Monthly streamflow forecasting is vital for managing water resources. Recently, numerous studies have explored and evidenced the potential of artificial intelligence (AI) models in hydrological forecasting. In this study, the feasibility of the convolutional neural network (CNN), a deep learning method, is explored for monthly streamflow forecasting. CNN can automatically extract critical features from numerous inputs with its convolution–pooling mechanism, which is a distinct advantage compared with other AI models. Hydrological and large-scale atmospheric circulation variables, including rainfall, streamflow, and atmospheric circulation factors are used to establish models and forecast streamflow for Huanren Reservoir and Xiangjiaba Hydropower Station, China. The artificial neural network (ANN) and extreme learning machine (ELM) with inputs identified based on cross-correlation and mutual information analyses are established for comparative analyses. The performances of these models are assessed with several statistical metrics and graphical evaluation methods. The results show that CNN outperforms ANN and ELM in all statistical measures. Moreover, CNN shows better stability in forecasting accuracy.
"The economic law of population distribution and migration has been studied chiefly based on the Chinese situation. The distribution and development of productive forces decide the distribution and migration of population, and in turn, the latter influences the former. The population distributions in three different stages of social development, namely agricultural, industrial and information society, are described. A new concept in population economics is introduced, i.e. population economic density, which is different from the concept of population density. The formula of population economic density is P(population)/R(resources). Many kinds of migration are analysed, and it is believed that the main efficient cause of migration is economy." 相似文献
