Fei Teng’s research while affiliated with Shaoguan University and other places

The accurate determination of rock elasticity modulus is crucial for geomechanical analysis and reliable rock engineering designs. Traditional experimental methods have limitations in estimating elasticity modulus, prompting the adoption of artificial intelligence and data-driven techniques to develop adaptive and accurate predictive models. This study utilized the Deep Random Forest Optimization (DRFO) algorithm, a hybrid approach combining deep learning and random forest algorithms, to predict rock elasticity modulus. The dataset consisted of 350 sedimentary rock samples from various regions in Iran, including sandstone, limestone, marlstone, and mudstone. The performance of the predictive models was assessed using confusion matrices, statistical errors, and the coefficient of determination (R²). The results revealed the superior performance of the DRFO model, exhibiting a remarkably low Mean Absolute Error (MAE) of 0.180 GPa, outperforming other models. The Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) values (0.026 and 0.161, respectively) confirmed the precision of DRFO’s predictions. DRFO demonstrated robustness and generalization capability, yielding excellent performance in both training and testing datasets. Moreover, accuracy and precision evaluation in the training dataset showed a high accuracy (0.97) and precision (0.97), indicating the reliability of DRFO in estimating rock elasticity modulus. The study underscores the significance of data-driven techniques, particularly the potential of DRFO in accurately predicting rock properties. It contributes valuable insights to the field of geotechnical engineering, aiding infrastructure design and ensuring the safety and stability of sedimentary rock-based structures. Further research can explore DRFO’s adaptability to different geological contexts and extend its application to other essential rock properties, advancing geotechnical and geological engineering practices. The integration of advanced data-driven approaches like DRFO can enhance rock mechanics understanding, facilitating sustainable engineering solutions for various geotechnical projects.

Naqadeh Region (NR) is one of the most sensitive regions regarding geo-hazards ‎occurrence in Northwest of Iran. The landslides triggering parameters that ‎identified for the studied region are classified as elevation, aspect, slope angle, ‎lithology, drainage density, distance to river, weathering, land-cover, ‎precipitation, vegetation, distance to faults, distance to roads, and distance to ‎the cities. These triggering factors are selected based on conducting field ‎survey, remote-sensing investigation, and historical development background ‎assessment. Regarding the investigations, 12 large-scale, 15 medium-scale, and 30 small-scale historical landslides ‎(57 in total) were recorded in the NR. The historical landslides were used to provide ‎sensitive area with high probability of ground movements. The objectives of this study are multifaceted, aiming to address critical gaps in understanding and predicting landslide susceptibility in the NR. First, the study seeks to evaluate and compare the effectiveness of ‎support-vector machine (SVM), multilayer perceptron (MLP), and decision tree ‎‎(DT) algorithms in predicting landslide susceptibility. So, as methodology, the ‎presented study used comparative models for landslide susceptibility based on ‎SVM, MLP, and DT approaches. The predictive models were compared based on model ‎accuracy as the area under the curve of the receiver operating characteristic ‎curve. According to the estimated results, MLP is the highest rank of overall ‎accuracy to provide susceptibility maps for landslides in NR. From a perspective of ‎the risk ability, the west and south-west sides of the county were identified within ‎the hazard area.

The hydrological system of thebasin of Lake Urmia is complex, deriving its supply from a network comprising 13 perennial rivers, along withnumerous small springs and direct precipitation onto the lake’s surface. Among these contributors, approximately half of the inflow is attributed to the Zarrineh River and the Simineh River. Remarkably, Lake Urmia lacks a natural outlet, with its water loss occurring solely through evaporation processes. This study employed a comprehensive methodology integrating ground surveys, remote sensing analyses, and meticulous documentation of historical landslides within the basin as primary information sources. Through this investigative approach, we preciselyidentified and geolocated a total of 512 historical landslide occurrences across the Urmia Lake drainage basin, leveraging GPS technology for precision. Thisarticle introduces a suite of hybrid machine learning predictive models, such as support-vector machine (SVM), random forest (RF), decision trees (DT), logistic regression (LR), fuzzy logic (FL), and the technique for order of preference by similarity to the ideal solution (TOPSIS). These models were strategically deployed to assess landslide susceptibility within the region. The outcomes of the landslide susceptibility assessment reveal that the main high susceptible zones for landslide occurrence are concentrated in the northwestern, northern, northeastern, and some southern and southeastern areas of the region. Moreover, when considering the implementation of predictions using different algorithms, it became evident that SVM exhibited superior performance regardingboth accuracy (0.89) and precision (0.89), followed by RF, with and accuracy of 0.83 and a precision of 0.83. However, it is noteworthy that TOPSIS yielded the lowest accuracy value among the algorithms assessed.