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In order to explore the mechanism of digital economy enabling high-quality development in the Yellow River Basin and provide reference for the full implementation of major national strategies for ecological protection and high-quality development in the Yellow River Basin, based on the theoretical analysis of the relationship between digital economy and high-quality development, the evaluation index systems of high-quality development level and digital economy development level were built respectively. Based on the panel data of 60 cities along the Yellow River from 2011 to 2020, the entropy method was used to measure the digital economy development level index and high-quality development level index in the Yellow River Basin. Then, taking the high-quality development level as the explained variable, the digital economy development level as the core explanatory variable, the industrial structure upgrading and green technology innovation as the mechanism variables, and the environmental level and employment level as the control variables, the benchmark measurement model and the mechanism test model were built. Through the model regression, the mechanism of digital economy promoting the high-quality development of the Yellow River Basin was empirically analyzed, and the empirical analysis results were endogenous and robust. The results show that the high-quality development of the Yellow River Basin empowered by the digital economy is significant, and its internal mechanism is to promote the upgrading of industrial structure and green technology innovation. There are spatial differences in the high-quality development of the Yellow River Basin enabled by the digital economy. Its role is most obvious in the middle reaches of the Yellow River and relatively smaller in the upper and lower reaches. It puts forward policy suggestions such as comprehensively promoting the development of digital economy in the Yellow River Basin and further enabling high-quality development, promoting the upgrading of industrial structure, improving the level of green technology innovation, implementing regional differentiation development strategy, and promoting regional integration development. [ABSTRACT FROM AUTHOR]

The allocation and regulation of water resources in the Yellow River Basin is crucial for the economic development and people's lives of various regions. In order to improve the prediction accuracy of the Yellow River water level, a water resources planning and prediction model of the Yellow River was proposed, which combined the sparse search algorithm (SSA) and the long short-term memory (LSTM) network. In other words, after optimizing the hyper-parameters of LSTM model by SSA algorithm, the Yellow River water level was predicted. The results show that the EMAP(Mean Absolute Percentage Error), ERMS (Root Mean Square Error), EMA (Mean Absolute Error) and R² of SSA-LSTM model are 0.006 3, 0.030 4, 0.024 7 and 0.994 5 respectively. Compared to Multi-Layer Perception (MLP) and LSTM control models, the EMAP, ERMS and EMA of the SSA-LSTM model are significantly reduced, while R² improved. The difficult issue of manual parameter selection in LSTM model is solved by using SSA automatic parameter selection. This method can not only greatly reduce the training time of the model, but also find the optimal network parameters, so as to exercise the best performance of the model. The SSA-LSTM model has good accuracy and robustness in predicting the water level of the Yellow River, which can provide a basis for the regulation of water resources in the Yellow River. [ABSTRACT FROM AUTHOR]

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In view of the issue that the runoff change was affected by many factors, characterized by randomness and nonlinearity and was difficult to predict accurately, based on the data of daily average runoff, daily precipitation and daily average sediment concentration of Huayuankou Hydrometrical Station on the Yellow River from 2008 to 2012, a multivariable input TCN Attention model combined with time convolution neural network (TCN) and attention mechanism was proposed to predict the daily runoff of the station, LSTM model and TCN model were selected for prediction and comparison experiments. The results show that the prediction performance of TCN model and TCN Attention model is better than that of LSTM model; The Attention mechanism can improve the prediction performance of TCN model by adjusting the feature vector weight. Compared with TCN model, the MAE, RMSE and MAPE values of TCN Attention model have been decreased by 20.25%, 24.90% and 24.39% respectively; TCN Attention model has better generalization performance, which can improve the prediction level of daily runoff. [ABSTRACT FROM AUTHOR]

In order to improve the quality of ecological environment protection, wireless sensor network (WSN) is widely used in the ecol cal environment intelligent monitoring in the Yellow River Basin. The energy consumption and lifespan of sensor nodes are the key iss faced in wireless sensor network routing. In order to reduce the energy consumption of nodes in the routing process and prevent nodes from ing prematurely, the evolutionary algorithm was applied to WSN routing optimization. This paper improved the traditional optimization alg rithm and applied the improved adaptive evolution algorithm to routing optimization, and randomly selected 100-node WSN topology model verify the effectiveness of the algorithm. The results show that compared with the traditional evolutionary algorithm and ant colony algorithm the algorithm in this paper effectively reduces the energy consumption of sensors in the routing optimization of wireless sensor networks, in proves the algorithm convergence speed, prolongs the life of WSN, enhances the connection reliability and realizes real-time monitoring an protection of ecological environment in the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Water quality prediction is the basic and prerequisite work for the management of water resources and prevention and control of water pollution, hut the research on water quality prediction in the Yellow River basin is relatively lagged behind. In order to improve the performance of the LSTM water quality prediction model mid increase its generalization ability, according to the periodic and non-linear characteristics of water quality changes, taking the dissolved oxygen concentration of the Xiaolangdi Reservoir on the Yellow River as the research object, a combination of convolutional neural network CNN and length was constructed. The CNN-LSTM prediction model of the time memory network LSTM had been verified by experiments. The model can efficiently extract water quality feature information and perform time series prediction. The prediction error is lower than that of the LSTM model. The average absolute error of the predicted value and the root mean square error arc 19.72% and 10.44% lower than that of the LSTM model respectively. The prediction of larger mid smaller values is more accurate mid it has better generalization performance. [ABSTRACT FROM AUTHOR]

Copyright of Yellow River is the property of Editorial Board of Yellow River and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Yellow River is the property of Editorial Board of Yellow River and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the development of internet, new-generation information technology represented by big data, AI and IOT, is accelerating the industrial integration among agriculture, manufacturing and service industries and driving high quality development of China’ s economy. In order to promote the application of new generation information technology in the process of ecological protection and high quality development in the Yellow River basin, this paper analyzed the current situation of natural resources, social and economic development and industrial composition of the Yellow River basin. Then on the basis of discussing the problems faced by ecological protection and high quality development of the basin, this paper put forward the general framework by using new-generation information technology to promote ecological protection and high quality development of the basin, that is to establish the air-space-ground integrated management of ecological environment, improve the comprehensive flood prevention and early warning system, maintain the management and control system of water resources in the Yellow River water supply area, strengthen the optimization and adjustment of industrial structure in the basin and establish a harmonized industrial chain in the basin. On the basis of the framework, it was suggested to establish an air-space-ground integrated big data platform, which could accelerate the ecological restoration along the Yellow River basin and ensure high quality development of the basin. [ABSTRACT FROM AUTHOR]

Copyright of Yellow River is the property of Editorial Board of Yellow River and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Yellow River is the property of Editorial Board of Yellow River and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Yellow River is the property of Editorial Board of Yellow River and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Yellow River is the property of Editorial Board of Yellow River and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)