Monitoring Water Levels in Fresh Water Tank Using The Concept of IoT (Internet of Think)

2021 ◽  
Vol 1 (4) ◽  
pp. 120-126
Author(s):  
Edi Kurniawan ◽  
Heri Sularno ◽  
I'ie Suwondo ◽  
Anak Agung Istri S.W
Keyword(s):  
Water Level ◽  
Fresh Water ◽  
Water Levels ◽  
The Internet ◽  
Water Tank ◽  
Level Data ◽  
Water Level Data ◽  
The Right ◽  
User Friendly ◽  
Height Data

Fresh water generator is one of the most important auxiliary aircraft on ships to produce fresh water. The efficient use of fresh water can extend the life of the fresh water generator and save electricity usage. Efficient use of fresh water can be done by remotely monitoring the level of fresh water in the tank in real time.The system for knowing the water level in real time is built with an ultrasonic sensor to transmit data to the Wemos in the form of height data. Wemos converts freshwater level data into the volume of water in the tank. The volume and water level data is then displayed on the LCD and the Wemos sends data on the volume of fresh water to the internet in the form of a website with a design that is easy to understand (user friendly) and the website can be accessed anywhere. It can be seen that the system can work properly because the highest error reading is only 5%, namely in 4 liters with a tilt position og 20 right . Meanwhile, the biggest difference between sendor readings and real when testing 5 liters with a slope of 30 to the right is 0.23 liters. The best average result occur when testing flat conutions.

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 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 Characteristics of Duwet Karst Spring Based on Baseflow Index Assessment and its Potential in Fresh Water Supply

GeoEco ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 38
Author(s):  
Pipit Wijayanti ◽  
Rita Noviani
Keyword(s):  
Water Level ◽  
Fresh Water ◽  
Rainy Season ◽  
Dry Season ◽  
Base Flow ◽  
Spring Discharge ◽  
Discharge Data ◽  
Level Data ◽  
Water Level Data ◽  
Total Base

<p><em>This study aims to determine the potential of Duwet springs and their availability for supplying fresh water to the surrounding community. we use hydrographs to analyze aquifer characteristics. To analyze the hydrographs, we use water level and spring discharge data. Automatic Water Level Record (AWLR) records water level data for 1 dry season and 1 rainy season every 15 minutes. We use the volumetric method to measure the spring discharge 14 times. We compare the base flow and demand over a year to analyze the potential for fresh water. The results show that the Stage discharge rating curve y = 0.0002e5,453x with R² value of 0.87. Duwet Springs is a perennial spring that has a small discharge (class VI). The largest discharge ever recorded was 0.69 L/s (March 7, 2020) and the smallest recorded was 0.12 L/s (August 21, 2020). BFI value varied between 0.05 and 1 with mean 0.801. The total base flow is 2490675.734 L (rainy season) and 1563419.873 L (dry season). These springs are sufficient for 75% of the rainy season and 84% in the dry season. This indicates that the existence of Duwet springs is very important for the surrounding community.</em></p>

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 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 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.

scholarly journals A model involving meteorological factors for short-to-medium term water level prediction of small- and medium-sized urban rivers

2021 ◽  
Author(s):  
Yawei Qin ◽  
Yongjin Lei ◽  
Xiangyu Gong ◽  
Wanglai Ju
Keyword(s):  
Water Level ◽  
Flood Control ◽  
Water Levels ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Urban Rivers ◽  
Medium Term ◽  
Level Data ◽  
Water Level Data ◽  
Svm Model

Abstract With the increasing of extreme weathers, cities, especially the small- and medium-sized urban rivers with the protection areas less than 200 square hectares, are experiencing significantly more flood disasters worldwide. Heavy snowfalls and rainfalls can rapidly overflow these rivers and cause floods due to the their unique geographic locations and fast runoff and confluence. Therefore, it is particularly important to accurately predict the short-to-medium term water levels of such rivers for reducing and avoiding urban floods. In the present work, a particle swarm optimization (PSO)-support vector machine (SVM) water level predication model was constructed by combining PSO and SVM and trained with the meteorological data of Wuhan, China, and the water level data of Yangtze River. The PSO-SVM model is able to lower mean square error (MSE) 70.47% and increase coefficient of determination (R2) 7.02% of the prediction results, as compared with SVM model alone. The highly accurate PSO-SVM model can be used to predict river water level real-time using the hourly weather and water level data, which thereby provides quantitative data support for urban flood control, construction management of water projects, improving response efficiency and reducing safety risks.

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