Water quality monitoring is very important in agricultural catchments. UV–Vis spectrometry is widely used in place of traditional analytical methods because it is cost effective and fast and there is no chemical waste. In recent years, artificial neural networks have been extensively studied and used in various areas. In this study, we plan to simplify water quality monitoring with UV–Vis spectrometry and artificial neural networks. Samples were collected and immediately taken back to a laboratory for analysis. The absorption spectra of the water sample were acquired within a wavelength range from 200 to 800 nm. Convolutional neural network (CNN) and partial least squares (PLS) methods are used to calculate water parameters and obtain accurate results. The experimental results of this study show that both PLS and CNN methods may obtain an accurate result: linear correlation coefficient (R²) between predicted value and true values of TOC concentrations is 0.927 with PLS model and 0.953 with CNN model, R² between predicted value and true values of TSS concentrations is 0.827 with PLS model and 0.915 with CNN model. CNN method may obtain a better linear correlation coefficient (R²) even with small number of samples and can be used for online water quality monitoring combined with UV–Vis spectrometry in agricultural catchment.

As China’s largest freshwater lake and an important wintering ground for white cranes in Asia, the Poyang Lake wetland has unique ecological value. However, wetland cover types have changed dynamically and have attracted the attention of society and researchers over the past few decades. To obtain detailed knowledge and understanding of the long-term landcover dynamics of Poyang Lake and the associated driving forces, Landsat and HJ-1A/B images (31 images) were used to acquire classification and frequency maps of Poyang Lake in the dry season from 1973–2019 based on the random forest (RF) algorithm. In addition, the driving forces were discussed according to the Geodetector model. The results showed that the coverage of water and mudflat showed opposite trends from 1987–2019. Water and vegetation exhibited a significant decreasing trend from 1981–2003 and from 1996–2004 (p < 0.01), respectively. A phenomenon of vegetation expanding from west to east was found, and the expansion areas were mainly concentrated in the central zone of Poyang Lake, while vegetation in the northern mountainous area of Songmen (region 1) and eastern Songmen Mountain (region 2), showed a significantly expanded trend (R² > 0.6, p < 0.01) during the five-decade period. The year-long dominant distribution of water occurred mainly in the two deltas formed by the Raohe and Tongjin rivers and the Fuhe and Xinjiang rivers, with deep water. In the 1973–2003 and 2003–2019 periods, a total of 313.522 km² of water turned into swamp and mudflat and 478.453 km² of swamp and mudflat transitioned into vegetation, respectively. Elevation and temperature appeared to be the main factors affecting the regional wetland evolution in the dry season and should be considered in the management of Poyang Lake. The findings of this work provide detailed information for spatial–temporal landcover changes of Poyang Lake, which could help policymakers to formulate scientific and appropriate policies and achieve restoration of the Poyang Lake wetland.

Total phosphorus (TP) is a significant indicator of water eutrophication. As a typical macrophytic lake, Lake Baiyangdian is of considerable importance to the North China Plain’s ecosystem. However, the lake’s eutrophication is severe, threatening the local ecological environment. The correlation between chlorophyll and TP provides a mechanism for TP prediction. In view of the absorption and reflection characteristics of the chlorophyll concentrations in inland water, we propose a method to predict TP concentration in a macrophytic lake with spectral characteristics dominated by chlorophyll. In this study, water spectra noise is removed by discrete wavelet transform (DWT), and chlorophyll-sensitive bands are selected by gray correlation analysis (GRA). To verify the effectiveness of the chlorophyll-sensitive bands for TP concentration prediction, three different machine learning (ML) algorithms were used to build prediction models, including partial least squares (PLS), random forest (RF) and adaptive boosting (AdaBoost). The results indicate that the PLS model performs well in terms of TP concentration prediction, with the least time consumption: the coefficient of determination (R²) and root mean square error (RMSE) are 0.821 and 0.028 mg/L in the training dataset, and 0.741 and 0.029 mg/L in the testing dataset, respectively. Compared with the empirical model, the method proposed herein considers the correlation between chlorophyll and TP concentration, as well as a higher accuracy. The results indicate that chlorophyll-sensitive bands are effective for predicting TP concentration.

Painted cultural relics hold significant historical value and are crucial in transmitting human culture. However, mold is a common issue for paper or silk-based relics, which not only affects their preservation and longevity but also conceals the texture, patterns, and color information, hindering cultural value and heritage. Currently, the virtual restoration of painting relics primarily involves filling in the RGB based on neighborhood information, which might cause color distortion and other problems. Another approach considers mold as noise and employs maximum noise separation for its removal; however, eliminating the mold components and implementing the inverse transformation often leads to more loss of information. To effectively acquire virtual restoration for mold removal from ancient paintings, the spectral characteristics of mold were analyzed. Based on the spectral features of mold and the cultural relic restoration philosophy of maintaining originality, a 3D CNN artifact restoration network was proposed. This network is capable of learning features in the near-infrared spectrum (NIR) and spatial dimensions to reconstruct the reflectance of visible spectrum, achieving the virtual restoration for mold removal of calligraphic and art relics. Using an ancient painting from the Qing Dynasty as a test subject, the proposed method was compared with the Inpainting, Criminisi, and inverse MNF transformation methods across three regions. Visual analysis, quantitative evaluation (the root mean squared error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MEA), and a classification application were used to assess the restoration accuracy. The visual results and quantitative analyses demonstrated that the proposed 3D CNN method effectively removes or mitigates mold while restoring the artwork to its authentic color in various backgrounds. Furthermore, the color classification results indicated that the images restored with 3D CNN had the highest classification accuracy, with overall accuracies of 89.51%, 92.24%, and 93.63%, and Kappa coefficients of 0.88, 0.91, and 0.93, respectively. This research provides technological support for the digitalization and restoration of cultural artifacts, thereby contributing to the preservation and transmission of cultural heritage.

As the second largest city in northern China, Tianjin has a unique geographical and social status. Following its rapid economic development, Tianjin is experiencing high levels of surface water pollution. The land use/land cover (LULC) pattern has a considerable impact on hydrological cycling and pollutant transmission, and thus on regional water quality. A full understanding of the water quality response to the LULC pattern is critical for water resource management and improvement of the natural environment in Tianjin. In this study, surface water monitoring station data and LULC data from 2021 to 2022 were used to investigate the surface water quality in Tianjin. A cluster analysis was conducted to compare water quality among monitoring stations, a factor analysis was conducted to identify potential pollution sources, and an entropy weight calculation was used to analyze the impact of the land use pattern on water quality. The mean total nitrogen (TN) concentration exceeded the class Ⅴ water quality standard throughout the year, and the correlation coefficient of the relationship between dissolved oxygen (DO) and pH exceeded 0.5 throughout the year, with other water quality parameters showing seasonal changes. On the basis of their good water quality, the water quality monitoring stations near large water source areas were distinguished from those near areas with other LULC patterns via the cluster analysis. The factor analysis results indicated that the surface water in Tianjin suffered from nutrient and organic pollution, with high loadings of ammonia nitrogen (NH₃N), TN, and total phosphorus (TP). Water pollution was more serious in areas near built-up land, especially in the central urban area. The entropy weight calculation results revealed that water, built-up land, and cultivated/built-up land had the greatest impact on NH₃N, while cultivated land had the greatest impact on electrical conductivity (EC). This study discusses the seasonal changes of surface water and impact of land use/land cover pattern on water quality at a macro scale, and highlighted the need to improve surface water quality in Tianjin. The results provide guidance for the sustainable utilization and management of local water resources.