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.

In this study, we propose a two-parameter symmetric tropical cyclone (TC) pressure model, derived from the fundamental equations of the TC wind field. This model rectifies the deficits of the traditional TC pressure model mathematically. It incorporates a new parameter into the Holland pressure model and establishes relationship equations between the new parameter, Holland parameter B, and TC attributes such as pressure difference, maximum wind speed radius, geographical latitude, and inflow angle. This derivation is achieved theoretically. Our model not only tackles the limitations of the traditional pressure model by meeting the gradient wind equations, but it also resolves the uncertainty issue of parameter B arising from varied factor selection, data time frames, and research maritime areas. As practical applications, we apply both this model and the corresponding wind field model to five TC profiles. Further, we juxtapose them with primary pressure and wind field models and conduct error and statistical significance analyses. Our findings reveal that the two-parameter model produces results on par with the Holland model and superior to Fujita and Takahashi models. Notably, reanalysis of the wind field consistently underestimates the wind field near the maximum wind speed. Hence, a hybrid wind field, synthesized from the modeled and reanalyzed wind fields, appears to be one of the most effective methodologies for reconstructing a tropical cyclone’s wind field.