Machine learning-based prediction and experimental validation of heavy metal adsorption capacity of bentonite.

As a natural adsorbent material, bentonite is widely used in the field of heavy metal adsorption. The heavy metal adsorption capacity of bentonite varies significantly in studies due to the differences in the properties of bentonite, solution, and heavy metal. To achieve accurate predictions of bentonite's heavy metal adsorption capacity, this study employed six machine learning (ML) regression algorithms to investigate the adsorption characteristics of bentonite. Finally, an eXtreme Gradient Boosting Regression (XGB) model with outstanding predictive performance was constructed. Explanation analysis of the XGB model further reveal the importance and influence manner of each input feature in predicting the heavy metal adsorption capacity of bentonite. The feature categories influencing heavy metal adsorption capacity were ranked in order of importance as adsorption conditions > bentonite properties > heavy metal properties. Furthermore, a web-based graphical user interface (GUI) software was developed, facilitating researchers and engineers to conveniently use the XGB model for predicting the heavy metal adsorption capacity of bentonite. This study provides new insights into the adsorption behaviors of bentonite for heavy metals, offering guidance and support for enhancing its application efficiency and addressing heavy metal pollution remediation.

Multi-scale failure mechanisms of hydraulic engineering exposed to seasonally frozen salinization environment: Integrating SBAS-InSAR and mechanical experiments.

Constructing hydraulic engineering ensures agricultural development and improves salinization environments. However, in seasonally frozen salinization regions, hydraulic engineering is prone to deformation failure. Leakage from canal raises the regional groundwater level, triggering secondary salinization environmental issues. Exploring the instability mechanisms is thus necessary for hydraulic engineering. Traditional deformation monitoring techniques and soil experiments are constrained by observation scale and timeliness. In this study, Sentinel-1B data from November 2017 to August 2019 were acquired. The small baseline subset (SBAS) InSAR approach was employed to interpret the seasonal deformation characteristics in both the vertical and slope directions of a damaged canal segment in Songyuan, Northeast China. The mechanical properties of saline-alkali soil under varying water contents were quantified by integrating unconfined compression experiment (UCE). In May, as the soil thawed downward, a frozen lenses with poor permeability formed at a depth of approximately 100 cm, causing the accumulation of meltwater and infiltrated precipitation between the frozen layer and the melting layer in the canal. The soil water content at a depth of 80 to 140 cm exceeded 22 %, reaching a threshold for rapid reduction in unconfined compression strength (UCS). Consequently, in spring, the low soil strength between the frozen layer and the melting layer resulted in interface sliding, with a displacement of -133.88 mm in the canal slope direction. Furthermore, the differential projection of freeze-thaw deformation in the slope direction caused continuous creep of the canal towards the free face, with a value of -23.27 mm, exacerbating the formation of the late spring landslide. Integrating InSAR and engineering geological analysis is beneficial for addressing deformation issues in hydraulic engineering. Ensuring the sustainable operation of hydraulic engineering holds important implications for mitigating the salinization process.

Hazard Assessment of Debris-Flow along the Baicha River in Heshigten Banner, Inner Mongolia, China.

This study focused on a cloud model approach for considering debris-flow hazard assessment, in which the cloud model provided a model for transforming the qualitative and quantitative expressions. Additionally, the entropy method and analytical hierarchy process were united for calculating the parameters weights. The weighting method avoids the disadvantages inherent in using subjective or objective methods alone. Based on the cloud model and component weighting method, a model was established for the analysis of debris-flow hazard assessment. There are 29 debris-flow catchments around the pumped storage power station in the study area located near Zhirui (Inner Mongolia, China). Field survey data and 3S technologies were used for data collection. The results of the cloud model calculation process showed that of the 29 catchments, 25 had low debris-flow hazard assessment, three had moderate hazard assessment, and one had high hazard assessment. The widely used extenics method and field geological surveys were used to validate the proposed approach. This approach shows high potential as a useful tool for debris-flow hazard assessment analysis. Compared with other prediction methods, it avoids the randomness and fuzziness in uncertainty problems, and its prediction results are considered reasonable.