity to climate change. Little is known about the severity of climate change within the coastal area of the monsoon–subtropical zone of climatic transition. This study advances a quasi-local scale analysis to simplify daily streamflow dynamics and their relationship with monthly hydro-climatic series (1981–2020) using six gauging stations on the Buffalo River due to its socio-economic significance. An integrated framework based on continuous wavelet transform (CWT), wavelet coherence (WC), innovative trend analysis (ITA), Mann–Kendall (MK), Sequential Mann–Kendall, and Pettitt tests were employed. CWT showed huge declivity in daily streamflow intensity (7676 to 719), >100 mm/day streamflow frequency (15 to 0), and wetness spell time-gap. WC obtained significant streamflow–rainfall co-movement of 8–196-month periodicities, which characterized Buffalo as anti-phase (1–4-month), lag-lead (8–32-month), and in-phase (64–196-month) in processes. The Buffalo River’s sensitivity to significantly decreasing rainfall trends and increasing temperature trends depicts Streamflow–ENSO teleconnection. Contrarily, ITA and MK exhibited significantly increasing trends of tributaries’ low flow and inferred the perennial status of the catchment. The Pettitt test corroborates the deductions and asserts 1990 (temperature), 1996 (streamflow), and 2004/2013 (rainfall) as the abrupt change points, while SMK captured a critical streamflow slump in 2015–2020. Overall, the study proved the reductionist approach and model framework to achieve the hydrological process simplification and resolution of hotspots of hydrologic extremes within a bimodal climate with complex topography. This study remarks on the management policy of the BR and provides a reference for managing water resources and catchment hydro-climatic extremes. Full article
(This article belongs to the Special Issue Advanced Statistical Techniques in Oceans and Climate Research)
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