Month: January 2025
Construction of a Full Process Evaluation for the SocialIntegration of Migrants in Water Conservancy and Hydropower Projects
Abstract
China's water conservancy and hydropower enterprises continue to face the challenge of lagging hydropower standards despite being a major player in the construction of water conservancy and hydropower projects. This paper presents a theory for evaluating the social integration process of immigrants in water conservancy and hydropower projects. The theory aims to promote China’s ‘Belt and Road’ strategy, enhance China’s modernization, and establish China’s right to speak. The paper explains the overall framework, process evaluation, and result evaluation of the theory. The evaluation process can be divided into three periods: planning, placement, and later support. The result evaluation is divided into three aspects: environmental adaptation, population development, and cultural integration. Using the Three Gorges Project as an example, this text preliminarily tests the science, rationality, and feasibility of the entire assessment theory. In the future, research on engineering immigration should expand the application scope of the whole process assessment. Sufficient attention should be paid to the problem of social restructuring, and efforts should be made to build an assessment system with Chinese characteristics.
Comparison of Several Predictor Selection Techniques for Station-Wise Regression-Based Statistical Downscaling of Precipitation for the Lower Krishna River Basin
Abstract
Climate change impact studies have been significantly applied in the past few decades, where understanding and implementing the available global datasets at regional and even station levels is inevitable. In the highly complex hydrological phenomena happening globally, the various predictors and their complex relationships with local predictand (precipitation) play a crucial role. Towards this step, numerous predictor selection techniques such as maximum relevance minimum redundancy (MRMR), least absolute shrinkage and selection operator (LASSO), step-wise regression (SWR), and whale optimization technique (WOA) are adapted. Twenty-two predictors measured during a period of 30 years (from 1985 to 2014) from 14 stations distributed throughout the lower Krishna River basin are used as the data set in this study. The selected predictors from each method are tested on simulated historical data from 1985 to 2014 by CMIP6 MPI-ESM1-2-HR global climate model at every station using artificial neural networks (ANN). Applying the best set of predictors and choosing them according to the site, can boost downscaling of precipitation by the ANN model, based on the evaluation results of screening methods. WOA outperformed in finding the optimal predictors over other methods, SWR, MRMR, and LASSO. The most effective predictors found for WOA for all sites are relative humidity at 850 and 1000 hPa, geo-potential height at 500 hPa, u-wind at 850 hPa, v-wind at 10 m and 500 hPa, minimum temperature, mean sea level pressure and mean zonal gravity wave stress.