In actual engineering fields, the bearing capacity of a rock is closely related to the pore water pressure in the rock. Studies have shown that the pore water in the rock has a great relationship with the change in runoff. Thus, it has crucial meaning to accurately evaluate and quantitate the property of the rainfall–runoff, and many traditional classic models are proposed to study the characteristic of rainfall–runoff. While considering the high uncertainty and randomness of the rainfall–runoff property, more and more artificial neural networks (ANN) are used for the rainfall–runoff modeling as well as other fields. Among them, the long short-term memory (LSTM), which can be trained for sequence generation by processing real data sequences one step at a time and has good prediction results in other engineering fields, is adopted in this study to investigate the changes of rainfall–runoff values and make a prediction. In order to ensure the accuracy of the trained model, the cross-validation method is used in this study. The training data set is divided into 12 parts. The monthly forecast results from 2014 to 2015 show that the model can well reflect the peaks and troughs. In a recent study, the relationship between the rainfall–runoff and discharge are commonly based on the current measured data, while the prediction results are adopted to analyze the relation of these parameters, and considering that the existing methods have fuzzy relationship between runoff and discharge, which leads to a high risk of forecasting and dispatching. A method of modeling analysis and parameter estimation of hydrological runoff and discharge relationship based on machine learning is designed. From the experimental results, the average risk of this method is 61.23%, which is 15.104% and 13.397% less than that of the other two existing methods, respectively. It proves that the method of hydrological runoff relationship modeling and parameter estimation integrated with machine learning has better practical application effect.

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Purpose This study aims to examine the spatial distribution and movement patterns of creative talent within the Yangtze River Delta Bay Area (YRDBA) and the factors that contribute to such trends. Design/methodology/approach The study examines data for the period 2006 to 2018 from the regions that constitute the YRDBA: Shanghai, Jiangsu, Zhejiang and Anhui. Spatial distribution pattern analysis is adopted to interpret the flow tendency both spatially and chronologically and a Lasso regression model is used to investigate variables that influence this tendency. Findings It is found that creative talents in YRDBA are accumulating steadily in provincial capitals and financially advanced cities. Technology infrastructure, women’s rights, medical care amenities and housing affordability are major determinants of such spatial distribution. The talent spillover effect raises attention in talent saturated areas, while the surrounding regions should prepare to receive and retain the overflow. Originality/value Creative talents geography in China and the dynamism of creative talent in YRDBA are rarely discussed. Determinants of creative talents lack systematic pectination, literature that filters multiple determinants of creative talents migration is limited and discussion specific to the Chinese context is scarce. This case can, thus, provide insights into creative talents in developing countries and add to the current literature, bridge the gap of the current understanding of creative talents in YRDBA – the innovation and development center in China and provide a reference for policymakers when making macro decisions.

The PRMS model was established for Zhenjiangguan watershed in the upper reach of the Minjiang River basin, China. The results showed that PRMS had an acceptable performance in simulating monthly runoff in the study area. The analysis on the impacts of precipitation changes on hydrological processes indicated that both runoff and evapotranspiration increased with the increase of precipitation. Moreover, evapotranspiration had larger sensitivity to the change of precipitation than runoff.

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