In response to the demand for termite prevention in hydraulic engineering， this study constructed a predictive model for termite habitat suitability in Zhejiang Province by integrating the MaxEnt ecological niche model with GIS spatial analysis technology， based on termite census data from Zhejiang Province’s hydraulic engineering projects （2023-2024）. The research systematically revealed the spatial distribution patterns and environmental driving mechanisms of termite habitats in Zhejiang Province. Through a dynamic screening process of “preliminary selection-contribution analysis-collinearity test-iterative optimization”， 22 initial environmental factors were refined to 14， achieving a model AUC value exceeding 0.963. The Jackknife test identified soil pH （6.0-6.4）， annual precipitation （1280-1450 mm） and elevation （80-200 m） as the core environmental drivers most suitable for termite survival in hydraulic engineering. Spatial simulations indicated that the total area of termite-suitable habitats in Zhejiang Province is 40，300 km²， with high-suitability zones （6 400 km²） concentrated in the hilly valleys of central and southern Zhejiang， medium-suitability areas （13，700 km²） distributed in patches in central and northern Zhejiang， and low-suitability zones （20，200 km²） covering all river basins in the province with potential diffusion risks. The research not only provides a reusable technical paradigm for biological disaster risk assessment in hydraulic engineering but also offers important references for formulating precise "one-reservoir-one-policy" prevention systems， guiding the layout of monitoring stations， prioritizing prevention efforts， and developing ecological barrier technologies.

In the identification of operational behavior of earth pressure cells and structural safety assessment， measurement gross errors often coexist with abrupt changes reflecting actual structural responses， thereby compromising the accuracy of safety evaluation. To address this issue， this paper proposes an anomaly detection method for earth pressure cell measurements based on frequency domain decomposition and multi-point joint analysis. Through decomposition and reconstruction in the frequency domain， frequency components representing long-term trends and short-term fluctuations are derived. Subsequently， by integrating correlation analysis and the Isolation Forest algorithm， joint anomaly detection and diagnosis across multiple measurement points are achieved. Case studies based on actual engineering monitoring data demonstrate that the proposed method can accurately distinguish between gross errors and abrupt changes caused by structural anomalies or environmental factors， with high detection accuracy. The method enhances the diagnostic capability of earth pressure cell measurements and improves structural safety monitoring.

Sand liquefaction caused by strong earthquakes is receiving increasing attention due to the frequency of extreme seismic events. The liquefaction possibility assessment is the primary task in the study of sand liquefaction. In this paper，a probability assessment model is established based on the field investigation of liquefaction cases，combined with the knowledge of probability statistics and logistic regression algorithm. The effectiveness of the model is verified by comparing with the existing deterministic liquefaction assessment methods. Furthermore，the parameters analysis affecting the liquefaction assessment results is also conducted. The results show that the liquefaction discrimination model established in this paper has a success rate of 85.70% and 82.50% for rejudging the liquefaction and non-liquefaction cases； and a success rate of 88.00% and 72.00% for the discrimination of the validation set， demonstrating a good discrimination success rate. The fine particle content， overburden stress correction factor，the correction coefficient for overburden stress，and the adjustment coefficient for seismic magnitude should be applied to correct case data when applying this model to assess liquefaction potential，which can improve the accuracy of sand liquefaction assessment.At the same time， the model can provide specific discrimination formulas. In the future， when new samples are incorporated， the model can be further improved by adjusting and modifying based on various parameters.

The construction of Low Impact Development （LID） facilities has a significant impact on alleviating urban waterlogging disasters. To study the effect of LID facility construction on rain and flood control in the northern plain area， this paper constructs a Storm Water Management Model （SWMM） based on the district as the base of the central urban area of Hengshui city， analyzes the current pipe network flow capacity， and simulates and compares the rain and flood control effects before and after the construction of LID facilities in the central urban area from the aspects of annual total runoff control rate and flood risk. The simulation results show that the drainage capacity of the central part of each area in the central urban area is relatively lower than that of the surrounding areas. More than 50% of the pipe network has a good drainage capacity， but about 30% of the pipe network still needs to be renovated. After the construction of LID facilities， the annual total runoff control rate in the central urban area has significantly increased to 76%， an increase of 14% compared with the development and construction before. The annual total runoff control rate of each key district has reached more than 75%. By comparing the flood risk in the built-up area of the central urban area under a 30-year return period 24-hour rainfall （cumulative rainfall of 195.7 mm） before and after the construction of LID facilities， the flood risk area has decreased by 4.46 km² after the construction， and all 8 severe flood waterlogging points that appeared before have been eliminated. The flood prevention standard area is about 71.59 km²， accounting for 93.3% of the built-up area， and the flood prevention standard area has reached the standard. The construction of LID facilities in the central urban area can effectively cope with a 30-year return period of heavy rain.