Simulated Water Levels Research Articles

1D-2D hydrodynamic and sediment transport modelling using MIKE models

A comprehensive modeling framework utilizing hydrodynamic and sediment transport models (MIKE Hydro River, MIKE 21 FM, MIKE 21C) was applied to the 33.57 km stretch of the Banshadhara River from Banshadhara bridge, Gunupur to Kashinagar town. Daily discharge, water levels, and sediment data from 2013 to 2022 of Gunupur and Kashinagar gauge stations was used as model input, and calibration–validation of models. Additionally, satellite remote sensing data used for determined Manning’s roughness (n) values as well compared the model outputs. Calibration procedures ensured high accuracy, with MIKE Hydro River achieving 98.3% agreement between observed and simulated water levels. 1D sediment transport model showing significant variability in total bed load values ranging from 0.3 to 3.83 m^3/s. The Wilcock & Crowe gravel bed load formula accurately predicted bed load transport within the expected magnitude, albeit tending to overestimate transport, indicative of supply-limited conditions. 2D MIKE 21 FM simulations highlighted flow dynamics, with rapid flood arrival at Kashinagar and varying current speeds along the river, especially at bends. MIKE 21C, focused on the central portion, indicated erosion processes at river bends with high discharge. Sediment transport model accuracy was approximately 86.7%, capturing general trends despite occasional discrepancies. Over a 10-year simulation, erosion trends and potential river course shifting were observed, particularly near bends. The analysis provides valuable insights for river management, flood risk mitigation, water resource management, infrastructure planning, and environmental preservation in the Banshadhara River basin and similar environments.

Early warning system for floods at estuarine areas: combining artificial intelligence with process-based models

AbstractFloods are among the most common natural disasters, causing countless losses every year worldwide and demanding urgent measures to mitigate their impacts. This study proposes a novel combination of artificial intelligence and process-based models to construct a flood early warning system (FEWS) for estuarine regions. Using streamflow and rainfall data, a deep learning model with long short-term memory layers was used to forecast the river discharge at the fluvial boundary of an estuary. Afterwards, a hydrodynamic process-based model was used to simulate water levels in the estuary. The river discharge predictors were trained using different forecasting windows varying from 3 h to 36 h to assess the relationship between the time window and accuracy. The insertion of attention layers into the network architecture was evaluated to enhance forecasting capacity. The FEWS was implemented in the Douro River Estuary, a densely urbanised flood-prone area in northern Portugal. The results demonstrated that the Douro Estuary FEWS is reliable for discharges up to 5000 m3/s, with predictions made 36 h in advance. For values higher than this, the uncertainties in the model predictions increased; however, they were still capable of detecting flood occurrences.

Modeling the Impact of Wind Drag Coefficient on Wind-Driven Currents in Lake Taihu, China

The wind drag coefficient, Cd, has a great influence on the numerical results obtained from shallow lakes. To analyze the modeling impacts of Cd on wind-driven currents, a series of numerical simulations of Lake Taihu were conducted at three grid resolutions (800 m × 800 m, 400 m × 400 m, and 100 m × 100 m) using the empirical formulae of Flather (F76), Large and Pond (LP81), Large and Yeager (LY04), Andreas (A12), and Gao (G20). The G20 formula produced the optimum results of all the formulae for both the water level and velocity simulations; however, the grid resolution was found to have a significant influence on simulation in G20 cases. Thus, the G20 formula is only recommended when using a high-resolution grid to meet the accuracy requirements of analyzing wind-driven currents in the numerical modeling of Lake Taihu. A combination of the A12 formula and a coarse grid is preferred when taking computational efficiency into consideration.

Total water level prediction at continental scale: Coastal ocean

We demonstrate recent progress made in the simulation of total water level (TWL) at continental scale, using the coastal ocean of US East Coast/Gulf of Mexico coast as an example. A key difference between the continental-scale and small-scale modeling is that the former requires a more accurate vertical datum. Using a geoid-based datum (xGEOID20b), a satellite altimetry product, and a state-of-the-art 3D unstructured-grid model, we significantly improve the accuracy for TWL both near- and off-shore. The average root-mean-square error at all NOAA stations is 14 cm. The non-tidal signals are found to be sensitive to the representation of a large-scale current system near the boundary and extending the domain extent to accommodate this system improves these signals.

Hydrodynamic modeling of dam breach floods for predicting downstream inundation scenarios using integrated approach of satellite data, unmanned aerial vehicles (UAVs), and Google Earth Engine (GEE)

Dam breach floods pose significant threats to downstream areas, necessitating accurate prediction of inundation scenarios to mitigate potential damage. This paper presents a novel methodology for hydrodynamic modeling of dam breach floods, leveraging a comprehensive approach that integrates satellite imagery, unmanned aerial vehicles (UAVs), and Google Earth Engine (GEE) to forecast downstream inundation scenarios. Specifically, UAVs were utilized to generate high-resolution Digital Elevation Models (DEMs) of the flood-affected areas, ensuring precise representation of topography in the model. The approach incorporates Cartosat DEM data for catchment modeling, while NASA's Global Precipitation Measurement mission data, integrated with GEE, facilitated accurate estimation of rainfall in ungagged catchment areas. Furthermore, the Hydrological Engineering Center-Hydrological Modeling System was employed for rainfall-runoff simulation and flood hydrograph derivation, followed by application of the HEC River Analysis System (RAS) for hydrodynamic modeling under dam breach conditions. This integrated modeling approach was applied as a case study of Banaskantha district, Gujarat, India. The outcome was the generation of scenario maps based on HEC-RAS results, which include flood extent, water depth, and flow velocity, highlighting downstream areas affected by flooding. Validation of the hydrodynamic dam breach model performance was conducted using actual field measurements and simulated results, employing statistical analysis methods including Support Vector Regression (SVR) and linear regression to determine coefficient of determination (R2), Root-Mean-Square Error, and Mean Absolute Error of observed and simulated data. The coefficient of determination (R2) values for measured and simulated flow (0.91) and water level (0.86) calculated using SVR demonstrate strong correlation between observed and simulated values. This integrated study of hydrodynamic modeling in data-scarce areas aids in accurate estimation of future probable flooding in downstream areas in the event of a dam break, underscoring the potential of advanced surveying and modeling techniques in flood assessment and management. Ultimately, this integration of technologies aims to enhance community resilience and mitigate socioeconomic costs associated with dam breach floods.

Identifying manning roughness coefficient using automatic calibration method and simulation of pollution incidents in the Nile River, Egypt

Study regionA reach of the Nile River located between Naga Hammadi barrage and Asyut barrage, Egypt Study focusAn accurate representation of hydrodynamics of an important water source helps cope with expected future climate changes, pollution incidents and water quality problems. Here, a comparison between HEC-RAS 1D and TELEMAC-2D model was conducted by identifying different Manning coefficients. Moreover, an automatic calibration using Dual-Annealing optimization method was applied for first time to calibrate the model with non-uniform Manning coefficients. The transport of tracer (pollution) was simulated by computing tracer residence times. Pollution transport scenarios were discussed to draw a picture of pollution incidents which will continue to happen in the future. An equation indicating the relation between flow discharge and residence time was derived to hurriedly help decision makers in water management during sudden pollution incidents. New hydrological insights for the regionA model with spatially variable Manning coefficients using TELEMAC-2D was set up and calibrated achieving good accuracyies with average errors of approximately 4 cm and 7 cm between field and simulated water levels for two different discharge scenarios. Moreover, an equation for relation between flow discharge and residence time was derived producing a strong correlation coefficient of 0.95. This study, integrating advanced hydrodynamic models and automatic calibration techniques, provides a robust framework for assessing and managing water resource challenges under varying flow conditions.

Increasing the water level accuracy in hydraulic river simulation by adapting mesh level elevation

2D hydraulic models are one of the tools to simulate water levels for effective river management. Mesh resolution in 2D models directly impacts the discretization of the bathymetry, the discharge capacity, and consequently, the accuracy of simulated water levels. The objective of this study is to develop a modified mesh setup that corresponds with the cross-sectional flow volume of the measured cross-section but with a low resolution for the entire discharge range. An algorithm is developed to vertically adjust mesh nodes within a limited range to achieve this objective. Subsequently, the D-Flow-FM software is utilized to model four hypothetical 100-kilometer river reaches to evaluate the modified mesh setup. The findings reveal that the water level using the modified low-resolution mesh is up to 90% closer to the high resolution mesh compared to the original low-resolution mesh for all discharges. Additionally, the simulation of the low-resolution mesh runs approximately 12.5 times faster than their high-resolution counterparts.

Numerical simulation of groundwater in hyporheic zone with coupled parameter stochastic scheme

Groundwater numerical modeling is a crucial scientific tool for understanding groundwater circulation and supporting regional water resource planning and management. The effectiveness of these models depends largely on the accuracy of hydrogeological parameters within aquifers, which are often spatially heterogeneous and randomly distributed due to complex geological and tectonic factors. Traditional modeling approaches frequently overlook this randomness, compromising the precision and resolution of groundwater simulations. This study focuses on a section of the Qingshui River in the Huaihe River Basin. Using field and laboratory data, probability distribution functions for key parameters like hydraulic conductivity, specific yield, and specific storage were developed. These functions were integrated into the groundwater model to reflect the inherent stochastic nature of aquifer properties. This integration significantly enhanced model accuracy, reducing the root mean square error of simulated water levels from 0.47–1.43 m to 0.13–0.16 m and improving the Nash-Sutcliffe efficiency coefficients (NSE) from −2.96–0.73 to 0.94–0.98. Additionally, the model facilitated analysis of the interactions between river and groundwater, particularly in the hyporheic zone, under various scenarios. It identified spatial and temporal variations in groundwater recharge dynamics and delay effects at different distances from the river channel. For instance, recharge rates at 50 m and 150 m from the river were 0.295 m/day and 0.015 m/day, respectively, indicating stronger recharge closer to the river. The study also assessed the impact of varying river flows, riverbed permeability, and irrigation practices on water exchanges between the river and groundwater. These factors were found to significantly influence the intensity of water exchange, seepage, and groundwater reserves. This research provides valuable insights for managing river-groundwater interactions and analyzing the ecological environment of surrounding groundwater systems, underscoring the importance of incorporating stochastic characteristics into groundwater modeling.

Inversion Study on Landslide Seepage Field Based on Swarm Intelligence Optimization Least-Square Support Vector Machine Algorithm

The permeability coefficient of landslide mass, a key parameter in the study of reservoir landslides, is commonly obtained through in situ and laboratory tests; however, the tests are costly and subject to high variability, leading to potential biases. In this paper, a new method was proposed to inversely estimate the permeability coefficient of landslide layers using monitoring data of groundwater level (GWL). First, the landslide transient seepage simulation was conducted to generate sample data for permeability coefficients and GWL during a reservoir operation cycle. Second, using GWL data as input and permeability coefficient data as output, the least-square support vector machine (LSSVM) was trained with two optimization algorithms, the particle swarm optimization (PSO) algorithm and the whale optimization algorithm (WOA), to construct the nonlinear mapping relationship between simulated GWL and permeability coefficients. Third, the accurate permeability coefficients for landslide seepage simulation were inverted or predicted based on the monitored GWL. Finally, using the inverted permeability coefficients for landslide seepage simulation, we compared simulation results with actual monitored GWL and achieved good consistency. In addition, this paper compared the inversion effects of three different algorithms: the standard LSSVM, PSO-LSSVM, and WOA-LSSVM. This study showed that these three algorithms had good nonlinear fitting effects in studying landslide seepage fields. Among them, using the inversion values from PSO-LSSVM for landslide seepage simulation resulted in the smallest relative error compared to actual monitoring data. Within a single reservoir operation cycle, the simulated water level changes were also largely consistent with the monitored water level changes. The results could provide a reference to determine landslide permeability coefficients and seepage.

Centrifuge modelling of wetting-induced settlement using different scaling methods

Groundwater variation is a significant cause for high-fill foundation settlement. In this study, centrifugal model tests were carried out to study the settlement of a high-fill foundation using a wetting–drying system for simulating water level variations. Three scaling methods including equivalent substitution method, trapezoid similarity method and parallel gradation method, were utilised to prepare the particle-size distribution of tested specimens. The test process consists of four loading cycles and each loading cycle consists of construction, water rise and drainage stages. The analysis results showed that settlement after construction only accounted for less than 20% of the total settlement. Besides, the settlement of the backfill increases with the filling height. The ratio between the settlement and the filling height of the specimen prepared by the equivalent substitution method is higher than the field monitoring data, while those prepared by the trapezoid similarity method or parallel gradation method agree well with the measurements. The settlement after construction with time can be predicted by the three-point method. Furthermore, significant settlement can be observed during the first wetting-drying cycle. The settlement during the water level rise was remarkably greater than that during the draw-down of the water level in each wetting–drying cycle.

Quantifying Compound and Nonlinear Effects of Hurricane‐Induced Flooding Using a Dynamically Coupled Hydrological‐Ocean Model

AbstractWe recently developed a dynamically coupled hydrological‐ocean modeling system that provides seamless coverage across the land‐ocean continuum during hurricane‐induced compound flooding. This study introduced a local inertial equation and a diagonal flow algorithm to the overland routing of the coupled system’s hydrology model (WRF‐Hydro). Using Hurricane Florence (2018) as a test case, the performance of the coupled model was significantly improved, evidenced by its enhanced capability of capturing backwater and increased water level simulation accuracy and stability. With four model experiments, we present a framework to detangle, define, and quantify compound and nonlinear effects. The results revealed that the flood peaks in the lower Cape Fear River Basin and the coastal waters were contributed by inland flooding and storm surge, respectively. These two processes had comparable contributions to the flooding in the Cape Fear River Estuary. The compound effect was identified when the flood levels resulting from the combination of land and ocean processes surpassed those caused by an individual process alone. The compound effect during Hurricane Florence exhibited limited impact on flood peaks, primarily due to the time lag between the peaks of the storm surge and the inland flooding. In the period between the two peaks, the compound effect was salient and significantly impacted the magnitude and variation of the flood level. The nonlinear effect, defined as the difference between the compound flood level and the superposition of storm surge and inland flooding water levels, reduced flood levels in the river channels while increasing flood levels on the floodplain.

Modeling Al-Qaraqoul canal before and after rehabilitation using HEC-RAS

The Egyptian Ministry of Water Resources and Irrigation launched in 2020 the national project to rehabilitate the canals network to rationalize the use of water resources to face the scarcity problems. The aim of study is to evaluate the impact of canal rehabilitation on the performance of irrigation water delivered laterally to Mesqa’s and longitudinally to the end of canal. Qaraqoul Canal et al.-Mallah Area, Alexandria, Egypt, was modeled using Hydrologic Engineering Center's-River Analysis System (HEC-RAS) to simulate water levels in the canal before and after rehabilitation using four discharge scenarios: 1.82, 3.7, 2.2, 7.87 m3/s. The calibration before rehabilitation shows that HEC-RAS simulated water levels corresponding to a discharge of 2.2 m3/s were in a good agreement with the actual field measured water levels. HEC-RAS results demonstrated that rehabilitation hydraulically improved the efficiency and performance of water conveyed by the canal. On the other hand, second scenario can be considered as suitable to keep water to reach the canal downstream with minimum suitable discharge, providing the need of two emergency pumps at last two branch canals called Mesqa’s. An ideal cross-section is also simulated using HEC-RAS which produced an efficient alternative with 40% less cost than the constructed alternative.

Comparative evaluation of meteorological inputs for improved storm surge modeling: A case study of tropical Cyclone Vayu

The selection of meteorological inputs in storm surge is crucial, with wind and pressure fields playing a significant role in energy transfer and the study area's bathymetry. While using observed track information for wind and pressure drop has been the standard approach for the past decade, recent studies have highlighted the need for atmospheric inputs from models like the Weather Research and Forecast model (WRF) for storm surge forecast. This study aims to compare the performance of a DELFT-3D FM storm surge model forced with inputs from IMD (India Meteorological Department) observed the best track and pressure drop (EXP-1) and wind and pressure fields from WRF (EXP-2) for Tropical Cyclone Vayu, which occurred in the southern Arabian Sea basin in June 2019. The study compares the simulated storm water levels and highlights the benefits of using time and space-varying wind and pressure input for improved surge representation. Results show that the WRF-DELFT setup outperforms the IMD-DELFT setup, particularly for tide gauge locations close to the storm eye. The simulated storm surge captures the intensified stage of Vayu and provides a more realistic representation than the model forced with IMD track data. However, biases and limitations, such as inadequate representation of land surface parameterization, are identified. The study suggests further exploring wave-induced effects on total water level and multiple cyclone scenarios to enhance wind speed and track displacement prediction accuracy and improved land-sea parameterization can help address these limitations.

Assessing impact of sluice gate operation on salinity intrusion in Ho Chi Minh City

This study aims to evaluate salinity intrusion propagation in Ho Chi Minh City under the operation of six tidal sluice gates, namely, Ben Nghe, Tan Thuan, Phu Xuan, Muong Chuoi, Cay Kho, and Phu Dinh by utilizing the HEC-RAS model. The outcomes during calibration and validation procedures underscore the high performance of the HEC-RAS model in simulating salinity intrusion over the study area. In particular, R2 and NSE values were higher than satisfactory thresholds of 0.80 for water level and discharge simulations and 0.50 for salinity concentration simulations. Based on the calibrated HEC-RAS model, this study examined the potential influences of sluice gate operations on salinity distribution under different scenarios. Results indicate that salinity concentration would exhibit increasing trends when operating Ben Nghe and Tan Thuan sluices, while the closure of the Phu Xuan and Muong Chuoi sluices would cause a reduction in the salinity concentration within the areas enclosed by these sluices. In addition, the operations of the Cay Kho and Phu Dinh sluices would lead to decreases and increases in salinity concentration in the upstream and downstream areas of each corresponding sluice gate, respectively. Moreover, when closing six sluice gates simultaneously, the salinity concentration inside and outside the sluice gates would decrease and increase, respectively. In general, these findings indicate that salinity concentration and spatial distribution are significantly influenced by the location and structure of each tidal sluice gate.

Hydraulic Simulation for Flow One Dimension of Shatt Al-Hilla River

Floods are considered the most serious disaster among all-natural catastrophes because they occur more frequently than any other natural hazard and strongly impact more individuals than all other natural disasters. Therefore, it is necessary to study flood risk. Hillah River is critical in securing industrial, agricultural, and civil water in three Iraqi governorates: Babil, Diwaniyah, and Muthanna. Its primary source is the Euphrates River, which extends approximately 100 kilometers within Babil Governorate – the study area. This research aims to evaluate and study the river's capacity and predict future floods by developing scenarios for anticipated events. Also, a hydraulic model was developed to assess the manning coefficient for Shatt Al-Hillah. The one-dimensional HEC-RAS 6.0.1 program has been used to simulate water flow in the river, incorporating over 350 cross-sections spaced at 250-meter intervals surveyed in 2018 by the Department of Water Resources in Babil Governorate. The model was calibrated using observed discharge data from 2004 to 2022 in Shatt al-Hillah. Subsequently, it was compared with a range of water levels by varying manning factors. The calibration results indicated that a roughness coefficient of 0.023 was suitable for unstable flow conditions, and the least mean square root error between the measured and simulated water levels was 0.053. The simulation results showed that the current capacity of Al-Hilla River was 205 m3/s, such that it cannot pass the design discharge of 303 m3/s. After conducting scenarios greater than 205 m3/s, the results showed that increasing the discharge increased the areas exposed to flooding so that when the discharge was 450 m3/s, flooding of the submerged areas increased. With a percentage of 92.2%, the northern side of Babil Governorate will be more vulnerable to flooding than the southern side because the southern part levels are lower than the northern part.

Dune Development Dominates Flow Resistance Increase in a Large Dammed River

AbstractDunes are important for bedload transport in almost all large river systems and exert an important control flow resistance. Investigating dunes is fundamental for simulating discharge, sediment transport, and flood routing. However, the spatiotemporal dynamics of dunes and flow resistance remain poorly understood in large alluvial river systems mainly due to the lack of high‐resolution data. Here we analyzed in‐field observed data on annual profiles of 450 cross‐sections, river bathymetry, discharge, water levels, and sediment in the Middle Yangtze River, and found that dunes developed and flow resistance increased (+10%) in the post‐Three Gorges Dam period. We ascertain that the development of dunes plays the dominant role in augmenting flow resistance, as evidenced by roughness height change (+29%) using a modified Van Rijn method. Ultimately, dune development was incorporated into a hydrogeomorphic numerical model of the Yangtze River to improve flow resistance quantification, and thus, the water level simulation. Our findings highlight the potential increase in flow resistance in response to upstream damming in large rivers and have important implications for flood management and riverine ecology.

The Simulation of Bracciano Lake (Central Italy) Levels Based on Hydrogeological Water Budget: A Tool for Lake Water Management when Climate Change and Anthropogenic Impacts Occur

Bracciano Lake is one of the major water bodies of Lazio region and the eighth lake in Italy by area. Similarly to other water bodies located on the Tyrrhenian side of the Italian Peninsula, Bracciano Lake has a volcanic origin. This water body is a strategic resource for both the emergency and ordinary water supply of Rome megacity, thanks to the short distance (about 30 km). During the last years, the concurrence of climate change and anthropogenic activities led to important water level drops of the lake: the last and worst in 2017, when the lake faced the most important water crisis of its entire history, with a critical impact on the ecological system and the consequent interruption of water supply. This paper, using the inverse hydrogeological water budget (HWB) method in a GIS environment, aims to define the water budget of Bracciano Lake on a 15-year time series (2008–2022), providing a framework of climate change and human impacts on lake water levels. The outputs have been used for the simulation of lake water levels during the same period, with a focus on the drought of 2017. The variation between simulated and observed level is, on average, 2 cm and in any case never more than 28 cm. Results show the fragile balance between inputs and outputs, highlighting the need for sustainable management of both surface water and groundwater withdrawals from the surrounding aquifer.

Modeling of Optimal Power Generation in Small Hydropower Plants

Abstract Small hydropower plants (SHPs) have gained international attention as a reliable and versatile renewable energy source. Unfortunately, this energy source is not used efficiently. This is because these systems utilize headwaters with lower water capacity called forebays instead of large water reservoirs like dams. Since this hinders the control of electricity generation, the headwater is either wasted before it can be converted into electricity or it is exhausted very quickly and electricity generation stops after a short time. The aim of this study is to prevent the loss of electricity generation through the efficient use of SHP headwater. For this purpose, a mathematical model was created based on two SHPs operating in tandem. The mathematical model obtained was analyzed, and forebay water level simulation graphs for electricity generation at water flowrates of 2000, 4000, 6000, and 8000 kg/h were obtained. With the help of the obtained graphs, critical wicket gate openings for maximum electricity generation at flowrates of 2000, 4000, 6000, and 8000 kg/h were calculated. The results of this study are applicable to all SHPs. These results are also promising in terms of optimizing the use of a renewable energy source.

Hydrodynamic Groundwater Modeling and Hydrochemical Conceptualization of the Closure Mining Area of the WuMa River Watershed of China.

The WuMa River (WMR) watershed is located in Renhuai City, Guizhou Province of China, which is a first-class tributary of the Chishui River. The geochemical investigation mainly included the determination of groundwater pH, total hardness, total dissolution solid, major cationic and anionic, and the geochemical groundwater modeling. The principal component analysis (PCA) and Gibbs model were used to analyze the pollution type and geochemical composition. The geochemical investigation results show that the cations of groundwater are dominated by Ca2+ and the anions are dominated by HCO3-; therefore, two main hydrochemical types in the study area are identified as Ca2+-Mg2+-HCO3- and Ca2+-Mg2+-SO42-. The chemical composition of groundwater in this area is mainly controlled by weathering of the carbonate rocks. The ion concentration of groundwater in the study area exhibited significant spatial variability between dry and wet seasons, while temporal changes of cationic and anionic concentrations exhibited irregularities. In PCA and FA analysis, PC1, PC2, and PC3 were extracted, which could explain 51.92, 26.98, and 12.61% of the total information, respectively. F1 explained 67.44% of the total variance, among which Ca2+, Mg2+, K+, SO42-, and Cl- contributed the most among the factors and were the main factors controlling the chemical composition of groundwater. The relative error between the measured water level and the simulated water level is less than 2%, which meets the requirements of simulation accuracy. During the simulation period of the model, a total recharge of 339.05 × 104 m3 was observed in the simulated area, primarily attributed to infiltration from rainfall. The total excretion amounted to 330.78 × 104 m3, primarily through evaporation, with a minor amount of lateral outflow. The migration pathway of pollutants in groundwater primarily follows the direction of groundwater flow while diffusing vertically. The migration range of the pollutant is in accordance with the direction of groundwater flow and extends along the larger hydraulic gradient, demonstrating consistency. The findings of this study serve as a reminder that the closure of coal mines can constitute a significant source of water pollution. Simultaneously, they offer empirical data and theoretical references for the simulation and prediction of groundwater contamination in enclosed coal mines.

Development of a Microservice-Based Storm Sewer Simulation System with IoT Devices for Early Warning in Urban Areas

This study proposes an integrated approach to developing a Microservice, Cloud Computing, and Software as a Service (SaaS)-based Real-Time Storm Sewer Simulation System (MBSS). The MBSS combined the Storm Water Management Model (SWMM) microservice running on the EC2 Amazon Web Services (AWS) cloud platform and an Internet of Things (IoT) monitoring device to prevent disasters in smart cities. The Python language and Docker container were used to develop the MBSS and Web API of the SWMM microservice. The IoT comprised a pressure water level meter, an Arduino, and a Raspberry Pi. After laboratory channel testing, the simulated and IoT-monitored water levels under different flow rates indicate that the simulated water level in MBSS was such as that monitored by the IoT. These findings suggest that MBSS is feasible and can be further used as a reference for smart urban early warning systems. The MBSS can be applied in on-site stormwater sewers during heavy rain, with the goal of issuing early warnings and reducing disaster damage. The use case can be the process by which the SWMM model parameters will be optimized based on the water level data from IoT monitoring devices in stormwater sewer systems. The predicted rainfall will then be used by the SWMM microservices of MBSS to simulate the water levels at all manholes. The status of the water levels will finally be applied to early warning.