This study proposes an artificial intelligence approach to assess watershed morphometry in the Makran subduction zones of South Iran and Pakistan. The approach integrates machine learning algorithms, including artificial neural networks (ANN), support vector regression (SVR), and multivariate linear regression (MLR), on a single platform. The study area was analyzed by extracting watersheds from a Digital Elevation Model (DEM) and calculating eight morphometric indices. The morphometric parameters were normalized using fuzzy membership functions to improve accuracy. The performance of the machine learning algorithms is evaluated by mean squared error (MSE), mean absolute error (MAE), and correlation coefficient (R²) between the output of the method and the actual dataset. The ANN model demonstrated high accuracy with an R² value of 0.974, MSE of 4.14 × 10⁻⁶, and MAE of 0.0015. The results of the machine learning algorithms were compared to the tectonic characteristics of the area, indicating the potential for utilizing the ANN algorithm in similar investigations. This approach offers a novel way to assess watershed morphometry using ML techniques, which may have advantages over other approaches.

This research assessed stress regimes and fields in eastern Iran using fault-slip data and the tectonic events associated with these changes. Our stress analysis of the brittle structures in the Shekarab Mountains revealed significant changes in stress regimes from the late Cretaceous to the Quaternary. Reconstructing stress fields using the age and sense of fault movements showed that during the late Cretaceous, the direction of the maximum horizontal stress axes (σ1) under a compressional stress regime was ~N290°. This stress regime led to the uplifting of ophiolites and peridotites in eastern Iran. During the Eocene, the σ1 direction was NE-SW. The late Eocene and Oligocene stress states showed two distinct transpression and transtension stress regimes. This transition from transpression to transtension in the eastern Shekarab Mountains was the consequence of regional variations in stress regimes. The Quaternary stress state indicates that the tectonic regime in the Quaternary is strike-slip and the σ1 direction is ~N046°, which coincides with the current convergence direction of the Arabia–Eurasia plates. Our paleostress analysis revealed that four distinct stress regimes have been recognized in the area, including compressional, transtensional, transpressional, and strike-slip regimes. Our findings indicated that the diversity of the tectonic regimes was responsible for the formation of a variety of geological structures, including folds with different axes, faults with different mechanisms, and the current configuration of the Sistan suture zone.

The Alborz mountain range in northern Iran is part of the active and seismic Alpide belt, where assessing seismic hazards is crucial due to the region’s history of large instrumental earthquakes and destructive seismic background. Moment rate estimation, which quantifies tectonic activity, offers a novel approach to understanding the energy potential of active tectonic regions. In this study, a regional perspective is employed to investigate the maximum horizontal acceleration for Tehran, the major city in Alborz, resulting from the Sorkh-e Hesar and Ghasr-e-Firuzeh faults located approximately 7.5 km southeast of Tehran. These faults have a seismic potential of Mw 6.5 and a gravity of ~0.5723. While previous studies have identified faults in northern Tehran as the greatest seismic risk, our findings suggest otherwise. The calculated geological moment was 5.18218 × 10¹⁷ Nm/y, with a seismic moment rate of 1.83375 × 10¹⁴ Nm/y, providing valuable insights into fault activity and seismic potential in the study area.