Fei Guo

3D reconstruction technology generates three-dimensional representations of real-world objects, scenes, or environments using sensor data such as 2D images, with extensive applications in robotics, autonomous vehicles, and virtual reality systems. Traditional 3D reconstruction techniques based on 2D images typically rely on RGB spectral information. With advances in sensor technology, additional spectral bands beyond RGB have been increasingly incorporated into 3D reconstruction workflows. Existing methods that integrate these expanded spectral data often suffer from expensive scheme prices, low accuracy, and poor geometric features. Three-dimensional reconstruction based on NeRF can effectively address the various issues in current multispectral 3D reconstruction methods, producing high-precision and high-quality reconstruction results. However, currently, NeRF and some improved models such as NeRFacto are trained on three-band data and cannot take into account the multi-band information. To address this problem, we propose Multispectral-NeRF—an enhanced neural architecture derived from NeRF that can effectively integrate multispectral information. Our technical contributions comprise threefold modifications: Expanding hidden layer dimensionality to accommodate 6-band spectral inputs; redesigning residual functions to optimize spectral discrepancy calculations between reconstructed and reference images; and adapting data compression modules to address the increased bit-depth requirements of multispectral imagery. Experimental results confirm that Multispectral-NeRF successfully processes multi-band spectral features while accurately preserving the original scenes’ spectral characteristics.

Groundwater level (GWL) is a significant indicator for quantifying groundwater availability. Currently, hydrologists worldwide are actively engaged in modelling and predicting GWL. In karst regions, GWL exhibit varying responses to rainfall events across different locations and the impact of rainfall events on GWL within the same location also varies. Despite incorporating rainfall as an input variable, most existing data‐driven GWL prediction models inadequately account for the spatio‐temporal heterogeneity of karst water areas. Therefore, this study proposes a new analysis method to investigate the response patterns of GWL to rainfall events in karst regions with typical spatio‐temporal variations, known as the sensitivity analysis of rainfall‐GWL response. The method introduces the rainfall response coefficient to describe the response characteristics of GWL to rainfall. Through the rainfall response coefficient, the rainfall response variable (RR) is calculated and incorporates it as an input in the RR‐long short‐term memory (LSTM) GWL prediction model. The effectiveness of proposed method was validated by GWL prediction in karst aquifers located in Jinan City, China, renowned for its spatial–temporal heterogeneity in karst development. Through the analysis and validation conducted by integrating geographical multi‐feature, the study revealed a significant improvement in the accuracy of the RR‐LSTM model after integrating RR as a variable, particularly during significant rainfall events. These findings affirm that the method proposed in this study is highly effective in karst regions characterized by anisotropic karst features.

The Yangtze River Delta (YRD) is one of the regions with the most intensive human activities. The eutrophication of lakes in this area is becoming increasingly serious with consequent negative impacts on the water supply of the surrounding cities. But the spatial-temporal characteristics and driving factors of the trophic state of the lake in this region are still not clearly addressed. In this study, a semi-analytical algorithm for estimating the trophic index (TSI) using particle absorption at 645 nm based on MODIS images is proposed to monitor and evaluate the trophic state of 41 large lakes (larger than 10 km²) in the YRD from 2002 to 2020. The performance of the proposed algorithm is evaluated using an independent dataset. Results showed that the root-mean-square error (RMSE) of the algorithm is less than 6 and the mean absolute percentage error (MAPE) does not exceed 8%, indicating that it can be applied for remotely deriving the TSI in the YRD. The spatial-temporal patterns revealed that there were significantly more lakes with moderate eutrophication in the Lower Yangtze River (LYR) than in the Lower Huaihe River (LHR). The overall average value of the TSI reaches a maximum in summer and a minimum in winter. The TSI value in the YRD over the period 2002–2020 showed a downward trend, especially after 2013. Individually, 33 lakes showed a downward trend and 8 lakes showed an upward trend. Furthermore, marked seasonal and interannual temporal variations can be clearly observed in the LYR and LHR and the sum of the variance contributions of seasonal and interannual components is more than 50%. Multiple linear regression analysis showed that human activities can explain 65% of the variation in the lake TSI in the YRD.

Groundwater is an important water resource, and groundwater level (GWL) forecasting is a useful tool for supporting the sustainable management of water resources. Existing studies have shown that GWLs can be accurately predicted by combining an artificial neural network model with meteorological and hydrological factors. However, GWL data are typically geographic spatiotemporal series data, and current studies have considered only the spatial distance factor when predicting GWLs. In karst aquifers, the GWL is affected by the developmental degree of the karst, topographic factors, structural features, and other factors; considering only the spatial distance is not enough, and the real spatial connectivity characteristics need to be considered. Thus, in this paper, we proposed a new method for forecasting GWLs in karst aquifers while considering connectivity characteristics using a neural network prediction model. The connectivity of a karst aquifer was analyzed by a multidimensional feature clustering method based on the distance index and hydrogeological characteristics recorded at observation wells, and a convolutional long short-term memory (ConvLSTM) conjunction model was constructed. The proposed approach was validated through GWL simulations and predictions in karst aquifers in Jinan, China, and four experiments were conducted for comparison. The experimental results show that the proposed method provided the most consistent results with the measured observation well data among the analyzed methods. These findings demonstrate that the proposed method, which considers connectivity characteristics in karst aquifers, has a higher simulation accuracy than other methods. This method is therefore effective and provides a new idea for the real-time prediction of the GWLs of karst aquifers.

The Humen Estuary, one of the largest outlets of the Pearl River, is a long and wide tidal channel with a considerable tidal flow every year. Storm surges, always superposing spring tide, travel from the estuary and endanger the safety of people living around the river. However, little research has quantified the relationship between the hydraulic characteristics and the geometry features in this estuary. In this regard, an analytical model, combined with a numerical model, is applied to investigate the characteristics of tidal waves and surge propagations in the estuary. Given the geometric, topographic, and tidal parameters at the mouth of the estuary, the tidal damping and wave celerity can be computed. The numerical results were used to calibrate and verify the analytical model. The results indicate that the analytical model can describe the astronomical tidal dynamics very well in correspondence with the numerical results. However, the analytical model cannot predict the tide well when a tropical cyclone-induced surge is superimposed on the astronomical tide. The reason is that this model does not take the wind stress and the pressure depression into account. After reducing Manning’s coefficient, we found that the analytical results could be close to the numerical results. Finally, we analyzed the characteristics of the tidal wave in the Humen Estuary using the analytical solution and its parameters.