Soil dielectric sensors have been widely used to obtain real-time soil moisture data, which are important for water resource management. However, soluble salts in the soil significantly affect the accuracy of these sensor measurements. Therefore, it is crucial to select suitable soil dielectric sensors for soil moisture measurements at different salinity levels. Eight mainstream sensors (EC-5, 5TE, Teros12, Hydra-probe II, TDR315L, TDR315H, TDR305H, and CS655) were selected and tested at four different soil salinity levels (EC_1:5 = 3.0, 1.5, 1.0, and 0.75 dS·m⁻¹). The measured values using the factory calibration formulas were compared at six soil moisture levels. The results showed that the measured soil moisture values from various sensors exhibited varying degrees of overestimation, which increased with increasing salinity. Only EC-5 did not exhibit distortion at high-salinity levels, with the measured values showing a good linear trend compared to the standard values. Mutational distortion of the measured apparent dielectric permittivity occurred in TDR315L, TDR315H, Hydra-probe II, and 5TE at EC_1:5 = 3.0 dS·m⁻¹. Insensitive distortion of the measured apparent dielectric permittivity occurred in Teros12 and TDR305H at EC_1:5 = 3.0 dS·m⁻¹ as well as in Teros12, TDR305H, 5TE and Hydra-probe II at EC_1:5 = 1.5 dS·m⁻¹. All tested sensors performed reasonably well at EC_1:5 ≤ 1.0 dS·m⁻¹. Seven sensors (excluding CS655) were calibrated within the distortion threshold. The soil moisture accuracy using the calibrated formulas could reach ±0.02 cm³·cm⁻³. At EC_1:5 ≤ 1.0 dS·m⁻¹, most sensors in this study could be applied with the factory calibration formulas. TDR series, EC-5, 5TE and Teros12 were recommended after calibration for EC_1:5 > 1.0 dS·m⁻¹. For extremely high soil salinity levels, the TDR series and EC-5 may be the best choices.

Groundwater serves as a crucial resource, with its quality significantly impacted by both natural and human-induced factors. In the highly industrialized and urbanized Yangtze River Delta region, the sources of pollutants in shallow groundwater are more complex, making the identification of groundwater pollution sources a challenging task. In this study, 117 wells in Wujiang District of Suzhou City were sampled, and 16 groundwater quality parameters were analyzed. The fuzzy synthetic evaluation method was used to assess the current status of groundwater pollution in the study area; the principal component analysis (PCA) was employed to discern the anthropogenic and natural variables that influence the quality of shallow groundwater; and the absolute principal component scores–multiple linear regression (APCS-MLR) model was applied to quantify the contributions of various origins toward the selected groundwater quality parameters. The results indicate that the main exceeding indicators of groundwater in Wujiang District are I ($28\%$), $N{H}_4\text{-}N$ ($18\%$), and Mn ($14\%$); overall, the groundwater quality is relatively good in the region, with localized heavy pollution: class IV and class V water are mainly concentrated in the southwest of Lili Town, the north of Songling Town, and the south of Qidu Town. Through PCA, five factors contributing to the hydrochemical characteristics of groundwater in Wujiang District were identified: water–rock interaction, surface water–groundwater interaction, sewage discharge from the textile industry, urban domestic sewage discharge, and agricultural non-point source pollution. Additionally, the APCS-MLR model determined that the contributions of the three main pollution sources to groundwater contamination are in the following order: sewage discharge from the textile industry ($10.63\%$) > urban domestic sewage discharge ($8.69\%$) > agricultural non-point source pollution ($6.26\%$).

Coastal engineering construction suitability evaluation methods are too empirical and difficult to quantify. Considering these weaknesses, in order to determine the weight of each factor reasonably, and to analyze the suitability of coastal zone construction comprehensively, the theory of establishing a coastal zone construction suitability evaluation model based on a Rough Set (RS) and an Analytic Hierarchy Process (AHP) is proposed. In total, 20 typical coastal areas of Putian are selected, and the main impact factors are determined according to a port dock, pollution-prone industry, and an electric power plant. The contribution rate and weight of each factor for the construction of a coastal zone are analyzed by the combination evaluation model, and the final evaluation result is consistent with the actual investigation situation. Finally, 52 evaluation units of the Putian coastal zone are evaluated by Neural Networks (NNs). The weight of the impact factors is made more objective by using the training sample set of the combination evaluation model as the sample set of the neural network. The learning speed and accuracy of the network are improved, and the and the evaluation result is consistent with the actual investigation situation. In a word, it is effective to perform the suitability evaluation of the coastal zone construction using the RS-AHP-NN proposed model, and it can be applied in practical engineering.