It is very well-known that the reliable coupling relationship between water area and water level is very important in analyzing the risks of floods and droughts for big lakes, such as Dongting Lake, especially when remote sensing images are absent and in situ measurements cannot be carried out. To obtain this relationship, two types of mathematical models—polynomial regression (PR) based on the least square algorithm and machine learning regression (MLR) based on the BP (Backpropagation) neural network algorithm—are constructed using the water area data extracted from multiple temporal remote sensing images and water levels recorded at several representative hydrological stations for nearly 30 years. In this study, Dongting Lake is divided into three parts: East Dongting Lake (EDL), South Dongting Lake (SDL), and West Dongting Lake (WDL). This is because water slope exists on its surface, which is formed by several inflow rivers and the high and low terrain. To calculate the total water area of this lake, two ways are put forward by choosing the water levels: from EDL, SDL, and WDL in their turn; or from all three simultaneously. In other words, three univariate and one multivariate regression. For PR, there are perfect coefficients of determination (most nearly 0.95, the smallest being 0.76), which is in line with regression test relative errors (between 0.27% and 6.7%). For MLR, which was initially applied to this problem, the best node number (10 for the first way, 8 for the second way) in the hidden layer of the neural network is adaptively chosen, with coefficients of determination (similar to PR), together with training and testing error performances (between 1% and 10%). These results confirm the validity and reliability of them. The regression and prediction results on the two models are better than the documented way (only focus on the water level of EDL). These results can provide some references for researchers and decision makers in studying similar big Lakes.

It is well-known that high-performance thermal protective clothing is crucial for personnel working in high-temperature environments, such as firefighters. Thermal protective clothing design usually integrates textile materials’ type, thickness, physical and chemical properties (such as thermal conductivity), ergonomics, and environmental adaptability. In this study, the heat transfer process and the optimal thickness are mainly discussed for providing some references on the design of this clothing. The thickness design of thermal protective clothing fabrics is carried out via numerical heat transfer simulations based on experimental data obtained from manikin tests. Firstly, one heat transfer model for thermal protective clothing, including three textile materials’ layers and one air layer, is constructed according to Fourier’s law of heat conduction, Newton’s law of cooling, and the Stefan–Boltzmann law, with appropriate boundary conditions assigned. Secondly, the finite volume element method, which has the important advantage of preserving conservation properties for physical quantities, is employed to discretize the heat transfer model. Thirdly, the convective heat transfer coefficient, which characterizes heat exchange between fluid and solid surfaces, is determined approximately by the least-squares method based on the given data, while the heat transfer process is simultaneously simulated. Fourthly, the thicknesses of the second and fourth layers are critical to the performance of thermal protective clothing. Two optimization algorithms are proposed to determine the optimal thickness configuration that effectively balances thermal insulation and wearing comfort. From the above results, it is recommended to use multilayer textile composite materials incorporating aerogel insulation layers and phase-change material interlayers.