Search Results (24)

Inter-basin water transfer projects are core infrastructure for achieving sustainable water resource allocation and addressing regional water scarcity, and pumping station units, as their critical energy-consuming and operation-controlling components, are vital to the projects’ sustainable performance. With the growing complexity and scale of these projects, pumping station units have become more intricate, leading to a gradual rise in failure rates. However, existing fault diagnosis methods are relatively backward, failing to promptly detect potential faults—this not only threatens operational safety but also undermines sustainable development goals: equipment failures cause excessive energy consumption (violating energy efficiency requirements for sustainability), unplanned downtime disrupts stable water supply (impairing reliable water resource access), and even leads to water waste or environmental risks. To address this sustainability-oriented challenge, this paper focuses on the fault characteristics of pumping station units and proposes a comprehensive and accurate fault diagnosis model, aiming to enhance the sustainability of water transfer projects through technical optimization. The model utilizes advanced algorithms and data processing technologies to accurately identify fault types, thereby laying a technical foundation for the low-energy, reliable, and sustainable operation of pumping stations. Firstly, continuous wavelet transform (CWT) converts one-dimensional time-domain signals into two-dimensional time-frequency graphs, visually displaying dynamic signal characteristics to capture early fault features that may cause energy waste. Next, the multi-head attention mechanism (MHA) segments the time-frequency graphs and correlates feature-location information via independent self-attention layers, accurately capturing the temporal correlation of fault evolution—this enables early fault warning to avoid prolonged inefficient operation and energy loss. Finally, the improved convolutional neural network (CNN) layer integrates feature information and temporal correlation, outputting predefined fault probabilities for accurate fault determination. Experimental results show the model effectively solves the difficulty of feature extraction in pumping station fault diagnosis, considers fault evolution timeliness, and significantly improves prediction accuracy and anti-noise performance. Comparative experiments with three existing methods verify its superiority. Critically, this model strengthens sustainability in three key ways: (1) early fault detection reduces unplanned downtime, ensuring stable water supply (a core sustainable water resource goal); (2) accurate fault localization cuts unnecessary maintenance energy consumption, aligning with energy-saving requirements; (3) reduced equipment failure risks minimize water waste and environmental impacts. Thus, it not only provides a new method for pumping station fault diagnosis but also offers technical support for the sustainable operation of water conservancy infrastructure, contributing to global sustainable development goals (SDGs) related to water and energy. Full article

Cultural ecosystem services (CES) represent fundamental expressions of human-environment interactions. A comprehensive assessment of CES supply and demand offers a robust scientific foundation for optimizing the transformation of ecosystem service values to improve human well-being. This study integrates multi-source datasets and employs Maximum Entropy (MaxEnt) modeling with the ArcGIS platform to analyze the spatial distribution of CES supply and demand in Hunan Province, a typical mountain tourism regions in China. Furthermore, geographical detector methods were used to identify and quantify the driving factors influencing these spatial patterns. The findings reveal that: (1) Both CES supply and demand demonstrate pronounced spatial heterogeneity. High-demand areas are predominantly concentrated around prominent scenic locations, forming a “multi-core, clustered” pattern, whereas high-supply areas are primarily located in urban centers, water systems, and mountainous regions, exhibiting a gradient decline along transportation corridors and river networks. (2) According to the CES supply-demand pattern, Hunan Province can be classified into demand, coordination, and enhancement zones. Coordination zones dominate (45–70%), followed by demand zones (20–30%), while enhancement zones account for the smallest proportion (5–20%). (3) Urbanization intensity and land use emerged as the primary drivers of CES supply-demand alignment, followed by vegetation cover, distance to water bodies, and population density. (4) The explanatory power of two-factor interactions across all eight CES categories surpasses that of any individual factor, highlighting the critical role of synergistic multi-factorial influences in shaping the spatial pattern of CES. This study provides a systematic analysis of the categories and driving factors underlying the spatial alignment between CES supply and demand in Hunan Province. The findings offer a scientific foundation for the preservation of ecological and cultural values and the optimization of spatial patterns in mountain tourist areas, while also serving as a valuable reference for the large-scale quantitative assessment of cultural ecosystem services. Full article

Neglecting ecosystem services has impeded sustainable urban–rural development, particularly in terms of the efficient flow of water supply services between urban and rural areas. This study focuses on the Fenhe River Basin, evaluating water supply and demand at the sub-basin, as well as county levels. Using the InVEST model to analyze basin-level geographic, meteorological, hydrological, and socio-economic data, the study reveals significant spatial and temporal mismatches between water supply and demand from 2010 to 2020. Through the calculated ecosystem services supply and demand ratio (0.3731 in 2010, −0.1555 in 2015, and −0.1063 in 2020), it is found although both supply and demand increased over the period, persistent deficits emerged, with water supply concentrated in upstream areas and demand primarily in downstream regions. The improved network connectivity by 2020, supported by water-saving policies and technological advancements, partially alleviated earlier imbalances. This research contributes a multi-scale framework to analyze ecosystem service flows and compensation mechanisms across grid, sub-basin, and county scales. Overall, the study underscores that research into ecological compensation plays a crucial role in enabling efficient resource flow, enhancing governance systems, and fostering an ecologically friendly urban–rural development model. Full article

Understanding the flow processes and pattern optimization of ecosystem services (ESs) supply and demand is crucial for integrated regional ecological management. However, the understanding of the flow process of ESs at the 1 km grid scale is still limited, especially in areas dominated by mineral resource development. The landscape in these areas has undergone significant changes due to mining activities. It is urgent to construct a regional management model that integrates the flow of ecosystem services and mine restoration. This study developed a framework that links ecosystem service flows (ESFs) and ecological security patterns (ESP) based on multi-source ecological monitoring data, constructed an ES supply-demand flow network through the flow properties, and determined the sequence and optimization strategies for mine rehabilitation to achieve integrated regional management. The results show that, except for food production (FP), other services were in surplus overall, mostly in synergistic relationships, but the spatial distribution of their supply and demand was not coordinated. Surplus areas were located mainly in the eastern woodlands, and deficit areas were located in the northwestern production agglomeration centers, suggesting that areas of supply-demand imbalance can be mitigated through ecological integration. Among these, water yield (WY) had a small number of sources and sinks and is limited in area range. Habitat quality (HQ) sources and sinks had the largest area coverage and the highest number. The distribution of ESF corridors, influenced by factors such as the number of sources and sinks, flow characteristics, and spatial resistance, varied significantly. HQ exhibited a more uniform distribution range, while WY had a longer average length of flow path. Overlaying ecological and mining factors, we identified ecological strategic spots, important supply areas, beneficiary areas, and mine priority restoration areas to further optimize the overall layout and rationally allocate the intrinsic structure of the patches based on ES supply and demand. Full article

Reservoir sediment severely impacts water supply in water-scarce regions, making reservoir dredging an urgent global issue. The investment required for deep-water dredging far exceeds that for dry land dredging. Therefore, against the backdrop of the national water network construction, this study focuses on a typical inter-basin water transfer project in Northern China. To increase the proportion of dry land dredging volume and save costs, this study uses compensation reservoirs to replace the emptied reservoir in undertaking water supply tasks as a constraint. Single-objective optimization models for single reservoirs and multi-objective optimization models for reservoir groups are established, using game theory comprehensive subjective and objective weighting methods to select the optimal solution. The following conclusions are drawn from comparing the water supply effects under various emptying sequences: the optimal sequence for emptying reservoirs should be determined through precise quantitative analysis; as the dredging is completed, the water supply tends to stabilize; the satisfaction with the water supply and the variance of the water shortage rate are primarily related to reservoirs with a large inflow and storage capacity; dredging occurs according to the descending order of the storage capacity of reservoirs; and the startup proportion of pump stations shows an increasing trend. Full article

Burst events in Water Distribution Networks (WDNs) pose a significant threat to the safety of water supply, leading people to focus on efficient methods for burst localization and prompt repair. This paper proposes a multi-stage burst localization method, which includes preliminary region determination and precise localization analysis. Based on the hydraulic model and spatio-temporal information, the effective sensor sequences and monitoring areas of the nodes are determined. In the first stage, the preliminary burst region is determined based on the monitoring region of sensors and the alarm sensors. In the second stage, localization metrics are used to analyze the dissimilarity degree between burst data from the hydraulic model and the monitoring data from the effective sensors at each node. This analysis helps identify candidate burst nodes and determine their localization priorities. The localization model is tested on the C-Town network to obtain comparative results. The method effectively reduces the burst region, minimizes the search region, and significantly improves the efficiency of burst localization. For precise localization, it accurately localizes the burst event by prioritizing the possibilities of the burst location. Full article

Food security remains a critical global challenge, increasingly threatened by the adverse effects of climate change on agricultural productivity and food supply chains. Ensuring the stability, availability, and accessibility of food resources necessitates innovative strategies to assess and mitigate climate-related risks. This study presents a comprehensive analysis of the impact of climate change on global food systems, focusing on the risk assessment and optimization of food supply chains from the perspective of importers. Deploying the Analytical Hierarchy Process (AHP), this study evaluates climate change risks associated with seven different suppliers for three key crops, considering a range of factors, including surface temperature, arable land, water stress, and adaptation policies. Utilizing these assessments, a multi-objective optimization model is developed and solved using MATLAB (R2018a)’s Genetic Algorithm, aiming to identify optimal suppliers to meet Qatar’s food demand, with consideration of the economic, environmental, and risk factors. The findings underscore the importance of a comprehensive approach in managing food supply chains and offer insights to enhance the resilience and sustainability of global food systems amid climate uncertainties. This study contributes to the literature by applying AHP and multi-objective optimization in climate risk management within food systems, providing valuable perspectives for policymakers and stakeholders in the agricultural sector. Furthermore, the multi-objective optimization model analyzed three crop networks, yielding total costs of USD 16 million, USD 6 million, and USD 10 million for tomatoes, onions, and cucumbers, respectively, with associated CO₂eq emissions and risk percentages. The findings reveal concentrated global vegetable markets, with major importers accounting for over 60% of imports, though the leading importers differ across crops, highlighting regional demand and production disparities, potentially impacting food security and supply chain resilience. Full article

The Central Valley of California is one of the most prolific agricultural regions in the world. Agriculture is reliant on the conjunctive use of surface-water and groundwater. The lack of available surface-water and land-use changes have led to pumping-induced groundwater-level and storage declines, land subsidence, changes to streamflow and the environment, and the degradation of water quality. As a result, in part, the Sustainable Groundwater Management Act (SGMA) was developed. An examination of the components of SGMA and contextualizing regional model applications within the SGMA framework was undertaken to better understand and quantify many of the components of SGMA. Specifically, the U.S. Geological Survey (USGS) updated the Central Valley Hydrologic Model (CVHM) to assess hydrologic system responses to climatic variation, surface-water availability, land-use changes, and groundwater pumping. MODFLOW-OWHM has been enhanced to simulate the timing of land subsidence and attribute its inelastic and elastic portions. In addition to extending CVHM through 2019, the new version, CVHM2, includes several enhancements as follows: managed aquifer recharge (MAR), pumping with multi-aquifer wells, inflows from ungauged watersheds, and more detailed water-balance subregions, streamflow network, diversions, tile drains, land use, aquifer properties, and groundwater level and land subsidence observations. Combined with historical approximations, CVHM2 estimates approximately 158 km³ of storage loss in the Central Valley from pre-development to 2019. About 15% of the total storage loss is permanent loss of storage from subsidence that has caused damage to infrastructure. Climate extremes will likely complicate the efforts of water managers to store more water in the ground. CVHM2 can provide data in the form of aggregated input datasets, simulate climatic variations and changes, land-use changes or water management scenarios, and resulting changes in groundwater levels, storage, and land subsidence to assist decision-makers in the conjunctive management of water supplies. Full article

Cities located in the plain river network area possess abundant water resources. However, due to urbanization and industrialization, there is a severe water shortage problem caused by poor water quality. To overcome this issue, a multi-objective optimal allocation model of water quantity and quality is proposed. The model considers regional water resources, economic, social, and environmental requirements and uses the NSGA-II genetic algorithm for model solution. Furthermore, to evaluate and analyze the degree of spatial equilibrium of regional water resources and how it relates to economic factors, the study uses the spatial equilibrium theory of water resources and the Gini coefficient of water resources. Jingjiang, a city in Jiangsu Province characterized by a typical plain river network area, was selected as the study area. The results of the optimal allocation of water resources in Jingjiang City show that: (1) total water consumption and chemical oxygen demand (COD) emissions for the current planning period are within their respective limits. In addition, the implementation of the water conservation program has resulted in a 5% reduction in total water shortages and a reduction of COD emissions by 1276 tons, (2) the structure of the water supply in Jingjiang City has been optimized; more than 90% of Ⅳ~V surface water is used for agriculture, and the domestic water supply is mainly from transit water, which effectively ensures that high-quality water is used in the domestic water supply, (3) the spatial equilibrium coefficient of water resources per sub-area is between 0.33 and 0.74, indicating an unbalanced or almost unbalanced level. The application of a water conservation program has resulted in the improvement of the spatial equilibrium level of water resources in each sub-area, with an overall spatial equilibrium of 0.64, indicating a more balanced level; the degree of matching of water resources with population, GDP, and land area is at the matching level, (4) according to the Gini coefficient of the distribution of water resources, the plains river network area displays a better match between water resources and economic and social factors of each water receiving area, thanks to its unique geographical location and natural conditions. This study can serve as a decision-making reference for addressing the urban water quality water shortage problem in the plain river network area. Full article

Unidentified abortion, of which leptospirosis, brucellosis, and ovine enzootic abortion are important factors, is the main cause of disease spread between animals and humans in all agricultural systems in most developing countries. Although there are well-defined risk factors for these diseases, these characteristics do not represent the prevalence of the disease in different regions. This study predicts the unidentified abortion burden from multi-microorganisms in ewes based on an artificial neural networks approach and the GLM. Methods: A two-stage cluster survey design was conducted to estimate the seroprevalence of abortifacient microorganisms and to identify putative factors of infectious abortion. Results: The overall seroprevalence of Brucella was 70.7%, while Leptospira spp. was 55.2%, C. abortus was 21.9%, and B. ovis was 7.4%. Serological detection with four abortion-causing microorganisms was determined only in 0.87% of sheep sampled. The best GLM is integrated via serological detection of serovar Hardjo and Brucella ovis in animals of the slopes with elevation between 2600 and 2800 meters above sea level from the municipality of Xalatlaco. Other covariates included in the GLM, such as the sheep pen built with materials of metal grids and untreated wood, dirt and concrete floors, bed of straw, and the well water supply were also remained independently associated with infectious abortion. Approximately 80% of those respondents did not wear gloves or masks to prevent the transmission of the abortifacient zoonotic microorganisms. Conclusions: Sensitizing stakeholders on good agricultural practices could improve public health surveillance. Further studies on the effect of animal–human transmission in such a setting is worthwhile to further support the One Health initiative. Full article

Water supply and demand management (WSDM) is essential for developing sustainable cities and societies. WSDM is only effective when tackled from the perspective of a holistic system understanding that considers social, environmental, hydrological, and economic (SEHEc) sub-systems. System dynamics modeling (SDM) is recommended by water resource researchers as it models the biophysical and socio-economic systems simultaneously. This study presents a comprehensive literature review of SDM applications in sustainable WSDM. The reviewed articles were methodologically analyzed considering SEHEc sub-systems and the type of modeling approach used. This study revealed that problem conceptualization using the causal loop diagram (CLD) was performed in only 58% of the studies. Moreover, 70% of the reviewed articles used the stock flow diagram (SFD) to perform a quantitative system analysis. Furthermore, stakeholder engagement plays a significant role in understanding the core issues and divergent views and needs of users, but it was incorporated by only 36% of the studies. Although climate change significantly affects water management strategies, only 51% of the reviewed articles considered it. Although the scenario analysis is supported by simulation models, they further require the optimization models to yield optimal key parameter values. One noticeable finding is that only 12% of the articles used quantitative models to complement SDM for the decision-making process. The models included agent-based modeling (ABM), Bayesian networking (BN), analytical hierarchy approach (AHP), and simulation optimization multi-objective optimization (MOO). The solution approaches included the genetic algorithm (GA), particle swarm optimization (PSO), and the non-dominated sorting genetic algorithm (NSGA-II). The key findings for the sustainable development of water resources included the per capita water reduction, water conservation through public awareness campaigns, the use of treated wastewater, the adoption of efficient irrigation practices including drip irrigation, the cultivation of low-water-consuming crops in water-stressed regions, and regulations to control the overexploitation of groundwater. In conclusion, it is established that SDM is an effective tool for devising strategies that enable sustainable water supply and demand management. Full article

Loss of production due to local water scarcity, i.e., Local Water Scarcity Risk (LWSR), is transferred downstream through international supply chains to distant economies, causing potential economic losses to countries and sectors that do not directly experience actual water scarcity, which is defined as Virtual Water Scarcity Risk (VWSR). Much research has focused on assessing VWSR and characterizing the structure of VWSR transfer networks, without explaining the formation and dynamics of VWSR transfer network patterns. In this study, the global VWSR transfer networks for 2001–2016 are then constructed based on a multi-regional input-output model and complex network theory. The determinants influencing the formation of VWSR transfer networks are further explored using the time-exponential random graph model. The results demonstrate that: (1) The VWSR transfer networks exhibit a distinctly small-world and heterogeneous nature; (2) Asia and Europe are the main targets of VWSR transfers, and Asia is also the main source of risks; (3) China and the USA play a leading role on the import side of VWSR, and India is the largest exporter of VWSR; (4) The evolution of VWSR transfer networks is significantly influenced by transitivity and stability. Countries located on the same continent, sharing geographical borders and having a higher level of economic development, have a facilitating effect on the formation and evolution of VWSR transfer networks. Countries with a higher share of merchandise trade are more inclined to receive VWSR inflows, while the urbanization rate has a restraining effect on VWSR outflows. The study provides a network-based insight that explores the structural evolution of VWSR transfer networks and the determinants of their formation, informing policy makers in developing strategies to mitigate the cascading spread of VWSR. Full article

The increasing demand for freshwater supplies and the effects of climate change in arid and hyper-arid regions are pushing governments to explore new water resources for food security assurance. Groundwater is one of the most valuable water resources in these regions, which are facing water scarcity due to climatic conditions and limited rainfall. In this manuscript, we provide an integrated approach of remote sensing, geographic information systems, and analytical hierarchical process (AHP) to identify the groundwater potential zone in the central Eastern Desert, Egypt. A knowledge-driven GIS-technique-based method for distinguishing groundwater potential zones used multi-criteria decision analysis and AHP. Ten factors influencing groundwater were considered in this study, including elevation, slope steepness, rainfall, drainage density, lineament density, the distance from major fractures, land use/land cover, lithology, soil type, and the distance from the channel network. Three classes of groundwater prospective zones were identified, namely good potential (3.5%), moderate potential (7.8%), and poor potential (88.6%) zones. Well data from the study area were used to cross-validate the results with 82.5% accuracy. During the last 8 years, the static water level of the Quaternary alluvium aquifer greatly decreased (14 m) due to excessive over pumping in the El-Dir area, with no recorded recharges reaching this site. Since 1997, there has been a noticeable decline in major rainfall storms as a result of climate change. The current study introduces a cost-effective multidisciplinary approach to exploring groundwater resources, especially in arid environments. Moreover, a significant modern recharge for shallow groundwater aquifers is taking place, even in hyper-arid conditions. Full article

Multi-regional waterworks are large-scale facilities for supplying tap water to the public and industrial parks, and interruptions in the water supply due to leaks result in massive social and economic damages. Accordingly, real-time, around-the-clock accident monitoring is necessary to minimize secondary damage. In the present study, a section of a large-scale waterworks transmission mains system with frequent changes in its physical boundaries was defined for sensor network map-based deep learning input and output. A deep neural network (DNN)-based pressure prediction model, able to detect pipe burst accidents in real-time using short-term data collected over periods within 1 month, was developed. A sensor network map refers to a sensor-based hierarchy diagram, which is expressed using a hydraulically divided area. A hydraulically independent area can be determined using known value information (e.g., the known flow, pressure, and total head) in a complex water supply system. The input data used for the deep learning model training were: the water levels measured at 1 min intervals, flow rates, ambient pressure, pump operation state, and electric valve opening data. To verify the developed methodology, two sets of real-world data from past burst accidents in different multi-regional waterworks systems were used. The results showed that the difference between the pressure as measured by pressure meters and an estimated pressure was extremely small before an accident, and that the difference would reach a maximum at the time point when an accident occurs. It was confirmed that an approximate estimation of an accident occurrence and accident location could be estimated based on predicted pressure meter data. The developed methodology predicts a mutual influence between pressure meters and, therefore, has the advantage of not requiring past data covering long time periods. The proposed methodology can be applied immediately and used in currently operational large-scale water transmission main systems. Full article

In a deep learning model, the effect of the model may vary depending on the setting of the hyperparameters. Despite the importance of such hyperparameter determination, most previous studies related to burst detection models of the water supply pipe network used hyperparameters applied in other fields as-is or made a trial-and-error setting based on experience, which is a limitation. In this paper, a study was conducted on the deep learning hyperparameter determination of a deep neural network (DNN)-based real-time detection model of pipe burst accidents. The pipe burst model predicted water pressure by using operation data in units of 1 min, and the data period applied for the model training was less than 1 month (1, 2, and 3 weeks) in order to consider frequent changes in the system. A sensitivity analysis was first performed on the type of activation function and the period of the learning data, which may have different effects depending on the characteristics of the target problem. The number of hidden layers related to the network structure and the number of neurons in each hidden layer were set as hyperparameters for additional sensitivity analysis. The sensitivity analysis results were derived and compared using four quantified prediction error indicators. In addition, the model running time was analyzed to evaluate the practical applicability of the development model. From the results, it was confirmed that excellent effects could be expected if using a rectifier function as the activation function, 144 nodes in the hidden layer, which is eight times the number of nodes in the input layer, and four hidden layers. Additionally, by analyzing the appropriate period of training data required for model pressure prediction through prediction error and driving time, it was confirmed that it was most appropriate to use the data of two weeks. By applying the hyperparameter values determined through detailed sensitivity analysis and by applying the data of one week including actual burst accidents to the built-up pressure prediction model, the accident detection and predictive performance of the model were verified. The rational determination of the period of input factors for the optimal hyperparameter setting and model building, as in this study, is very necessary and very important as it can serve to ensure the continuity of the operation effects of the deep learning model. Full article