Hybrid Deep Learning Frameworks for Spatial–Temporal Dynamics Analysis Based on Remote Sensing and GIS

Peyman Yariyan; Bakhtiar Feizizadeh; Thomas Blaschke; Sadra Karimzadeh

doi:10.31127/tuje.1946149

Hybrid Deep Learning Frameworks for Spatial–Temporal Dynamics Analysis Based on Remote Sensing and GIS

Abstract

Atmospheric mineral dust critically influences climate systems, ecological frameworks, and human health, particularly in arid and semi-arid regions. While various modeling approaches have been applied to map dust susceptibility, conventional methods often fail to capture the complex spatio-temporal interactions governing dust emission dynamics. This study addresses this gap by evaluating four hybrid deep learning-support vector machine (DL-SVM) frameworks—CNN-SVM, DBN-SVM, LSTM-SVM, and AE-SVM—for predicting dust-prone areas in the Lake Urmia Basin, Iran. Quantitative analyses demonstrated that all models possess significant predictive capability. The LSTM-SVM model achieved the highest discriminative power during training (AUC = 0.981), while the AE-SVM model delivered the most robust generalization during testing, with an accuracy of 87.63%. SHAP-based feature importance analysis revealed that the Normalized Difference Vegetation Index (NDVI) was the uniformly dominant factor controlling dust susceptibility across all models. Complementary soil properties—texture, pH, and salinity—played a decisive secondary role. These findings confirm that hybrid deep learning architectures, which simultaneously leverage spatial, temporal, and non-linear feature representations, offer superior capability in identifying dust-source areas compared to traditional models. This study provides a scientific and practical framework for soil management, vegetation conservation, and dust risk mitigation.

Keywords

References

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Details

Primary Language

English

Subjects

Geographical Information Systems (GIS) in Planning

Journal Section

Research Article

Authors

Peyman Yariyan
0000-0000-0000-2564
Iran

Bakhtiar Feizizadeh ^*
0000-0002-3367-2923
Germany

Thomas Blaschke
0000-0002-1860-8458
Austria

Sadra Karimzadeh
0000-0001-2345-6789
Iran

Publication Date

October 16, 2026

Submission Date

May 7, 2026

Acceptance Date

July 5, 2026

Published in Issue

Year 2026 Volume: 10 Number: 4

DOI

https://doi.org/10.31127/tuje.1946149

IZ

https://izlik.org/JA43NX97EZ

Cite

RIS / Bibtex

APA

Yariyan, P., Feizizadeh, B., Blaschke, T., & Karimzadeh, S. (2026). Hybrid Deep Learning Frameworks for Spatial–Temporal Dynamics Analysis Based on Remote Sensing and GIS. Turkish Journal of Engineering, 10(4), 1310-1327. https://doi.org/10.31127/tuje.1946149

AMA

1.Yariyan P, Feizizadeh B, Blaschke T, Karimzadeh S. Hybrid Deep Learning Frameworks for Spatial–Temporal Dynamics Analysis Based on Remote Sensing and GIS. TUJE. 2026;10(4):1310-1327. doi:10.31127/tuje.1946149

Chicago

Yariyan, Peyman, Bakhtiar Feizizadeh, Thomas Blaschke, and Sadra Karimzadeh. 2026. “Hybrid Deep Learning Frameworks for Spatial–Temporal Dynamics Analysis Based on Remote Sensing and GIS”. Turkish Journal of Engineering 10 (4): 1310-27. https://doi.org/10.31127/tuje.1946149.

EndNote

Yariyan P, Feizizadeh B, Blaschke T, Karimzadeh S (October 1, 2026) Hybrid Deep Learning Frameworks for Spatial–Temporal Dynamics Analysis Based on Remote Sensing and GIS. Turkish Journal of Engineering 10 4 1310–1327.

IEEE

[1]P. Yariyan, B. Feizizadeh, T. Blaschke, and S. Karimzadeh, “Hybrid Deep Learning Frameworks for Spatial–Temporal Dynamics Analysis Based on Remote Sensing and GIS”, TUJE, vol. 10, no. 4, pp. 1310–1327, Oct. 2026, doi: 10.31127/tuje.1946149.

ISNAD

Yariyan, Peyman - Feizizadeh, Bakhtiar - Blaschke, Thomas - Karimzadeh, Sadra. “Hybrid Deep Learning Frameworks for Spatial–Temporal Dynamics Analysis Based on Remote Sensing and GIS”. Turkish Journal of Engineering 10/4 (October 1, 2026): 1310-1327. https://doi.org/10.31127/tuje.1946149.

JAMA

1.Yariyan P, Feizizadeh B, Blaschke T, Karimzadeh S. Hybrid Deep Learning Frameworks for Spatial–Temporal Dynamics Analysis Based on Remote Sensing and GIS. TUJE. 2026;10:1310–1327.

MLA

Yariyan, Peyman, et al. “Hybrid Deep Learning Frameworks for Spatial–Temporal Dynamics Analysis Based on Remote Sensing and GIS”. Turkish Journal of Engineering, vol. 10, no. 4, Oct. 2026, pp. 1310-27, doi:10.31127/tuje.1946149.

Vancouver

1.Peyman Yariyan, Bakhtiar Feizizadeh, Thomas Blaschke, Sadra Karimzadeh. Hybrid Deep Learning Frameworks for Spatial–Temporal Dynamics Analysis Based on Remote Sensing and GIS. TUJE. 2026 Oct. 1;10(4):1310-27. doi:10.31127/tuje.1946149