Time Series Forecasting in Physical Geography

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: 15 June 2024 | Viewed by 4174

Special Issue Editors

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Guest Editor
Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, SE-223 62 Lund, Sweden
Interests: surface water hydrology; snow hydrology; remote sensing; hydrological modeling
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Guest Editor
Laboratory of Water and Environment, Faculty of Nature and Life Sciences, Hassiba Benbouali University of Chlef, Chlef 02180, Algeria
Interests: hydrology; environment; machine learning; remote sensing; hydroinformatics
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Special Issue Information

Dear Colleagues,

In the last several years, time series forecasting via machine learning-based models, statistical-based models, and physically-based models has been rapidly providing solutions to many outstanding problems in the field of physical geography. In the field of physical geography, the artificial intelligence-based solution approach has indisputable advantages, and researchers have also been trying to solve environmental problems via the application of new technologies in time series forecasting. There are some linear and non-linear relationships in physical geography components (e.g., the water cycle) that can be simulated by observing symmetry and finding relationships between geographic variables. Due to the complex nature of physical geographic variables, it is important to consider symmetry in the time series forecasting of these variables. Time series forecasting via new technologies (machine learning, remote sensing and hybrid artificial intelligence-based models) could be widely used in different areas of physical geography, such as vegetation studies, drought monitoring and forecasting, rainfall-runoff modeling, groundwater studies, forest management, land cover studies, evaporation, and evapotranspiration forecasting, streamflow modeling, solar radiation simulation, precipitation prediction, and soil moisture modeling, etc. This Special Issue on "Time Series Forecasting in Physical Geography" is seeking original research papers about the applications of new technologies for time series forecasting in physical geography. Potential topics that will be covered by this Special Issue include, but are not limited to, the following:

  • Using machine learning models in hydrological studies.
  • Time series forecasting for symmetric exclusion.
  • Deep learning for analyzing symmetries in physical geography.
  • Time series forecasting by statistical-based models in physical geography.
  • Application of artificial intelligence and remote sensing for time series prediction.
  • Earth observation via satellite imagery.
  • Symmetry and its role in physical geography.

Dr. Babak Mohammadi
Prof. Dr. Mohammed Achite
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. Symmetry 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.


  • physical geography
  • hydrology
  • time series forecasting
  • artificial intelligence
  • geographic information systems
  • satellite image analysis
  • evolutionary computation
  • earth observation
  • water resources management

Published Papers (1 paper)

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24 pages, 7275 KiB  
Long-Short Term Memory Technique for Monthly Rainfall Prediction in Thale Sap Songkhla River Basin, Thailand
by Nureehan Salaeh, Pakorn Ditthakit, Sirimon Pinthong, Mohd Abul Hasan, Saiful Islam, Babak Mohammadi and Nguyen Thi Thuy Linh
Symmetry 2022, 14(8), 1599; https://doi.org/10.3390/sym14081599 - 3 Aug 2022
Cited by 11 | Viewed by 3004
Rainfall is a primary factor for agricultural production, especially in a rainfed agricultural region. Its accurate prediction is therefore vital for planning and managing farmers’ plantations. Rainfall plays an important role in the symmetry of the water cycle, and many hydrological models use [...] Read more.
Rainfall is a primary factor for agricultural production, especially in a rainfed agricultural region. Its accurate prediction is therefore vital for planning and managing farmers’ plantations. Rainfall plays an important role in the symmetry of the water cycle, and many hydrological models use rainfall as one of their components. This paper aimed to investigate the applicability of six machine learning (ML) techniques (i.e., M5 model tree: (M5), random forest: (RF), support vector regression with polynomial (SVR-poly) and RBF kernels (SVR- RBF), multilayer perceptron (MLP), and long-short-term memory (LSTM) in predicting for multiple-month ahead of monthly rainfall. The experiment was set up for two weather gauged stations located in the Thale Sap Songkhla basin. The model development was carried out by (1) selecting input variables, (2) tuning hyperparameters, (3) investigating the influence of climate variables on monthly rainfall prediction, and (4) predicting monthly rainfall with multi-step-ahead prediction. Four statistical indicators including correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), and overall index (OI) were used to assess the model’s effectiveness. The results revealed that large-scale climate variables, particularly sea surface temperature, were significant influence variables for rainfall prediction in the tropical climate region. For projections of the Thale Sap Songkhla basin as a whole, the LSTM model provided the highest performance for both gauged stations. The developed predictive rainfall model for two rain gauged stations provided an acceptable performance: r (0.74), MAE (86.31 mm), RMSE (129.11 mm), and OI (0.70) for 1 month ahead, r (0.72), MAE (91.39 mm), RMSE (133.66 mm), and OI (0.68) for 2 months ahead, and r (0.70), MAE (94.17 mm), RMSE (137.22 mm), and OI (0.66) for 3 months ahead. Full article
(This article belongs to the Special Issue Time Series Forecasting in Physical Geography)
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