Risk Of Overflow Research Articles

Assessment of Flood Mitigation Strategies: A Case Study of the Aït Athmane Center in the Errachidia Region

The development of impoverished and marginalized communities is being increasingly jeopardized by the risks associated with climate change. Extreme weather events, like floods, can impact infrastructure, ecosystems, agriculture, water, and other vital sectors. Our study focuses on the flood risk in Aït Athmane, located 28 kilometres north of Errachidia, Morocco. This area is particularly vulnerable to flooding and overflows from the Ziz Wadi tributary, which passes through the territory of Aït Athmane. The specific objectives of this study are to assess the effects of flooding in this area and to propose various adaptation strategies. The methodology involved processing GIS data for the basin's hydrological study, statistically analyzing the maximum daily rainfall at the “Foum Tillicht” rainfall station using HyfranPlus software, and estimating peak flow using various empirical formulas. We then used the 'HEC-RAS' hydraulic modeling program to perform a hydraulic simulation, an essential step in understanding these values for Aït Athmane Wadi, a tributary of Ziz Wadi. GIS software was used to create the maps, which were superimposed to identify locations vulnerable to flooding. The findings indicate that, for varying return periods, flows range from 60.13 m³/s to 92.69 m³/s, with a concentration-time of 125.55 minutes. The hydraulic study indicates an overflow risk, endangering nearby residents and their crops. In certain areas, these floods can reach three meters. Numerous strategies have been identified and proposed to mitigate the effects of this significant risk. These primarily involve simple mechanical adjustments such as constructing gabion weirs, raising retaining walls along the Wadi's banks, and creating crossing structures.

Assessment of Overflow Risk in Agricultural Reservoir Based on Water Balance Theory

The unexpected climate changes are causing rainfalls to exceed the design frequency, which complicates the management and operation of agricultural reservoirs and increases the risk of overflow. According to the “Planning and Design Standards for Agricultural Prediction Infrastructure Maintenance Project (Fill Dam Section),” agricultural reservoirs were originally designed without flood control functions, with the standard being to directly discharge incoming flood. Based on this foundation, this study assesses the overflow risk of agricultural reservoirs using the concepts of inflow and outflow theories as well as storage capacity. The balance when inflow and outflow are at their maximum was analyzed for prioritized areas, focusing on the flood management area (the area between the full water level and crest elevation) relative to the basin area, to examine the feasibility of balance through flood routing. The proposed method is easily accessible to facility managers, enabling them to assess the risk of reservoirs for efficient management and operation during floods.

A novel physics-guided spatial-temporal data mining method with external and internal causal attention for drilling risk evaluation

As drilling technology advances and operations extend into more complex geological environments, evaluating drilling risks has become increasingly complex, challenging the effectiveness of traditional methods. The novel physics-guided spatial-temporal data mining method that integrates external and internal causal attention mechanisms for drilling risk evaluation is proposed to address this issue. Firstly, a risk calibration method based on spatial-temporal sequence clustering is designed. This method dynamically calibrates drilling risks by mining subtle changes in sign data during drilling. Secondly, an expert experience extraction method based on the correlation of drilling risk signs and a fuzzy inference system is established. Kendall's tau is used to quantify the correlation between drilling risk signs. The fuzzy inference system is employed to convert fuzzy and difficult-to-quantify expert experience into computable and interpretable rules. In order to improve the flexibility and adaptability of the fuzzy inference system, an expert experience rules base is also constructed. Subsequently, a spatial-temporal data mining model integrating both external and internal causal attention mechanisms (STMIEICAM) is constructed. The external causal attention mechanism (ECAM) quantified the correlation between signs and risk. The internal causal attention mechanism (ICAM) improved the model's ability to capture and quantify the features of spatial-temporal sequences. Finally, the physical knowledge from the fuzzy inference system and well-site is embedded into the STMIEICAM model, forming a physics-guided spatial-temporal data mining model integrating both external and internal causal attention mechanisms (PG-STMIEICAM) that enables graded evaluation of drilling risks. The proposed method was applied to overflow risk evaluation in an oil field to validate its effectiveness. The results demonstrate that the method not only excels in uncovering hidden relationships within the data but also integrates expert knowledge, achieving accurate evaluation of drilling risks.

Study of the Risks of Overflow and Salinization of the Mbao Backwater: Impact on Local Populations

The Mbao backwater, located in the Mbao forest in Dakar, plays an important role in income generation and flood prevention for the localities of Pikine, Keur Massar and Rufisque. Today, it is increasingly threatened with the risk of overflow due to the considerable reduction in its storage capacity and the arrival of large quantities of runoff water from the watersheds of which it is the outlet. In addition, its salinization is noted due to its proximity to the sea. The objective of this article is to present the results of the study of the risks of overflow and salinization of the Mbao backwater and their impacts on socio-economic activities. After modelling the volumes of water received by the backwater using the EPASWMM software and a study of its vulnerability to saline intrusion during high tides, It emerged that in the event of a decadal rainfall of the Desbordes type in the area, an overflow of 1,001,758 m3 could be noted, causing flooding and inconvenience to the local populations. In addition, a connection between the waters of the sea and those of the backwater causes salinization of the latter, negatively impacting the market gardening activities carried out by local residents. A pumping system at 1,500 m3/h could help prevent the backwater from overflowing, and the construction of a weir dam between the sea and the backwater would help control the salinization of the downstream part. Keywords: risk, overflow, salinization, vulnerability, Mbao backwater

Optimized Probabilistic Sink Node Deployment for Enhanced Performance in Wireless Sensor Networks

This paper presents an innovative mechanism to deploy sink nodes in Wireless Sensor Networks (WSN), which play an essential role in the gathering sensed data from sensor nodes across diverse applications. Our mechanism is different from existing mechanisms, which deploy sink nodes based on the residual status of buffer and energy of the sensor nodes. Instead, proposed mechanism’s sink node deployment is decided based on the optimal probabilistic metrics related to buffer and energy status during data processing; aim to extend the network performance with respect to risk of buffer overflow and energy consumption. We analyse and validate the performance of the proposed work through simulation and compared the results with existing residual-based sink node deployment mechanisms. The results indicate the signification extension in the performance in terms of energy, packet loss and lifetime by mitigating bottlenecks at nodes. The work contributes to the improvement of WSN design and deployment mechanisms by significantly enhancing the performance and reliability of the network.

농업용 저수지 안전점검 및 등급 체계 개선방안 연구

Accidents in agricultural reservoirs continue to occur due to heavy rain caused by abnormal weather conditions. To prevent reservoir accidents, it is necessary to identify factors affecting collapse and manage them safely through prior inspection and repair. For reservoirs with a total storage capacity of less than 50,000㎥, which accounts for 81% of reservoirs nationwide, regular inspections conducted at least once a quarter are the only safety inspection procedures, and the conduct of regular inspections can be seen as a measure of safety inspections. As a result of the survey, it was determined that the implementation status of regular inspection was low at 46%, and problems were analyzed and improvement measures were proposed. Additionally, if the factor affecting collapse is a member with a low score, it is difficult to reflect the results of regular inspection in the safety grade. In this study, we analyzed the problems with the regular inspection grading system and proposed an improvement plan by borrowing and applying the condition evaluation method of precise safety diagnosis, assessing overflow risk, and considering downstream impacts.

Substance flow analysis of non-intentional lead use in China from 1980 to 2050

As a heavy metal element abundant in the earth's crust, lead is released into the environment through the use of fossil fuels and minerals, resulting in environmental pollution. This study conducted a substance flow analysis to explore the evolution of China's non-intentional use of lead from 1980 to 2050. Results showed that the main sources of lead inflow from the fossil fuel, metal, and phosphorus systems were coal mining, imported ores, and phosphorus fertilizers, respectively. In 2019, intentional and non-intentional lead use introduced lead flows of 3034 and 616 kt/yr, resulting in 1.1 and 8 kt/yr of lead emissions, respectively. A scenario analysis indicated that lead emissions will be 2, 1.3, and 0.9 kt/yr under the high-resource, baseline, and low-resource consumption scenarios in 2050, respectively. It is necessary to reduce the lead inflow from resource consumption and the risk of lead overflow during the accumulation or recycling of lead-containing waste.

FusionFlow: Accelerating Data Preprocessing for Machine Learning with CPU-GPU Cooperation

Data augmentation enhances the accuracy of DL models by diversifying training samples through a sequence of data transformations. While recent advancements in data augmentation have demonstrated remarkable efficacy, they often rely on computationally expensive and dynamic algorithms. Unfortunately, current system optimizations, primarily designed to leverage CPUs, cannot effectively support these methods due to costs and limited resource availability. To address these issues, we introduce FusionFlow, a system that cooperatively utilizes both CPUs and GPUs to accelerate the data preprocessing stage of DL training that runs the data augmentation algorithm. FusionFlow orchestrates data preprocessing tasks across CPUs and GPUs while minimizing interference with GPU-based model training. In doing so, it effectively mitigates the risk of GPU memory overflow by managing memory allocations of the tasks within the GPU-wide free space. Furthermore, FusionFlow provides a dynamic scheduling strategy for tasks with varying computational demands and reallocates compute resources on the fly to enhance training throughput for both single and multi-GPU DL jobs. Our evaluations show that FusionFlow outperforms existing CPU-based methods by 16--285% in single-machine scenarios and, to achieve similar training speeds, requires 50--60% fewer CPUs compared to utilizing scalable compute resources from external servers.

A multi-day waste collection and transportation problem with selective collection and split delivery

This paper addresses a multi-day waste collection and transportation problem with selective collection and split delivery (MDWCTP-SCSD). Rather than emptying waste according to a fixed schedule, garbage trucks only visit community waste collection sites whose waste level reaches a predetermined threshold, reducing collection costs but increasing overflow risk. Collection sites likely to overflow the next day if unvisited today are also selected for preventive collection. Split delivery is permitted. The MDWCTP-SCSD is formulated as a mixed integer linear programming model. To solve this NP-hard problem, a heuristic combining variable neighborhood search and simulated annealing is proposed. In addition to classical operators, three new operators are developed to handle split delivery. Experiments demonstrate the proposed algorithm is competitive in solving two split delivery vehicle routing benchmark sets; outperforms CPLEX in solving small-scale instances within an hour; and quickly solves large-scale instances with high quality. Noteworthy findings indicate fleet costs and size can be reduced by approximately 50% if only visiting sites whose waste level reaches 80%.

Modelling overflow using mixed integer programming in short-term hydropower scheduling

AbstractShort-term hydropower scheduling seeks to find a production schedule that maximizes profit, but must also consider the hydrological balance and risk of overflow. Overflow is by nature a non-linear and non-convex phenomenon. Common approximations and relaxations may cause non-physical results such as overflow from reservoirs that are not full. This paper presents a mixed-integer linear programming formulation that can be used to prevent non-physical overflow behaviour, but that comes at a cost of significant higher solving time. To achieve an acceptable solving time, we propose a heuristic to provide tight upper bounds on the overflow variables in each time step. When applied on the model of the Fossdal watercourse in Norway, the proposed method reduces the solving time with more than 90% compared to using a conservative fixed coefficient for all time steps.

Smart IoT-based water treatment with a Supervisory Control and Data Acquisition (SCADA) system process

Abstract Water treatment is necessary to ensure the availability of clean and safe water for various uses. Integrating Internet of Things (IoT) technology with water purification systems has shown enormous potential in recent years for enhancing the efficiency and efficacy of the treatment process. Monitoring the disposal of sewage in treatment facilities is the primary obstacle. As a result, a Supervisory Control And Data Acquisition (SCADA) system, including the IoT, has been proposed to ensure the proper operation of these sewer systems and limit the risk of overflow and malfunction. In this paper, we suggest a novel approach that blends Deep Belief Networks (DBNs) with an IoT-based water treatment system equipped with a SCADA system for increased monitoring and control. An IoT–SCADA system can be implemented at various wastewater collection and treatment phases. Secondly, incorporating DBNs enhances the system's predictive capabilities, enabling proactive maintenance and decision-making to prevent potential failures and optimize resource allocation. The proposed technique computes the efficacy of the effluent treatment facility and ensures that chemical emissions do not exceed permissible limits. Furthermore, Complex Event Processing (CEP) can be utilized to evaluate and analyze the massive influx of real-time data sets provided by IoT sensors.

Iber 모형을 이용한 편수위 발생에 따른 범람위험지역에 대한 분석

Climate change is leading to an increase in the frequency of natural disasters, including storms and floods. Rivers with high sinuosity are experiencing severe floods due to the heightened river discharge caused by climate change. In this study, we focused on the downstream section of the Samcheok Osipcheon basin (from 20.8km to 26km), where the lack of embankment height poses an overflow risk. We used the two-dimensional Iber model to analyze the boundary conditions of the basic river plans for comparison. Our findings reveal an average difference of 0.45m between the river comprehensive plan and the actual water level. At the high bend, the maximum water level difference was 1.25m, with a maximum superelevation difference of 1.49m. To better understand and utilize the hydraulic characteristics of the river bends experiencing superelevation, we propose the application of both one-dimensional and two-dimensional Iber models for analysis and design purposes.

Rapid quantification of the surface overflow and underground infiltration in sewer pipes based on computer vision and continuous optimization

The overloading of the sewer network caused by unwarranted infiltration of stormwater may lead to waterlogging and environmental pollution. The accurate identification of infiltration and surface overflow is essential to predict and reduce these risks. To retrieve the limitations of infiltration estimation and the failure of surface overflow perception using the common stormwater management model (SWMM), a surface overflow and underground infiltration (SOUI) model is proposed to estimate the infiltration and overflow. First, the precipitation, water level of the manhole, surface water depth and images of the overflowing point, and volume at the outfall are collected. Then, the surface waterlogging area is identified based on computer vision to reconstruct the local digital elevation model (DEM) by spatial interpolation, and the relationship between the waterlogging depth, area and volume is established to identify the real-time overflow. Next, a continuous genetic algorithm optimization (CT-GA) model is proposed for the underground sewer system to determine the inflow rapidly. Finally, surface and underground flow estimations are combined to perceive the state of the urban sewer network accurately. The results show that, compared with the common SWMM simulation, the accuracy of the water level simulation is improved by 43.5% during the rainfall period, and the time cost of the computational optimization is reduced by 67.5%. The proposed method can effectively diagnose the operation state and overflow risk of the sewer networks in real time during rainfall seasons.

Revisiting the central aortopulmonary shunt procedure.

In this study, we present our experience with the central aortopulmonary shunt technique with interposing a polytetrafluoroethylene graft between main pulmonary artery (end-to-end) and the ascending aorta (side-to-side) in a variety of cyanotic congenital heart defects. Between January 2019 and June 2022, a total of 10 patients (6 males, 4 females; mean age: 4.3±2.8 months; range, 5 days to 10 months) with hypoplastic central pulmonary arteries who underwent central aortopulmonary shunt procedure were retrospectively analyzed. Demographic characteristics, preoperative, operative, and postoperative data of the patients were recorded. The Nakata indices of the patients were also noted before the procedure, as well as before the second stage of palliation or definitive repair. Four (40%) patients were operated as the first-step palliation for univentricular circulation. Six (60%) patients had well-developed ventricles and were palliated to be treated with total correction. The median follow-up after the procedure was 12 (range, 8 to 16) months. The mean systemic arterial saturation level at room air was 89.3±2.9% during follow-up. No mortality was observed in any patient. A central aortopulmonary shunt procedure provides a reliable antegrade blood flow with a relatively non-challenging surgical technique that offers sufficient growth for the hypoplastic and confluent central pulmonary arteries with a very low risk of shunt thrombosis and overflow.

Field Monitoring and Identification Method for Overflow of Fractured-Vuggy Carbonate Reservoir

Since carbonate reservoirs develop pores and fractures and have a complex formation pressure system, overflow and even blowout seriously threaten the exploration and development of these kinds of reservoirs. According to the overflow characteristics of fractured-vuggy carbonate reservoirs, a field monitoring and identification method for overflow has been developed. This method is based on the top-down logic framework for early overflow identification, combined with optimized monitoring parameters. The DBSCAN clustering algorithm is used to identify abnormal logging parameters, and thus, a probability weight coefficient of overflow (K) indicated by the abnormal engineering parameters of a gas well can be calculated. K is divided into four early warning response levels of overflow, and the overflow control operation can be made according to the different levels of early warning response. Based on this method, an integrated software system for field monitoring and identification of early overflow is designed. This software system integrates the functions of data collection and management, overflow risk parameter calculation, overflow early warning, and overflow simulation analysis. Finally, according to a case analysis of overflow prediction for a fractured-vuggy carbonate reservoir in Well XX, this method’s prediction stability and accuracy are demonstrated, indicating that it can be used for the overflow early warning in engineering practice.

Spatiotemporal variations and overflow risk analysis of the Salt Lake in the Hoh Xil Region using machine learning methods

Global warming is inducing dramatic changes in fluvial geomorphology and reshaping the hydrological connections between rivers and lakes. The water level and area of the Salt Lake have increased rapidly since the outburst of the Zonag Lake in the Hoh Xil region of the Qinghai–Tibet Plateau in 2011, threatening the downstream infrastructure. However, fewer studies have focused on its spatiotemporal variation and overflow risk over long time series. Here, we used three machine learning algorithms: Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) to extract the area of the Salt Lake for a long time series, analyzed its spatiotemporal variation from 1973 to 2021, and finally assessed the overflow risk. The Kappa coefficient (KAPPA) and the overall accuracy (OA) were used to evaluate the performance of the models. The results showed that Random Forest performs superior in lake extraction (KAPPA = 0.98, overall accuracy = 0.99), followed by Classification and Regression Trees and Support Vector Machine. normalized difference water index is the relatively important feature variable in both RF and CART. Before the outburst event, the area change of the Salt Lake was consistent with the variation in precipitation; after that, it showed a remarkable area increase (circa 350%) in all orientations, and the main direction was the southeast. Without the construction of the emergency drainage channel, the simulation result indicated that the earliest and latest times of the Salt Lake overflow event are predicted to occur in 2020 and 2031, respectively. The results of this paper not only demonstrate that RF is more suitable for water extraction and help understand the water system reorganization event.

Impact of Digital Platform Organization on Reducing Green Production Risk to Tackle COVID-19: Evidence from Farmers in Jiangsu China

The agricultural organization based on digital platforms in C2F (Company–Platform–Farmers) may be an effective way to reduce the increased risk of green production caused by the COVID-19 pandemic, but the specific mechanism and impact involved are unclear. Applying risk cognition and decision theory, we built the theoretical framework on platform organization, pandemic risk, perception value, and green production continuity, and ascertained the impact effects and path using the PLS-SEM method. We found that the risk of COVID-19 overflow had a significant impact on farmers’ green production continuity by mediating perception value, especially regarding reduced green technology adoption intention and increased cost of living. Utilizing perfect platform value cognition, participation co-operation, access and benefit distribution, and safeguard and restraint measures of platform organization in C2F, we offered a new approach to reduce the green production risks caused by COVID-19, such as material and labor shortages, financial pressure, sales channel blockages, and price volatility. We explained the behavior-moderating motivation of farmers with different risk preferences and subjective norms in relation to tackling COVID-19. We should aim to perfect the functions of digital platform organizations to optimize the benefit linkages in C2F, accelerate farmers’ digitization ability cultivation to increase the cognitive risk level, and strengthen the policy guidance of COVID-19 prevention to reduce the influence of farmers’ subjective norms.

Adaptive Signal Control for Overflow Prevention at Isolated Intersections Based on Fuzzy Control

Nonrecurrent congestion on urban roads may cause overflow, possibly leading to gridlock at intersections. Signal control is one of the most important measures to prevent such phenomena. For nonrecurrent congestion with uncertain bottleneck capacity, however, existing signal control methods have mainly adopted the strategy of cutting off the green signal of the overflow movement while the release priority of green time has not been considered. Consequently, the green time allocated to overflow movements is too long, aggravating overflow and congestion. Therefore, a signal control method is proposed to prevent overflow caused by uncertain bottleneck capacity. The proposed method extends existing real-time adaptive signal control to explicitly consider the risk of queue spillover. When an overflow risk is detected, the green times of overflow movements will be reassigned using fuzzy control to determine the release priority depending on the queue length and red time. Numerical studies were conducted for two scenarios with different downstream bottleneck capacities. The results demonstrate that the proposed method not only outperforms existing methods in respect of intersection efficiency but also reduces the risk of overflow. The proposed method achieves a 27.7% to 33.8% reduction in the maximum queue length of overflow movements compared with a traditional adaptive signal control method and a 10.9% to 11.0% decrease compared with a modified signal control method with a cutting-off strategy. The sensitivity analysis indicates that the average delay resulting from the proposed method is 51.1% and 18.0% less than the delays resulting from the traditional and modified methods, respectively.

Kajian Kehandalan Saluran Drainase dengan Pendekatan Metode First-Order Second Momentdan Safety Factor

The growth and development of big cities in Indonesia is quite fast. Especially the city of Bandung which is the center of tourism in the West Java region. Ir. H. Juanda street is one of the access to tourism places in Bandung City and is an icon of Bandung City. In addition, Ir.H Juandastreet has problems, namely high levels of congestion and flooding in the rainy season. The last ten years the Jalan Ir.H. Djuanda always floods when there is heavy rain. This causes puddles of water to enter the Jalan Ir. H. Juanda section, namely from Pasar Simpang to Dago Terminal. This has an impact on road damage, drainage, and congestion. To determine the risk of overflow of the channel in the study area as a result of various amounts of flood discharge, carried outthree analyzes stages, namely hydrological analysis to determine the amount of return period discharge, hydraulic analysis to identify overflow points and improvement scenarios and reliability analysis using the safety factor and first-order-second moment methods. The results of the analysis show that there are 2 points that have the opportunity for overflow, namely at the location of the Al-Ihsan Mosque and the Honda Workshop. The solution given to overcome this is to normalize the drainage with a size of 1.5 m x 1.5 m from upstream to downstream.The results showed that the reliability of the channel increased to 92.96% (Two Years Return Period Discharge), 88.54% (Five Years Return Period Discharge) and 85.81% (Ten Years Return Period Discharge) for the Al-Ihsan Mosque channel and 100% (Two Years Return Period Discharge), 99.98% (Five Years Return Period Discharge) and 99.92% (Ten Years Return Period Discharge) for the Honda Workshop channel. The method in the risk analysis above can relate the probability of failure or exceedance to the expected success of the function of a drainage cross-sectional structure in accommodating flood flows.

Gas Overrun Risk Assessment in Coal Mining Face Based on Fuzzy Bayesian Network

A risk assessment technology based on a fuzzy Bayesian network is proposed to improve the degree of gas control management in the coal mining face and avoid gas overrun. Five characteristics of the coal mining working face, i.e., geological structure, ventilation conditions, gas extraction, mining activities, and coal mine management affecting the coal mining working face, were examined for 17 risk variables and a gas overrun assessment model was created. A priori information and sample data indicate that there is a 3% chance of gas overflow. The reverse reasoning test found that the main reasons for gas overrun were unreported gas anomalies, ventilation modes, gas content in coal seams, goaf extraction volume, and coal mining rate in coal mining face. The research results show that the approach can assess the risk of gas overflow in coal mining face.