The adsorption mechanism of superabsorbent polymer (SAP) can provide theoretical guidance for their practical applications in different environments. However, there has been limited research on the mechanism of attapulgite–sodium polyacrylate. This research aimed to compare the Cd(II) adsorption characteristics and water retention properties of organic–inorganic composite SAP (attapulgite–sodium polyacrylate, OSAP) and organic SAP (polyacrylamide, JSAP). Batch experiments were used to investigate the kinetics of Cd(II) adsorption, as well as the thermodynamic properties and factors influencing these properties. The results show that the Cd(II) adsorption capacity was directly proportional to the pH value. The maximum adsorption capacities of OSAP and JSAP were of 770 and 345 mg·g−1. The Cd(II) adsorption for OSAP and JSAP conformed to the Langmuir and the quasi-second-order kinetic model. This indicates that chemical adsorption is the primary mechanism. The adsorption process was endothermic (ΔH0 > 0) and spontaneous (ΔG0 < 0). The water adsorption ratios of OSAP and SAP were 474.8 and 152.6 in pure water. The ratio decreases with the increase in Cd(II) concentration. OSAP and JSAP retained 67.23% and 38.37% of the initial water adsorption after six iterations of water adsorption. Hence, OSAP is more suitable than JSAP for agricultural and environmental ecological restoration in arid and semi-arid regions.

Mountainous regions are prone to dammed lake disasters due to their rough topography, scant vegetation, and high summer rainfall. By measuring water level variation, monitoring systems can detect dammed lake events when mudslides block rivers or boost water level. Therefore, an automatic monitoring alarm method based on a hybrid segmentation algorithm is proposed. The algorithm uses the k-means clustering algorithm to segment the picture scene in the RGB color space and the region growing algorithm on the image green channel to select the river target from the segmented scene. The pixel water level variation is used to trigger an alarm for the dammed lake event after the water level has been retrieved. In the Yarlung Tsangpo River basin of the Tibet Autonomous Region of China, the proposed automatic lake monitoring system was installed. We pick up data from April to November 2021, during which the river experienced low, high, and low water levels. Unlike conventional region growing algorithms, the algorithm does not rely on engineering knowledge to pick seed point parameters. Using our method, the accuracy rate is 89.29% and the miss rate is 11.76%, which is 29.12% higher and 17.65% lower than the traditional region growing algorithm, respectively. The monitoring results indicate that the proposed method is a highly adaptable and accurate unmanned dammed lake monitoring system.