State Key Laboratory of Water and Sediment Science and Water Conservancy and Hydropower Engineering

Abstract The Manas River Basin is located in the inland arid area of China. It has a unique natural environment that contains a mountain, oasis, and desert complex ecosystem. Changes in land use type have had significant impacts on the social, economic, and ecological environment in the basin. Based on the remote sensing interpretation data of land use types from 1980 to 2020 in the Manas River Basin, using ArcGIS 10.2 and Fragstats 4.2 and other software to study the temporal and spatial evolution of land use, landscape pattern, and ecological service value (ESV) in the Manas River Basin, several key results were obtained. (1) Unused land accounted for the largest proportion of the total area at about 44%, and the smallest proportion was construction land at 1%, the construction land and farmland areas increased significantly to 82.16 and 34.87%, while the woodland and grassland area decreased to 15.06 and 14.34%, respectively. (2) Between 1980 and 2020, the inflows and outflows of the quantitative transfer tracks for farmland, grassland, and unused land were highly dominant, but the frequent conversion among various types of land led to the transfer tracks becoming more diversified. (3) From 1980 to 2020 the complexity and fragmentation of landscape in the basin decreased, and the heterogeneity, differences, and connectivity of the landscape increased. (4) The ESV of the Manas River Basin had a tendency to initially decrease and then increase, which increased from 237.27 × 10 8 yuan in 1980 to 238.10 × 10 8 yuan in 2020. The above research results can not only provide a basis for the ecological improvement of the Manas River Basin but also provide a reference for the study of other basins/regions in arid areas.

Safety monitoring of hydraulic structures is a critical task in the field of hydraulic engineering construction. This study developed a method for preprocessing and classifying monitoring data for the identification of gross errors in hydraulic structures. By utilizing linear regression and wavelet analysis techniques, it effectively differentiated various waveform characteristics in data sets, such as Sinusoidal Wave Cyclical, Triangular Wave Cyclical, Seasonal Cyclical, and Weakly Cyclical growth types. In the experiments for gross error identification, the 3σ algorithm, K-medoids algorithm, and Isolation Forest algorithm were applied to test the data. The results showed that the K-medoids algorithm excelled in processing Sinusoidal Wave Cyclical Data Sets; the 3σ algorithm adapted better to Triangular Wave Cyclical Data Sets; the Isolation Forest algorithm performed well in handling data sets with significant anomalies or atypical fluctuations and excelled in scenarios with strong seasonality and large data fluctuations; and for complex Weakly Cyclical Growth Data Sets, all three algorithms were less effective, indicating the potential need for more advanced analysis methods or a combination of multiple techniques. Testing on actual engineering data further confirmed the importance of using specific gross error identification techniques for special data types after data set pre-classification, providing a more effective technical solution for the safety monitoring of hydraulic structures.