To obtain the high-resolution multitemporal precipitation using spatial downscaling technique on a precipitation dataset may provide a better representation of the spatial variability of precipitation to be used for different purposes. In this research, a new downscaling methodology such as the global precipitation mission (GPM)-based multitemporal weighted precipitation analysis (GMWPA) at 0.05° resolution is developed and applied in the humid region of Mainland China by employing the GPM dataset at 0.1° and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m DEM-based geospatial predictors, i.e., elevation, longitude, and latitude in empirical distribution-based framework (EDBF) algorithm. The proposed methodology is a two-stepped process in which a scale-dependent regression analysis between each individual precipitation variable and the EDBF-based weighted precipitation with geospatial predictor(s), and to downscale the predicted multitemporal weighted precipitation at a refined scale is developed for the downscaling of GMWPA. While comparing results, it shows that the weighted precipitation outperformed all precipitation variables in terms of the coefficient of determination (R²) value, whereas they outperformed the annual precipitation variables and underperformed as compared to the seasonal and the monthly variables in terms of the calculated root mean square error (RMSE) value. Based on the achieved results, the weighted precipitation at the low-resolution (e.g., at 0.75° resolution) along-with the original resolution (e.g., at 0.1° resolution) is employed in the downscaling process to predict the average multitemporal precipitation, the annual total precipitation for the year 2001 and 2004, and the average annual precipitation (2001–2015) at 0.05° resolution, respectively. The downscaling approach resulting through proposed methodology captured the spatial patterns with greater accuracy at higher spatial resolution. This work showed that it is feasible to increase the spatial resolution of a precipitation variable(s) with greater accuracy on an annual basis or as an average from the multitemporal precipitation dataset using a geospatial predictor as the proxy of precipitation through the weighted precipitation in EDBF environment.

Natural and non‐natural factors have combined effects on the trajectory of COVID‐19 pandemic, but it is difficult to make them separate. To address this problem, a two‐stepped methodology is proposed. First, a compound natural factor (CNF) model is developed via assigning weight to each of seven investigated natural factors, that is temperature, humidity, visibility, wind speed, barometric pressure, aerosol, and vegetation in order to show their coupling relationship with the COVID‐19 trajectory. Onward, the empirical distribution based framework (EDBF) is employed to iteratively optimize the coupling relationship between trajectory and CNF to express the real interaction. In addition, the collected data is considered from the backdate, that is about 23 days—which contains 14‐days incubation period and 9‐days invalid human response time—due to the nonavailability of prior information about the natural spreading of virus without any human intervention(s), and also lag effects of the weather change and social interventions on the observed trajectory due to the COVID‐19 incubation period; Second, the optimized CNF‐plus‐polynomial model is used to predict the future trajectory of COVID‐19. Results revealed that aerosol and visibility show the higher contribution to transmission, wind speed to death, and humidity followed by barometric pressure dominate the recovery rates, respectively. Consequently, the average effect of environmental change to COVID‐19 trajectory in China is minor in all variables, that is about −0.3%, +0.3%, and +0.1%, respectively. In this research, the response analysis of COVID‐19 trajectory to the compound natural interactions presents a new prospect on the part of global pandemic trajectory to environmental changes.

In this work, a Constraint Law method (CLM) based Genetic Algorithm (CLM-GA) is proposed for Nonlinear Least Square (LS) Regression and Normal Optimization Problems (NOP). Numerical experimental results show that CLM-GA is more robust than traditional methods (Gauss Newton, Levernberg Marquardt, EM-GA, EDBF-GA) for both LS and NOP problems in three aspects: 1) efficiency; 2) accuracy; 3) astringency. There are many LS and NOP applications in the physical world, such as military, economics, industry, photogrammetry and so on. Research of this paper can be easily implemented and applied for those applications.

Empirical distribution-based framework (EDBF), as a general framework utilized in lots of muti-parent crossover algorithms (MCAs), makes MCAs much more efficient at each iteration. However, EDBF cannot work with the numerous parent chromosomes especially exceeding fifty. To address this problem, an enhanced EDBF framework, namely constraint-law-method (CLM), is proposed by adaptively changing the boundary of weight assigned to each parent chromosome according to the constraint law. Furthermore, CLM is compared with EDBF, ABPSO and RCBBFA algorithms on 20 benchmark functions in terms of convergence, efficiency and accuracy. Experimental results demonstrate that CLM outperforms comparative algorithms on most of benchmark functions. As a general framework rather than a specific algorithm, CLM is easy to implement and can easily be accommodated to any existing MCAs. Finally, the C++ source code is available at https://github.com/ZhengkangZUO-2020/CLM-Framework-Codes .

Lots of works aim to reveal the driving factors of COVID-19 pandemic trajectory yet ignore the confidence of utilized trajectory data, making consequent results suspicious. Hereby, we proposed a pa...