scholarly journals Stochastic Modeling for Estimating Real-Time Inundation Depths at Roadside IoT Sensors Using the ANN-Derived Model

Water ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3128
Author(s):  
Shiang-Jen Wu ◽  
Chih-Tsu Hsu ◽  
Che-Hao Chang
Keyword(s):  
Real Time ◽  
Transfer Functions ◽  
High Reliability ◽  
Correction Method ◽  
Water Levels ◽  
Lead Times ◽  
Generation Model ◽  
Inundation Depth ◽  
Simulated Rain ◽  
Northern Taiwan

This paper aims to develop a stochastic model (SM_EID_IOT) for estimating the inundation depths and associated 95% confidence intervals at the specific locations of the roadside water-level gauges, i.e., Internet of Things (IoT) sensors under the observed water levels/rainfalls and the precipitation forecasts given. The proposed SM_EID_IOT model is an ANN-derived one, a modified artificial neural network model (i.e., the ANN_GA-SA_MTF) in which the associated ANN weights are calibrated via a modified genetic algorithm with a variety of transfer functions considered. To enhance the reliability and accuracy of the proposed SM_EID_IOT model in the estimations of the inundation depths at the IoT sensors, a great number of the rainfall induced flood events as the training and validation datasets are simulated by the 2D hydraulic dynamic (SOBEK) model with the simulated rain fields via the stochastic generation model for the short-term gridded rainstorms. According to the results of model demonstration, Nankon catchment, located in northern Taiwan, the proposed SM_EID_IOT model can estimate the inundation depths at the various lead times with high reliability in capturing the validation datasets. Moreover, through the integrated real-time error correction method integrated with the proposed SM_EID_IOT model, the resulting corrected inundation-depth estimates exhibit a good agreement with the validated ones in time under an acceptable bias.

scholarly journals Stochastic modeling of artificial neural networks for real-time hydrological forecasts based on uncertainties in transfer functions and ANN weights

2021 ◽  
Author(s):  
Shiang-Jen Wu ◽  
Chih-Tsung Hsu ◽  
Che-Hao Chang
Keyword(s):  
Real Time ◽  
Transfer Functions ◽  
Prediction Interval ◽  
Forecast Error ◽  
Correction Method ◽  
Study Data ◽  
Water Levels ◽  
Model Verification ◽  
The Real ◽  
Artificial Neural

Abstract This study proposes a stochastic artificial neural network (named ANN_GA-SA_MTF), in which the parameters of the multiple transfer functions considered are calibrated by the modified genetic algorithm (GA-SA), to effectively provide the real-time forecasts of hydrological variates and the associated reliabilities under the observation and predictions given (model inputs); also, the resulting forecasts can be adjusted through the real-time forecast-error correction method (RTEC_TS&KF) based on difference between real-time observations and forecasts. The observed 10-days rainfall depths and water levels (i.e., hydrological estimates) from 2008 to 2018 recorded within the Shangping sub-basin in northern Taiwan are adopted as the study data and their stochastic properties are quantified for simulating 1,000 sets of rainfall and water levels at 36 10-days periods as the training datasets. The results from the model verification indicate that the observed 10-days rainfall depths and water levels are obviously located at the prediction interval (i.e., 95% confidence interval), revealing that the proposed ANN_GA-SA_MTF model can capture the temporal behavior of 10-days rainfall depths and water levels within the study area. In spite of the resulting forecasts with an acceptable difference from the observation, their real-time corrections have evident agreement with the observations, namely, the resulting adjusted forecasts with high accuracy.

Integrated real-time flood forecasting and inundation analysis in small-medium streams

2019 ◽  
Author(s):  
Byunghyun Kim ◽  
Seung-Yong Choi ◽  
Kun-Yeun Han
Keyword(s):  
Real Time ◽  
Fuzzy Inference ◽  
Flood Forecasting ◽  
Water Levels ◽  
Subtractive Clustering ◽  
Lead Times ◽  
Hydrodynamic Models ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Input Variables

This study presents the application of an adaptive neuro-fuzzy inference system (ANFIS) and one dimensional (1-D) and two dimensional (2-D) hydrodynamic models to improve the problems of hydrological models currently used for flood forecasting in small-medium streams of South Korea. The optimal combination of input variables (e.g., rainfall and water level) in ANFIS was selected based on a statistical analysis of the observed and forecasted values. Two membership functions (MFs) and two ANFIS rules were determined by the subtractive clustering (SC) approach in the processes of training and checking. The developed ANFIS was applied to Jungrang Stream and water levels for six lead times (0.5, 1.0, 1.5, 2.0, 2.5 and 3.0 hour) were forecasted. Based on point forecasted water levels by ANFIS, 1-D section flood forecast and 2-D spatial inundation analysis were carried out. This study demonstrated that the proposed methodology can forecast flooding based only on observed data without abundant physical, and can be performed in real time by integrating point- and section flood forecasting and spatial inundation analysis.

scholarly journals Integrated Real-Time Flood Forecasting and Inundation Analysis in Small–Medium Streams

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 919 ◽  
Author(s):  
Byunghyun Kim ◽  
Seng Yong Choi ◽  
Kun-Yeun Han
Keyword(s):  
Water Level ◽  
Real Time ◽  
Fuzzy Inference ◽  
Flood Forecasting ◽  
Water Levels ◽  
Subtractive Clustering ◽  
Lead Times ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Input Variables

This study presents the application of an adaptive neuro-fuzzy inference system (ANFIS) and one dimensional (1-D) and two dimensional (2-D) hydrodynamic models to improve the problems of hydrological models currently used for flood forecasting in small–medium streams of South Korea. The optimal combination of input variables (e.g., rainfall and water level) in ANFIS was selected based on a statistical analysis of the observed and forecasted values. Two membership functions (MFs) and two ANFIS rules were determined by the subtractive clustering (SC) approach in the processes of training and checking. The developed ANFIS was applied to Jungrang Stream and water levels for six lead times (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 hour) were forecasted. Based on point forecasted water levels by ANFIS, 1-D section flood forecast and 2-D spatial inundation analysis were carried out. This study demonstrated that the proposed methodology can forecast flooding based only on observed rainfall and water level without extensive physical and topographic data, and can be performed in real-time by integrating point- and section flood forecasting and spatial inundation analysis.

AUTOMATIC WATER LEVEL MONITORING WITH IOT AND LPWAN

2019 ◽  
pp. 95-103
Author(s):  
Krum Videnov ◽  
Vanya Stoykova
Keyword(s):  
Water Level ◽  
Real Time ◽  
Early Warning ◽  
Water Sources ◽  
Water Levels ◽  
Water Level Fluctuations ◽  
Level Monitoring ◽  
Water Level Monitoring

Monitoring water levels of lakes, streams, rivers and other water basins is of essential importance and is a popular measurement for a number of different industries and organisations. Remote water level monitoring helps to provide an early warning feature by sending advance alerts when the water level is increased (reaches a certain threshold). The purpose of this report is to present an affordable solution for measuring water levels in water sources using IoT and LPWAN. The assembled system enables recording of water level fluctuations in real time and storing the collected data on a remote database through LoRaWAN for further processing and analysis.

scholarly journals Flood Inundation Assessment in the Low-Lying River Basin Considering Extreme Rainfall Impacts and Topographic Vulnerability

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 896
Author(s):  
Thanh Thu Nguyen ◽  
Makoto Nakatsugawa ◽  
Tomohito J. Yamada ◽  
Tsuyoshi Hoshino
Keyword(s):  
River Basin ◽  
Extreme Rainfall ◽  
Flood Damage ◽  
Water Levels ◽  
Flood Inundation ◽  
Inundation Area ◽  
Inundation Depth ◽  
Study Results ◽  
Inundation Duration ◽  
Short Time

This study aims to evaluate the change in flood inundation in the Chitose River basin (CRB), a tributary of the Ishikari River, considering the extreme rainfall impacts and topographic vulnerability. The changing impacts were assessed using a large-ensemble rainfall dataset with a high resolution of 5 km (d4PDF) as input data for the rainfall–runoff–inundation (RRI) model. Additionally, the prediction of time differences between the peak discharge in the Chitose River and peak water levels at the confluence point intersecting the Ishikari River were improved compared to the previous study. Results indicate that due to climatic changes, extreme river floods are expected to increase by 21–24% in the Ishikari River basin (IRB), while flood inundation is expected to be severe and higher in the CRB, with increases of 24.5, 46.5, and 13.8% for the inundation area, inundation volume, and peak inundation depth, respectively. Flood inundation is likely to occur in the CRB downstream area with a frequency of 90–100%. Additionally, the inundation duration is expected to increase by 5–10 h here. Moreover, the short time difference (0–10 h) is predicted to increase significantly in the CRB. This study provides useful information for policymakers to mitigate flood damage in vulnerable areas.

scholarly journals Probe-based confocal laser endomicroscopy and Barrett's esophagus – just a scientific toy or significant improvement in diagnosis?

2021 ◽  
Author(s):  
Luka Vranić ◽  
Tin Nadarević ◽  
Davor Štimac
Keyword(s):  
Diagnostic Accuracy ◽  
Real Time ◽  
Barrett’S Esophagus ◽  
Predictive Value ◽  
Barrett's Esophagus ◽  
High Reliability ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Imaging Method ◽  
Increased Risk

Background: Barrett’s esophagus (BE) requires surveillance to identify potential neoplasia at early stage. Standard surveillance regimen includes random four-quadrant biopsies by Seattle protocol. Main limitations of random biopsies are high risk of sampling error, difficulties in histology interpretation, common inadequate classification of pathohistological changes, increased risk of bleeding and time necessary to acquire the final diagnosis. Probe-based confocal laser endomicroscopy (pCLE) has emerged as a potential tool with an aim to overcome these obvious limitations. Summary: pCLE represents real-time microscopic imaging method that offers evaluation of epithelial and subepithelial structures with 1000-fold magnification. In theory, pCLE has potential to eliminate the need for biopsy in BE patient. The main advantages would be real-time diagnosis and decision making, greater diagnostic accuracy and to evaluate larger area compared to random biopsies. Clinical pCLE studies in esophagus show high diagnostic accuracy and its high negative predictive value offers high reliability and confidence to exclude dysplastic and neoplastic lesions. However, it still cannot replace histopathology due to lower positive predictive value and sensitivity. Key messages: Despite promising results, its role in routine use in patients with Barrett’s esophagus remains questionable primarily due to lack of well-organized double-blind randomized trials.

Real Time Monitoring and Controlling of Water Levels in Tank with Improved Blynk Features

2021 ◽  
Author(s):  
G. V. Nagesh Kumar ◽  
C. Bhavana Reddy ◽  
K. Vijay Kumar ◽  
D. Prasanna Kumari ◽  
P. Sunil ◽  
...  
Keyword(s):  
Real Time ◽  
Water Levels ◽  
Real Time Monitoring

scholarly journals A New Tool to Estimate Inundation Depths by Spatial Interpolation (RAPIDE): Design, Application and Impact on Quantitative Assessment of Flood Damages

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1805 ◽  
Author(s):  
Anna Scorzini ◽  
Alessio Radice ◽  
Daniela Molinari
Keyword(s):  
River Basin ◽  
Water Depth ◽  
Spatial Interpolation ◽  
Large Scale ◽  
Quantitative Estimation ◽  
Digital Terrain Model ◽  
Water Levels ◽  
Flood Depth ◽  
Inundation Depth ◽  
Basin Scale

Rapid tools for the prediction of the spatial distribution of flood depths within inundated areas are necessary when the implementation of complex hydrodynamic models is not possible due to time constraints or lack of data. For example, similar tools may be extremely useful to obtain first estimates of flood losses in the aftermath of an event, or for large-scale river basin planning. This paper presents RAPIDE, a new GIS-based tool for the estimation of the water depth distribution that relies only on the perimeter of the inundation and a digital terrain model. RAPIDE is based on a spatial interpolation of water levels, starting from the hypothesis that the perimeter of the flooded area is the locus of points having null water depth. The interpolation is improved by (i) the use of auxiliary lines, perpendicular to the river reach, along which additional control points are placed and (ii) the possibility to introduce a mask for filtering interpolation points near critical areas. The reliability of RAPIDE is tested for the 2002 flood in Lodi (northern Italy), by comparing the inundation depth maps obtained by the rapid tool to those from 2D hydraulic modelling. The change of the results, related to the use of either method, affects the quantitative estimation of direct damages very limitedly. The results, therefore, show that RAPIDE can provide accurate flood depth predictions, with errors that are fully compatible with its use for river-basin scale flood risk assessments and civil protection purposes.

On Improving the Performances of Hydraulic Incremental Positioning Units with Mechanical Position Feedback

2012 ◽  
Vol 463-464 ◽  
pp. 1277-1280 ◽  
Author(s):  
Constantin Bucşan ◽  
Mihai Avram
Keyword(s):  
Real Time ◽  
Correction Method ◽  
Practical Application ◽  
Positioning Accuracy ◽  
Positioning Systems ◽  
Sensing System ◽  
Position Feedback ◽  
Time Determination ◽  
Interesting Solution

This paper presents a method for increasing the speed and the positioning accuracy of the positioning systems with mechanical position feedback. The method consists in using a position transducer for real time determination of the position of the load and correcting this position using an adequate algorithm. It is preferable not to modify the construction of the positioning unit, allowing the user to decide when to use this correction method according to the practical application. An interesting solution to this problem is to use an external space-position finding sensing system, as presented in the paper.

scholarly journals THE BEHAVIOR CHARACTERISTICS OF A RESERVOIR LEVEE SUBJECTED TO INCREASING WATER LEVELS

2017 ◽  
Vol 23 (1) ◽  
pp. 15-27
Author(s):  
Chung-Won LEE ◽  
Yong-Seong KIM ◽  
Sung-Yong PARK ◽  
Dong-Gyun KIM ◽  
Gunn HEO
Keyword(s):  
Hydraulic Fracturing ◽  
Real Time ◽  
Pore Water ◽  
Volumetric Strain ◽  
Water Levels ◽  
The Core ◽  
The Stability ◽  
Centrifugal Model ◽  
Behavior Characteristics ◽  
Plastic Volumetric Strain

Centrifugal model testing has been widely used to study the stability of levees. However, there have been a limited number of physical studies on levees where the velocity of increasing water levels was considered. To investigate the behavior characteristics of reservoir levees with different velocities of increasing water levels, centrifugal model tests and seepage-deformation coupled analyses were conducted. Through this study, it was confirmed that increasing water levels at higher velocities induces dramatic increases in the displacement, plastic volumetric strain and risk of hydraulic fracturing occurring in the core of the levee. Hence, real-time monitoring of the displacement and the pore water pres­sure of a levee is important to ensure levee stability.

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