New quality productive forces are the core driving force for the construction of a modern industrial system. To provide references for the deep transformation and upgrading of industries in the Yellow River Basin and the construction of a modern industrial system, this paper analyzes how new quality productive forces enable the deep transformation and upgrading of traditional industries in the Yellow River Basin towards high-end, intelligent, and green development. It also examines the internal logic of how disruptive and frontier technological innovations empower emerging and future industries. Based on the current development status of the Yellow River Basin, this paper identifies issues in the empowerment process of new quality productive forces, such as weak technological innovation foundations, insufficient supply of high-quality labor, high pressure for green transformation, insufficient vitality of data elements, and imperfect new forms of production relationships. Therefore, this paper proposes practical pathways to strengthen the empowerment of new quality productive forces in the Yellow River Basin’s modern industrial system, including strengthening the cultivation of high-quality labor, reinforcing technological support, facilitating the flow of data elements, accelerating green and low-carbon transformation, and deepening institutional and mechanism reforms.

Due to changes in climate and human factors, surface runoff will also change. In this study, in order to measure the contribution of climate and human factors to the change of runoff in the middle and upper reaches of the Huai River from 1982 to 2019, this study first used Mann Kendall trend analysis to conduct a sudden change analysis of the flow data in the basin, and divided the study time period into a baseline period and a mutation period. The contribution of climate and human factors to the runoff changes in the middle and upper reaches of the Huai River was quantitatively measured based on the extended Budyko model at three-time scales: month, season, and year. The results are as follows: a) On the month scale, the months in which climatic factors led to an increase in runoff were January, February, April, May, June, and December, and the months in which they led to a decrease in runoff were March, July, August, September, October, and November; anthropogenic factors increased the runoff depth of the middle and upper Huaihe River in January, October, November, and December, and decreased it in the rest of the months. b) On a seasonal scale, human factors are the dominant factor in reducing runoff in spring, summer, and autumn. Climate factors increase runoff in all four seasons, with the increase being smaller than the decrease caused by human factors. In winter, climate and human factors have a small-scale increasing effect on runoff changes. c) On an annual scale, climatic factors led to an increase in runoff depth of 4.71 mm, and human factors led to a decrease in runoff depth of 89.75 mm, and human factors had a greater effect on runoff change than climatic factors.

To provide theoretical support for policy formulation related to ecological protection and high-quality development in the Yellow River Basin, this study constructs a green development evaluation index system comprising four dimensions(economic greenness, social greenness, environmental greenness, and governmental support) and eight subsystems(economic growth, industrial structure, green lifestyle, social coordination, pollution reduction and resource efficiency, resource endowment, environmental governance, and infrastructure). Using 61 prefecture-level cities in the Yellow River Basin as the research sample and the period 2005-2022 as the study horizon, we adopt the coupling coordination model, regional coordination model, and obstacle degree model to measure the coupling coordination level among subsystems, the regional coordination level, and to diagnose obstacle factors. The results show that: a) The coupling coordination level among the subsystems of green development in the Yellow River Basin has increased year by year, yet remained relatively low by the end of the study period (on the verge of imbalance), with significant regional disparities; b) The regional coordination level of green development is generally low, at a barely coordinated stage, and the ranking of subsystem coordination levels by the end of the study period is as follows: resource endowment, green lifestyle, pollution reduction and resource efficiency, economic growth, environmental governance, infrastructure, social coordination, and industrial structure (ranging from well-coordinated, primary coordination, primary coordination, barely coordinated, verge of imbalance, verge of imbalance, mild imbalance, to moderate imbalance, respectively); c) Among the eight subsystems, infrastructure, environmental governance, and social coordination are the main obstacle factors to green collaborative development, while among the four dimensions, governmental support emerges as the key to enhancing collaborative green development in the Yellow River Basin. Accordingly, this study recommends: Strengthening the comprehensiveness and integrality of green development, increasing governmental support for infrastructure construction and environmental governance, promoting urban-rural integration and social coordination, enhancing environmental greenness, and advancing pollution reduction and resource efficiency.

Clarifying water security patterns plays an important role in the sustainable management of water resources and the protection of ecological environment. Based on five periods of land use data from 2000 to 2020, the spatial and temporal characteristics of water supply, water demand and water resource risk zones in Gansu Province were explored by using the InVEST model, the supply and demand index method and the spatial correlation model, and the spatial correlation analyses of water resource risk zones were carried out by means of the global Moran’s index and the local Moran’s index. The results show that: during the 21 years, the spatial distribution pattern of water production in Gansu Province shows high in the southeast and low in the northwest, and the water production shows a good trend of increasing year by year; the high value areas of water demand are mainly concentrated in the urban built-up areas and the agricultural concentration zones in the Hexi oasis, and the water demand is relatively balanced in different periods; the high-risk areas of water resources are mainly located in Hexi Corridor area, and the low-risk areas are located in the East Longnan area and near the northern foot of Qilian Mountains; the water resource risk areas, water demand, and water production all show strong spatial correlation in their spatial distribution, Longnan area and the southern part of the Hexi Corridor, near the northern foot of Qilian Mountains; water resources risk area, water demand, water production in the spatial distribution of all show strong spatial correlation.

The Yellow River is characterized by its scarce water resources and abundant sediment, leading to an imbalance between water and sediment dynamics. With the advancement of hydrological monitoring and forecasting capabilities, there have been significant strides in water level and flow monitoring. However, sediment concentration monitoring technology has yet to see a breakthrough. To address the issue of sediment monitoring, the Hydrology Bureau of the Yellow River Conservancy Commission developed the HHSW·NUG-1 photoelectric sediment meter. This instrument overcomes the limitations of traditional optical sediment measurement methods, which are constrained by flow velocity and range requirements. It enables year-round online continuous monitoring, providing stable and highly accurate data. This paper focuses on the comparative testing and application of this instrument at the Xiaolangdi and Huayuankou hydrological stations on the Yellow River. The study found that during the comparative testing period, the Xiaolangdi Reservoir was undergoing regulation, resulting in significant water and sediment processes and favorable sediment conditions. The performance of the photoelectric sediment meter was relatively stable, with no abrupt changes in sediment data. The overall trend of the data from the photoelectric sediment meter was consistent with the unit sediment concentration. The photoelectric sediment meter is capable of real-time online monitoring of sediment concentration data, accurately capturing the rise and fall processes and trends of sediment concentration. This aids in the rational arrangement of sediment discharge measurements at the stations, allowing for more precise judgments on abnormal sediment conditions and peaks, and the rational scheduling of tests. This not only reduces labor intensity but also enhances the rationality and timeliness of the testing process.

To provide insights for accelerating the development of new productive forces in the Yellow River Basin, based on an analysis of the internal mechanisms driving the development of new productive forces, an evaluation index system for new productive forces is constructed from three dimensions: laborers, labor objects, and means of labor. Using panel data from nine provinces in the basin, the vertical and horizontal range method is employed to measure the development level of new productive forces from 2012 to 2022, analyzes regional imbalance with Kernel density estimation and Theil index, and examines spatial patterns and correlation via Moran’s Index and ArcGIS. The result show that: a) The overall level is low but exhibits a good upward trend, with growth across all nine regions. b) Significant disparities exist: Shandong ranks highest, followed by Sichuan and Shaanxi, while Qinghai and Gansu are relatively low; downstream regions outperform the middle reaches, which in turn surpass the upstream. c) Spatial differences are mainly reflected in the gap between upstream and mid-downstream areas, and within the upstream, Sichuan differs greatly from others, with obvious polarization remaining by the end of the study period. d) There is significant positive spatial correlation, with a pattern of “higher in the east and lower in the west” and “higher in the south and lower in the north”. Shandong, Henan and Shaanxi are high-high clusters; Shanxi and Inner Mongolia are low-high clusters; Gansu, Ningxia and Qinghai are low-low clusters; Sichuan is a high-low cluster. The following recommendations are proposed: Adapting to local conditions to leverage strength and address weakness; Promoting regional linkage and coordinated development; Achievement transformation; And advancing industrial upgrading to create more opportunities for new productive forces.

New quality productive forces serve as the endogenous driver for rural revitalization, while rural revitalization provides an expansive practical arena for their development. To offer theoretical and practical insights into advancing new quality productive forces and facilitating  rural revitalization in the Yellow River Basin, this study elucidates their coupling mechanism. Using panel data from nine provinces (autonomous regions) in the basin from 2012 to 2022, it measures the development levels of both dimensions via a projection pursuit model optimized by an accelerated genetic algorithm. An improved coupling coordination degree model quantifies their synergistic relationship. Additionally, a Coordinated Influence Model identifies key drivers affecting their coupling coordination. Results indicate: 1) Steady improvements in both dimensions across all provinces (regions), exhibiting a “concave relationship”. 2) Sustained growth in the three subsystems of laborers, labor objects, and labor materials in new quality productive  forces. 3) New quality productive forces played a pivotal role in transitioning most provinces (regions) from mild imbalance to primary coordination. 4) Enhancement of new quality productive forces primarily relied on contributions from labor objects, though this contribution gradually diminished over time. Laborers typically exerted inhibitory effects, while labor materials demonstrated relatively minor influence. Recommendations include stimulating growth in labor objects, alleviating constraints from laborers, and strengthening the driving effect of labor materials to achieve higher-level coordinated development in the Yellow River Basin.

To clarify the seasonal characteristics and succession patterns of the phytoplankton community structure in the Yellow River Wetland Park of Zhongwei, Ningxia, a systematic investigation was conducted on the species composition, density and biomass of phytoplankton in the park in spring (April), summer (July) and autumn (October) of 2024. Through non-metric multidimensional scaling (NMDS) and niche analysis methods, the community structure of phytoplankton was analyzed. The results showed that a total of 7 phyla and 43 species of phytoplankton were detected in the Yellow River Wetland Park of Zhongwei, among which the Chlorophyta had the most species (16 species). There were 8 dominant species, and Platymonas subcordiformis was the dominant species at all three sampling points, with a significant dominance. The average density of phytoplankton was 420.11 million/L and the average biomass was 5.049 mg/L. The phytoplankton species in the Yellow River Wetland Park of Zhongwei were relatively few, and the community structure was relatively simple and unstable. There were significant differences in phytoplankton populations at different sampling times (P < 0.05). The niche overlap index of dominant species in different seasons was significantly different, and the resource utilization was significantly different.

Exploring the spatio-temporal characteristics and influencing factors of the new and old growth drivers conversion in the Yellow River Basin is crucial for fostering high-quality development in the basin. The evaluation index system is constructed according to the dimensions of new and old growth drivers, and the Entropy Weight-TOPSIS method and the GMM model are introduced to portray the spatio-temporal characteristics and the influencing factors of the new and old growth drivers conversion in the basin. The findings reveal several key insights: a) The conversion of old and new growth drivers in the Yellow River Basin unfolds in three distinct stages: a period of fluctuating growth followed by a gradual decline, ultimately leading to a steady upward trajectory. This trend reflects the alternation and replacement of the driving forces behind economic growth in the basin. b) Significant disparities exist in the level of new and old growth drivers  conversion among the upper, middle, and lower reaches of the Yellow River Basin, resulting in a spatial distribution pattern characterized by higher levels in the east and lower levels in the west. c) Regarding influencing factors, labor force, infrastructure, informationization, technology research and development, and high-end human resources exert positive effects on the conversion of old and new growth drivers in the basin. Conversely, financial development negatively impacts this conversion process, while the influence of capital remains non-significant. Therefore, it is imperative for the Yellow River Basin to prioritize the cultivation of new growth drivers alongside existing resources. Tailored policies should be designed, formulated, and implemented to facilitate the conversion of old and new growth drivers according to local conditions. Additionally, efforts should be made to maximize the influence and linkage effects of the basin’s central cities.

In order to quantitatively analyze the decoupling relationship between water resources utilization and economic development in the Yellow River Basin in Shandong Province, based on the relevant statistical data of the Yellow River Basin in Shandong Province from 2012 to 2022, the water footprint theory, Tapio decoupling model and LMDI decomposition model were used to analyze the water resources utilization situation, the decoupling relationship between water resources utilization and economic development and their decoupling factors in the Yellow River Basin in Shandong Province. The results show that the total water footprint of the Yellow River Basin in Shandong Province shows a fluctuating upward trend, with agricultural water footprint accounting for the largest proportion and ecological water footprint growing fastest, and the average per capita water footprint for many years is lower than the internationally recognized water shortage line. Water resources mainly come from internal supply, and the problem of water safety is prominent, and the economic benefits of water footprint continue to grow, with an average annual growth rate of 5.06%; There is a weak decoupling relationship between water resources utilization and economic development as a whole, between agricultural water use and economic development as a whole, and between industrial water use and economic development as a whole. The decoupling rate of Jinan, Jining, Tai’an and Heze is 100%, Zibo and Dezhou is 80%, and Dongying, Liaocheng and Binzhou are relatively weak. Water use efficiency effect is the main driving factor to promote decoupling, and economic scale effect is always the main factor to inhibit decoupling; The water use efficiency of most prefecture-level cities has a good decoupling relationship. Except Dongying’s economic scale decoupling decomposition index in 2016 is negative, other prefecture-level cities are positive.

To accelerate the cultivation of new productive forces and provide reference for implementing the major national strategies of ecological protection and high-quality development in the Yellow River Basin, based on the connotation of new productive forces, this paper constructs an “technology-factor-industry” analytical framework to explore the inherent logic of how new productive forces empower ecological protection and high-quality development in the Yellow River Basin. Specifically, revolutionary technological breakthroughs provide new driving forces, innovative allocation of production factors strengthens data empowerment, and in-depth transformation and upgrading of industries provide carrier support. It points out that empowering ecological protection and high-quality development in the Yellow River Basin with new productive forces still faces challenges such as insufficient innovation drive, the need to improve the level of factor integration and allocation, and lagging industrial transformation and upgrading. Given the reality of the Yellow River Basin, this paper proposes an enhancement path for empowering ecological protection and high-quality development in the Yellow River Basin with new productive forces: improving the science and technology innovation system, achieving high-level self-reliance and self-strengthening in science and technology, deepening factor market allocation, promoting the flow of data factors, accelerating the transformation and upgrading of industrial structure, and enhancing the competitive advantage of the modern industrial system.

In order to offer policy references for promoting the green total factor productivity (GTFP) in the Yellow River Basin driven by digital finance, this study utilized the panel data of 72 prefecture-level cities in the Yellow River Basin from 2011 to 2022. Utilizing a comprehensive analytical framework that integrates individual and time fixed-effects models, threshold regression models, and spatial econometric models, the research investigates the impact effects of digital finance on GTFP in the Yellow River Basin and elucidates its underlying mechanisms.The results indicate that: a) Digital finance significantly promotes the enhancement of GTFP in the Yellow River Basin, with the green effect from the digitalization level of digital finance being the most substantial. b)Both a capable government and an effective market exhibit dual threshold effects in the process of digital finance influencing green total factor productivity in the Yellow River Basin. Moreover, the positive effect of digital finance on GTFP demonstrates nonlinear incremental enhancement as government governance capacity and marketization levels improve. c) The positive effect of digital finance on GTFP in the Yellow River Basin demonstrates regional heterogeneity and resource dependency heterogeneity, specifically manifested as a greater positive effect in midstream and downstream cities compared to upstream cities, and a larger positive effect in non-resource-based cities than in resource-based cities. d) The development of digital finance not only elevates the GTFP of the local city but also radiates to enhance that of neighboring cities. The study proposes the following policy recommendations: Persistently advance market-oriented reforms in both breadth and depth to elevate marketization levels. Support green development in the Yellow River Basin through improvements in new infrastructure and structural tax and fee reductions. Optimize measures for digital finance development according to local conditions. Strengthen regional cooperation among cities within the basin, leveraging the “Belt and Road” Initiative as a linkage.

The development of new quality productivity is the key to promoting the high-quality development of the Yellow River Basin. In order to explore the improvement path of the new quality productivity of the manufacturing industry in the Yellow River Basin, based on the TOE theoretical framework, this paper takes 359 listed manufacturing enterprises in the Yellow River Basin in 2022 as research samples and constructs the driving model of the new quality productivity from the three levels of technology, organization, and environment. Two methods, NCA and fsQCA, are used to study and analyze the influencing factors and configuration paths of new quality productivity. The results show that, first, the improvement of new quality productivity is affected by the coordination and matching effects of technology, organization, and environment, and no single factor can constitute new quality productivity. Second, there are three configurations for the improvement path of new quality productivity, namely, the technology-driven type composed of technological innovation, digital-intelligence transformation, and government subsidies; the single-factor comprehensive synergy type composed of technological innovation, equity restriction, and government subsidies; and the multi-factor comprehensive synergy type composed of digital-intelligence transformation, equity restriction, supply chain relationships, and government subsidies. Third, technological innovation and government subsidies play a key role in the path of new quality productivity improvement; digital-intelligence transformation and equity restriction have substitution effects, which can improve new quality productivity in the same way.

Accurately assessing the carbon storage of the ecosystem in the Henan section of the Yellow River Basin is of great significance for promoting low-carbon sustainable development in the region and achieving the “dual carbon” goals. Based on carbon density sampling data, a spatial density distribution dataset of carbon density in the Henan section of the Yellow River Basin was constructed. Combined with remote sensing data on land use, a systematic assessment was conducted of the carbon storage patterns and spatio-temporal evolution laws of the ecosystem in the Henan section of the Yellow River Basin for the years 1980, 1990, 2000, 2005, 2010, 2015, and 2020. The Geographic Detector was utilized to explore the influence of natural and socio-economic factors on carbon storage. The results indicate that the average carbon storage in the Henan section of the Yellow River Basin over the past 40 years was 431.16×106 t, with a spatial distribution pattern showing a slight increase from east to west and a decreasing trend from northeast to southwest, with high-value agglomeration areas mainly distributed in the downstream floodplain of the Yellow River. From 1980 to 2000, the ecosystem carbon storage in the Henan section of the Yellow River Basin decreased, followed by an increase, but overall, the carbon storage in the Yellow River Basin from 1980 to 2020 showed a downward trend. Between 1980 and 2020, 82.05% of the region in the Henan section of the Yellow River Basin maintained unchanged carbon storage, while 10.05% experienced a decrease and 7.90% an increase. Among human factors, changes in land use type were the key drivers of dynamic changes in carbon storage in this region, especially the continuous expansion of construction land and encroachment on farmland and grassland, which were the main reasons for the significant decline in carbon storage. Among natural factors, altitude and temperature also had a certain impact on changes in carbon storage.

The upper reaches of the Yellow River (UPYR) serve as the primary source area for the basin’s runoff. It is essential to quantify the impacts of climate change and anthropogenic activities on the variation patterns of runoff in this region to enhance effective water resource management and support informed decision-making within the Yellow River Basin. In this study, we developed the SWAT hydrological model for the upper reaches of the Yellow River, calibrating and validating it from the base period of 1964 to 1980. We systematically evaluated the effects of climate change and human activities—including water usage, reservoir regulation, and land use—on runoff changes from 1981 to 2020. The findings indicate that a) The basin is currently undergoing a significant increase in both precipitation and temperature, with precipitation levels rising at a rate of 8.11 mm per decade and a corresponding warming rate of 0.35 ℃ per decade. It is important to note that there is spatial heterogeneity in the intensity of the impacts of climate change. In the source area, the contribution rate of the Jimai climate factor is 94%. In contrast, in the headway region, the contribution rate decreases to 21%. b) The influence of human activities on runoff attenuation exhibits spatial gradient characteristics, with variations ranging from 60% to 80% between Lanzhou and Toudaoguai. Human water consumption is identified as the principal contributing factor, accounting for approximately 40% to 45% of this phenomenon. The establishment and operation of the reservoir have resulted in a temporal redistribution of runoff, leading to a decrease of 18.11% ± 6.27% during the flood season and an increase of 12.33% ± 4.2% during the non-flood season. c) Between 1964 and 2020, the annual runoff in the upstream region of the Yellow River experienced a decline of 148 million cubic meters per decade. An analysis of the factors contributing to runoff reveals that precipitation recharge is the primary determinant, accounting for approximately 80% ± 11.33%. This is followed by contributions from snow and ice melt, thawing of frozen soil, and groundwater recharge. The findings of this study elucidate the nonlinear superposition effects of climate change and anthropogenic activities in the upper reaches of the Yellow River, thereby providing theoretical support for understanding the variations in upstream runoff in the context of climate change.

In response to the complex characteristics of rivers with abundant sediment in the north, such as the Yellow River, traditional flow measurement technologies face challenges such as inaccuracy due to complex morphologies and susceptibility of measurement capabilities to environmental factors. To address these challenges, this paper proposes a novel intelligent and precise measurement method for open channel flow based on the concept of digital twin and the Bayesian hierarchical model. This method integrates time series prediction and intelligent management technology. Field tests conducted in the "Yellow River Water Diversion to Hebei Province for Replenishing Baiyangdian Lake" project in the Yellow River basin have demonstrated significant advantages of this method. Compared with existing technologies, this method not only solves the problem of inaccurate flow measurement under conditions of unstable flow velocity and scouring and deposition changes but also achieves precise prediction of flow data over small time scales in the future.

Based on the development of method, theory, and the legislation in the process of Yellow River management, this paper proposes the philosophical concept of “method Theory legislation Daoism” in Yellow River management, analyzes the basic principles and rules of dialectical materialism reflected in it, and demonstrates its philosophical scientificity, rationality, and the dialectical relationship between them. Through reviewing the ancient river management ideas, analyzing the Yellow river management strategies of the people at different stages, and comprehending the profound essence of the new era’s water management ideas in Yellow River management, we aim to explore the concepts of “method Theory legislation Daoism” in it, Exploring its dialectical development in the practice of Yellow River control in various historical periods. Analyzing that the Yellow River control is a comprehensive process of the method progress, the ideological development, and the legislation improvement, which is a historical practice of coordinated development of “method Theory legislation Daoism”. The protection and management of the Yellow River should innovate the method of river management, study the theory of water management, clarify the legislation of valley harnessing, and follow the Daoism of harmonious coexistence between humans and water.

Scientific and technological collaboration provides foundational support for high-quality economic development. By continuously introducing new technologies and methods, it enhances production efficiency, reduces costs, promotes industrial upgrading, and injects new vitality into high-quality economic growth. In the Yellow River Basin, the industrial technology level has been steadily rising, the scale of scientific and technological expenditure continues to expand, the number of patent applications and grants is increasing steadily, and the mechanisms for scientific collaboration are improving. However, challenges remain, such as an overly heavy industrial structure, insufficient innovation capacity, uneven economic development among provinces (regions), and a weak foundation for industrial division and collaboration. To address these issues, this paper proposes pathways for scientific and technological collaboration to drive high-quality economic development in the Yellow River Basin: strengthening institutional safeguards for collaboration, elevating the industrial capacity of collaboration, enhancing the effectiveness of collaborative platforms, and fully leveraging the role of talent in scientific collaboration.

To provide a scientific theoretical foundation for promoting the coordinated development of new quality productivity between the Yellow River Basin and Yangtze River Basin, and to offer decision-making references for formulating more rational regional development policies, this study established an evaluation index system for regional new quality productivity development. Employing methods such as entropy method, Theil index, spatial correlation analysis, and QAP analysis, a comparative empirical investigation was conducted on the development levels and influencing factors of new quality productivity in both basins from 2013 to 2022. The results showed that: 1) The Yangtze River Basin exhibited overall higher new quality productivity development indices than the Yellow River Basin, with both basins demonstrating distinct "block segmentation" spatial distribution patterns characterized by incremental improvements from upstream to downstream regions. Notably, the Yellow River Basin exhibited significantly higher polycentric than the Yangtze River Basin. 2) Intra-regional and inter-regional differences in the Yangtze River Basin decreased significantly, with a turning point occurring in 2019 when inter-regional differences fell below intra-regional differences for the first time, signaling a shift toward intra-regional variance dominance. Conversely, differences between eastern and western regions within the Yellow River Basin, along with intra-regional differences in western areas, displayed an expanding trend. 3) Global spatial autocorrelation of new quality productivity development in both basins showed continuous growth during 2013-2022, reflecting intensified spatial agglomeration. Provincial-level spatial correlation intensity in the Yellow River Basin remained weaker than that in the Yangtze River Basin. 4) Regional differences in future-oriented industries and green development emerged as the strongest drivers of new quality productivity differences in the Yangtze River Basin, while innovation-driven industrial development levels and production materials differences constituted the predominant influencing factors in the Yellow River Basin. Based on these findings, the study proposed recommendations including optimizing resource allocation to enhance regional equilibrium, implementing innovation-driven strategies with differentiated approaches for cultivating new quality productivity, refining targeted policy frameworks, and strengthening inter-regional collaborative development mechanisms.

The shortage of water resources has become a global challenge, which seriously restricts the economic development and social stability of water-deficient areas. The evaporation loss of water bodies is a major cause of this phenomenon. In order to solve the problem of evaporation loss, domestic and foreign scholars have been constantly exploring effective measures to suppress water surface evaporation to improve the utilization rate of water resources. At present, the most commonly used technologies are chemical reagent method, biological measure method, physical material covering method, floating photovoltaic technology covering method and so on. In this paper, the published research results are reviewed, and the characteristics, main advantages and disadvantages of common technologies are summarized. From the current research results and comprehensive benefits, the physical material covering method is the most suitable anti-evaporation technology for large-scale application, and its inhibition rate of water evaporation is up to 90 % or more. If it is combined with photovoltaic technology to achieve the dual benefits of power generation and reduction of water evaporation, it should be the focus of future research by scholars. In addition, in order to realize the sustainable development of water resources, we not only need to pay attention to economic benefits, but also need to fully consider ecological benefits.

In order to improve the prediction accuracy of seepage water level of hydropower station dam foundation, a BP neural network model based on random forest (RF-BP model) was proposed. Taking Baihetan Hydropower Station as an example, the data of 18 seepage measurement points at the dam foundation from August 1, 2021 to February 23, 2023 were analyzed. The GA (Genetic Algorithm)-BP, PSO (Particle Swarm Optimization)-BP, RF, LSTM (Long Short Term Memory Network)-BP models were selected to compare the prediction accuracy with the RF-BP model. Considering that there was a certain correlation between the seepage water level and the reservoir water level, the Pearson correlation coefficient of the two was calculated. The results show that the RF-BP model has the smallest MAE, RMSE and MAPE and the highest prediction accuracy at the typical measurement points of OH-WML1-1, OH-WML1-2 and OH-WML5-3, which highlights the significant effect of random forest algorithm in optimizing selection factors. The stronger the correlation between the seepage water level and the reservoir water level at the measurement point, the higher the prediction accuracy of the RF-BP model, indicating that the correlation between the seepage water level and the reservoir water level has an important impact on the prediction accuracy.