scholarly journals Deteksi Level Ketinggian Air Dengan Nikelin, Encoder dan Sensor Tekanan Untuk Sistem Peringatan Dini Banjir

2021 ◽  
Vol 1 ◽  
pp. 45-49
Author(s):  
Latiful Hayat ◽  
Dian Nova Kusuma Hardani
Keyword(s):  
Water Level ◽  
Early Warning ◽  
Pressure Sensor ◽  
Pressure Sensors ◽  
Warning System ◽  
Water Levels ◽  
Nickel Wire ◽  
High Data ◽  
Level Data ◽  
Water Level Data

Floods and their problems show an increasing indication when rainfall is high. Data from BNPB shows that floods, landslides and tornadoes contributed to the total disasters in Indonesia in a decade. The existence of an early warning flood disaster can help evacuate before a disaster strikes. The system requires a water level detector as the basic data for determining flood predictions. In order to get the water level value, a touch water method can be used using electrodes or without touching the water with the help of pressure sensors, ultrasonic and imaging. Each method has advantages over the other. In this study, the effectivity and accuracy of detecting water levels were investigated using 3 methods: the direct touch of water through nickel wire, buoys with encoder, and pressure sensors. Detection of water levels can be used as a reference to obtain river water level data which is then connected via an IoT or internet connection as a reference for the Early Warning System for the arrival of floods. This study found that changes in water level of less than 30 cm can utilize buoys and encoders with an accuracy of detecting 5 to 6 counts per 1 mm increase in water level. Meanwhile, the measurement of less than 30 cm water level using nickel wire resulted in a non-linear value. The utilization of nickel wire can be used for a height of more than 30 cm where the change in resistivity has started to be linear. ADC change value is 2.93 mV/cm using 10 bit ADC at 5 Volt reference voltage. For water level heights of 50 cm and above, a pressure sensor can use a pressure sensor that can detect changes in pressure of 0.002 in Hg/mm or 0.05 mmHg/mm.

scholarly journals Transfer Entropy as a Tool for Hydrodynamic Model Validation

Entropy ◽  
2018 ◽  
Vol 20 (1) ◽  
pp. 58 ◽  
Author(s):  
Alicia Sendrowski ◽  
Kazi Sadid ◽  
Ehab Meselhe ◽  
Wayne Wagner ◽  
David Mohrig ◽  
...  
Keyword(s):  
Water Level ◽  
Numerical Models ◽  
Water Levels ◽  
Transfer Entropy ◽  
Spatial And Temporal Scales ◽  
River Deltas ◽  
Level Data ◽  
Water Level Data ◽  
Wax Lake Delta ◽  
Validation Tool

The validation of numerical models is an important component of modeling to ensure reliability of model outputs under prescribed conditions. In river deltas, robust validation of models is paramount given that models are used to forecast land change and to track water, solid, and solute transport through the deltaic network. We propose using transfer entropy (TE) to validate model results. TE quantifies the information transferred between variables in terms of strength, timescale, and direction. Using water level data collected in the distributary channels and inter-channel islands of Wax Lake Delta, Louisiana, USA, along with modeled water level data generated for the same locations using Delft3D, we assess how well couplings between external drivers (river discharge, tides, wind) and modeled water levels reproduce the observed data couplings. We perform this operation through time using ten-day windows. Modeled and observed couplings compare well; their differences reflect the spatial parameterization of wind and roughness in the model, which prevents the model from capturing high frequency fluctuations of water level. The model captures couplings better in channels than on islands, suggesting that mechanisms of channel-island connectivity are not fully represented in the model. Overall, TE serves as an additional validation tool to quantify the couplings of the system of interest at multiple spatial and temporal scales.

scholarly journals Multi-objective optimization of typhoon inundation forecast models for water-level gauging network by integration of ARMAX with a Genetic Algorithm

2016 ◽  
Author(s):  
Huei-Tau Ouyang
Keyword(s):  
Genetic Algorithm ◽  
Water Level ◽  
Water Levels ◽  
Time Shift ◽  
Multi Objective ◽  
Cumulative Rainfall ◽  
Level Data ◽  
Water Level Data ◽  
Forecast Models ◽  
Gauging Station

Abstract. The forecasting of inundation levels during typhoons requires that multiple objectives be taken into account, including the forecasting capacity with regard to variations in water level throughout the entire weather event, the accuracy that can be attained in forecasting peak water levels and the time at which peak water levels are likely to occur. This paper proposed a means of forecasting inundation levels in real-time using monitoring data from a water-level gauging network. ARMAX was used to construct water-level forecast models for each gauging station using input variables including cumulative rainfall and water level data from other gauging stations in the network. Analysis of the correlation between cumulative rainfall and water level data makes it possible to obtain an approximation as to the cumulative duration of rainfall and time lags associated with each gauging station. Analyses on water levels as well as on cumulative rainfall enable the identification of associate sites pertained to each gauging station that share high correlations with regard to water level and low mutual information with regard to cumulative rainfall. Water level data from associate sites is used as a second input variable for the water-level forecast model of the target site. Three indices were considered in the selection of an optimal model: the coefficient of efficiency (CE), error in the stage of peak water level (ESP), and relative time shift (RTS). We used a multi-objective genetic algorithm to derive an optimal Pareto set of models capable of performing well in the three objectives. A case study was conducted on the Xinnan area of Yilan County, Taiwan in which optimal water-level forecast models were established for each of the four water-level gauging stations in the area. Test results demonstrate that the model best able to satisfy PE exhibited significant time shift, whereas the models best able to satisfy CE and RTS provide accurate forecasts of inundations when variations in water level are less extreme.

scholarly journals Assimilating SAR-derived water level data into a hydraulic model: a case study

2011 ◽  
Vol 8 (1) ◽  
pp. 2103-2144 ◽  
Author(s):  
L. Giustarini ◽  
P. Matgen ◽  
R. Hostache ◽  
M. Montanari ◽  
D. Plaza ◽  
...  
Keyword(s):  
Particle Filter ◽  
Water Level ◽  
Cross Sections ◽  
Hydraulic Model ◽  
Water Levels ◽  
Hydraulic Models ◽  
Level Data ◽  
Water Level Data ◽  
Assimilation Scheme

Abstract. Satellite-based active microwave sensors not only provide synoptic overviews of flooded areas, but also offer an effective way to estimate spatially distributed river water levels. If rapidly produced and processed, these data can be used for updating hydraulic models in near real-time. The usefulness of such approaches with real event data sets provided by currently existing sensors has yet to be demonstrated. In this case study, a Particle Filter-based assimilation scheme is used to integrate ERS-2 SAR and ENVISAT ASAR-derived water level data into a one-dimensional (1-D) hydraulic model of the Alzette River. Two variants of the Particle Filter assimilation scheme are proposed with a global and local particle weighting procedure. The first option finds the best water stage line across all cross sections, while the second option finds the best solution at individual cross sections. The variant that is to be preferred depends on the level of confidence that is attributed to the observations or to the model. The results show that the Particle Filter-based assimilation of remote sensing-derived water elevation data provides a significant reduction to the model forecast uncertainty. Moreover, it is shown that the periodical updating of hydraulic models through the proposed assimilation scheme leads to an improvement of model predictions over several time steps. However, the performance of the assimilation depends on the skill of the hydraulic model and the quality of the observation data.

scholarly journals Particle Tracking Using Dynamic Water-Level Data

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 2063
Author(s):  
Yuan Gao
Keyword(s):  
Groundwater Flow ◽  
Water Level ◽  
Water Table ◽  
Flow Direction ◽  
Water Levels ◽  
Subsurface Water ◽  
Pressure Transducers ◽  
Level Data ◽  
Water Level Data ◽  
Fluid Particles

The movement of fluid particles about historic subsurface releases is often governed by dynamic subsurface water levels. Motivations for tracking the movement of fluid particles include tracking the fate of subsurface contaminants and resolving the fate of water stored in subsurface aquifers. This study provides a novel method for predicting the movement of subsurface particles relying on dynamic water-level data derived from continuously recording pressure transducers. At least three wells are needed to measure water levels which are used to determine the plain of the water table. Based on Darcy’s law, particle flow pathlines at the study site are obtained using the slope of the water table. The results show that hydrologic conditions, e.g., seasonal transpiration and precipitation, influence local groundwater flow. The changes of water level in short periods caused by the hydrologic variations made the hydraulic gradient diversify considerably, thus altering the direction of groundwater flow. Although a range of groundwater flow direction and gradient with time can be observed by an initial review of water levels in rose charts, the net groundwater flow at all field sites is largely constant in one direction which is driven by the gradients with higher magnitude.

scholarly journals Can an early-warning system help minimize the impacts of coastal storms? A case study of the 2012 Halloween storm, northern Italy

2016 ◽  
Vol 16 (1) ◽  
pp. 209-222 ◽  
Author(s):  
M. D. Harley ◽  
A. Valentini ◽  
C. Armaroli ◽  
L. Perini ◽  
L. Calabrese ◽  
...  
Keyword(s):  
Water Level ◽  
Early Warning ◽  
Early Warning System ◽  
Numerical Models ◽  
Warning System ◽  
Northern Italy ◽  
Water Levels ◽  
Storm Event ◽  
Forecast Performance ◽  
High Hazard

Abstract. The Emilia-Romagna early-warning system (ER-EWS) is a state-of-the-art coastal forecasting system that comprises a series of numerical models (COSMO, ROMS, SWAN and XBeach) to obtain a daily 3-day forecast of coastal storm hazard at eight key sites along the Emilia-Romagna coastline (northern Italy). On the night of 31 October 2012, a major storm event occurred that resulted in elevated water levels (equivalent to a 1-in-20- to 1-in-50-year event) and widespread erosion and flooding. Since this storm happened just 1 month prior to the roll-out of the ER-EWS, the forecast performance related to this event is unknown. The aim of this study was to therefore reanalyse the ER-EWS as if it had been operating a day before the event and determine to what extent the forecasts may have helped reduce storm impacts. Three different reanalysis modes were undertaken: (1) a default forecast (DF) mode based on 3-day wave and water-level forecasts and default XBeach parameters; (2) a measured offshore (MO) forecast mode using wave and water-level measurements and default XBeach parameters; and (3) a calibrated XBeach (CX) mode using measured boundary conditions and an optimized parameter set obtained through an extensive calibration process. The results indicate that, while a "code-red" alert would have been issued for the DF mode, an underprediction of the extreme water levels of this event limited high-hazard forecasts to only two of the eight ER-EWS sites. Forecasts based on measured offshore conditions (the MO mode) more-accurately indicate high-hazard conditions for all eight sites. Further considerable improvements are observed using an optimized XBeach parameter set (the CX mode) compared to default parameters. A series of what-if scenarios at one of the sites show that artificial dunes, which are a common management strategy along this coastline, could have hypothetically been constructed as an emergency procedure to potentially reduce storm impacts.

scholarly journals Multi-objective optimization of typhoon inundation forecast models with cross-site structures for a water-level gauging network by integrating ARMAX with a genetic algorithm

2016 ◽  
Vol 16 (8) ◽  
pp. 1897-1909 ◽  
Author(s):  
Huei-Tau Ouyang
Keyword(s):  
Genetic Algorithm ◽  
Water Level ◽  
Water Levels ◽  
Time Shift ◽  
Cumulative Rainfall ◽  
Level Data ◽  
Water Level Data ◽  
Forecast Models ◽  
Gauging Station ◽  
Cross Site

Abstract. The forecasting of inundation levels during typhoons requires that multiple objectives be taken into account, including the forecasting capacity with regard to variations in water level throughout the entire weather event, the accuracy that can be attained in forecasting peak water levels, and the time at which peak water levels are likely to occur. This paper proposed a means of forecasting inundation levels in real time using monitoring data from a water-level gauging network. ARMAX was used to construct water-level forecast models for each gauging station using input variables including cumulative rainfall and water-level data from other gauging stations in the network. Analysis of the correlation between cumulative rainfall and water-level data makes it possible to obtain the appropriate accumulation duration of rainfall and the time lags associated with each gauging station. Analyses on cross-site water levels as well as on cumulative rainfall enable the identification of associate sites pertaining to each gauging station that share high correlations with regard to water level and low mutual information with regard to cumulative rainfall. Water-level data from the identified associate sites are used as a second input variable for the water-level forecast model of the target site. Three indices were considered in the selection of an optimal model: the coefficient of efficiency (CE), error in the stage of peak water level (ESP), and relative time shift (RTS). A multi-objective genetic algorithm was employed to derive an optimal Pareto set of models capable of performing well in the three objectives. A case study was conducted on the Xinnan area of Yilan County, Taiwan, in which optimal water-level forecast models were established for each of the four water-level gauging stations in the area. Test results demonstrate that the model best able to satisfy ESP exhibited significant time shift, whereas the models best able to satisfy CE and RTS provide accurate forecasts of inundations when variations in water level are less extreme.

scholarly journals Threshold Definition for Monitoring Gapa Landslide under Large Variations in Reservoir Level Using GNSS

2021 ◽  
Vol 13 (24) ◽  
pp. 4977
Author(s):  
Shuangshuang Wu ◽  
Xinli Hu ◽  
Wenbo Zheng ◽  
Matteo Berti ◽  
Zhitian Qiao ◽  
...  
Keyword(s):  
Water Level ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
Water Levels ◽  
Deformation Model ◽  
Warning Systems ◽  
Velocity Threshold ◽  
Landslide Deformation

The triggering threshold is one of the most important parameters for landslide early warning systems (EWSs) at the slope scale. In the present work, a velocity threshold is recommended for an early warning system of the Gapa landslide in Southwest China, which was reactivated by the impoundment of a large reservoir behind Jinping’s first dam. Based on GNSS monitoring data over the last five years, the velocity threshold is defined by a novel method, which is implemented by the forward and reverse double moving average of time series. As the landslide deformation is strongly related to the fluctuations in reservoir water levels, a crucial water level is also defined to reduce false warnings from the velocity threshold alone. In recognition of the importance of geological evolution, the evolution process of the Gapa landslide from topping to sliding is described in this study to help to understand its behavior and predict its potential trends. Moreover, based on the improved Saito’s three-stage deformation model, the warning level is set as “attention level”, because the current deformation stage of the landslide is considered to be between the initial and constant stages. At present, the early warning system mainly consists of six surface displacement monitoring sites and one water level observation site. If the daily recorded velocity in each monitoring site exceeds 4 mm/d and, meanwhile, the water level is below 1820 m above sea level (asl), a warning of likely landslide deformation accelerations will be released by relevant monitoring sites. The thresholds are always discretely exceeded on about 3% of annual monitoring days, and they are most frequently exceeded in June (especially in mid-June). The thresholds provide an efficient and effective way for judging accelerations of this landslide and are verified by the current application. The work presented provides critical insights into the development of early warning systems for reservoir-induced large-scale landslides.

scholarly journals Identification of Hydrological Droughts in Lithuanian Rivers

2021 ◽  
pp. 87-99
Author(s):  
Gintarė Kugytė ◽  
Gintaras Valiuškevičius
Keyword(s):  
Water Level ◽  
Water Flow ◽  
Flow Rate ◽  
Hydrological Drought ◽  
Water Levels ◽  
True Value ◽  
Level Data ◽  
Water Level Data ◽  
Lithuanian Rivers ◽  
Hydrological Droughts

Globally, hydrological droughts are most commonly identified based on various indices calculated from water flow values. However, the water flow rate is calculated from a flow rate curve that needs to be updated constantly, so it takes a long time to resolve its true value. For this reason, the possibility of identifying a hydrological drought on the basis of hourly and prompt treated water levels seems much more attractive. 8 water gauging stations (WGS) operating along 7 important rivers and covering the hydrological areas of visas in the Lithuanian region were selected for the study. In this study, a modified SPI function of the R programming language SPEI package (traditionally used to calculate the standardized precipitation index, SPI) was applied for the streamflow drought index (SDI) calculations. Given how it was applied to the SDI calculation, just like the baseline data, this was the ten-day mean water flow and then the water level. The suitability of water level data for SDI calculations was assessed by analyzing the relationships between SWLI (Standartized Water Level Index) calculated from water level data and SDI calculated from water flow information. SWLI and SDI in all 8 WGS are closely interconnected. It was found that the possibility of recurrence of droughts of different severity identified by both methods is significantly influenced by the profile of the river bed in a specific section. In areas where riverbanks have steeper slopes, the SWLI and SDI similarly describes the water situation and the recurrence of droughts. It is believed that a modified SDI methodology (SWLI), which is based on water level data, may become a good alternative in our country for identifying hydrological droughts. Keywords: Lithuanian rivers, hydrological drought, identification of droughts, water level, SDI, SWLI.

scholarly journals Assimilating SAR-derived water level data into a hydraulic model: a case study

2011 ◽  
Vol 15 (7) ◽  
pp. 2349-2365 ◽  
Author(s):  
L. Giustarini ◽  
P. Matgen ◽  
R. Hostache ◽  
M. Montanari ◽  
D. Plaza ◽  
...  
Keyword(s):  
Particle Filter ◽  
Water Level ◽  
Cross Sections ◽  
Hydraulic Model ◽  
Water Levels ◽  
Hydraulic Models ◽  
Level Data ◽  
Water Level Data ◽  
Assimilation Scheme

Abstract. Satellite-based active microwave sensors not only provide synoptic overviews of flooded areas, but also offer an effective way to estimate spatially distributed river water levels. If rapidly produced and processed, these data can be used for updating hydraulic models in near real-time. The usefulness of such approaches with real event data sets provided by currently existing sensors has yet to be demonstrated. In this case study, a Particle Filter-based assimilation scheme is used to integrate ERS-2 SAR and ENVISAT ASAR-derived water level data into a one-dimensional (1-D) hydraulic model of the Alzette River. Two variants of the Particle Filter assimilation scheme are proposed with a global and local particle weighting procedure. The first option finds the best water stage line across all cross sections, while the second option finds the best solution at individual cross sections. The variant that is to be preferred depends on the level of confidence that is attributed to the observations or to the model. The results show that the Particle Filter-based assimilation of remote sensing-derived water elevation data provides a significant reduction in the uncertainty at the analysis step. Moreover, it is shown that the periodical updating of hydraulic models through the proposed assimilation scheme leads to an improvement of model predictions over several time steps. However, the performance of the assimilation depends on the skill of the hydraulic model and the quality of the observation data.

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