scholarly journals Real-time flood forecasting by employing artificial neural network based model with zoning matching approach

2011 ◽  
Vol 8 (5) ◽  
pp. 9357-9393 ◽  
Author(s):  
M. Sulaiman ◽  
A. El-Shafie ◽  
O. Karim ◽  
H. Basri
Keyword(s):  
Water Level ◽  
Real Time ◽  
High Volume ◽  
High Water ◽  
Flood Forecasting ◽  
Forecasting Model ◽  
Flood Events ◽  
High Water Level ◽  
Level Data ◽  
Water Level Data

Abstract. Flood forecasting models are a necessity, as they help in planning for flood events, and thus help prevent loss of lives and minimize damage. At present, artificial neural networks (ANN) have been successfully applied in river flow and water level forecasting studies. ANN requires historical data to develop a forecasting model. However, long-term historical water level data, such as hourly data, poses two crucial problems in data training. First is that the high volume of data slows the computation process. Second is that data training reaches its optimal performance within a few cycles of data training, due to there being a high volume of normal water level data in the data training, while the forecasting performance for high water level events is still poor. In this study, the zoning matching approach (ZMA) is used in ANN to accurately monitor flood events in real time by focusing the development of the forecasting model on high water level zones. ZMA is a trial and error approach, where several training datasets using high water level data are tested to find the best training dataset for forecasting high water level events. The advantage of ZMA is that relevant knowledge of water level patterns in historical records is used. Importantly, the forecasting model developed based on ZMA successfully achieves high accuracy forecasting results at 1 to 3 h ahead and satisfactory performance results at 6 h. Seven performance measures are adopted in this study to describe the accuracy and reliability of the forecasting model developed.

Quality Control of Real-Time Water Level Data: The U.S. IOOS® QARTOD Project

2018 ◽  
Vol 52 (2) ◽  
pp. 13-17
Author(s):  
Mark Bushnell
Keyword(s):  
Quality Control ◽  
Quality Assurance ◽  
Sea Level Rise ◽  
Water Level ◽  
Real Time ◽  
Sea Level ◽  
Hampton Roads ◽  
Level Data ◽  
Water Level Data ◽  
The U.S

AbstractWithin the U.S. Integrated Ocean Observing System Program, the Quality Assurance/Quality Control of Real-Time Oceanographic Data (QARTOD) Project develops manuals that describe variable-specific quality control (QC) tests for operational use. The QARTOD's Manual for Real-Time Quality Control of Water Level Data: A Guide to Quality Control and Quality Assurance for Water Level Observations was created with broad support from entities engaged in operational observations of water levels. The process used to generate this manual and all other QARTOD manuals exemplifies the integration of “federal, state, and local government agencies as well as the private and nonprofit sectors” described by the Hampton Roads Sea Level Rise Preparedness and Resilience Intergovernmental Pilot Project.Another project that supports Hampton Roads, Virginia, sea level rise and utilizes multiple partners is the deployment of continuous global positioning system (cGPS) receivers directly on water level sensors. These cGPS installations enable the determination of absolute sea level rise and local land subsidence. Successful transition of cGPS to an operational status requires the application of real-time data QC.

Accuracy Validation of Flood Forecasting Method Based on Time Series Analysis Using Observed Water Level and Rainfall Data

2020 ◽  
Author(s):  
Naoki Koyama ◽  
Tadashi Yamada
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Water Level ◽  
First Grade ◽  
Flood Forecasting ◽  
Rainfall Data ◽  
Flood Prediction ◽  
Series Analysis ◽  
Level Data ◽  
Water Level Data

<p>The aim of this paper is to verify the accuracy of the real-time flood prediction model, using the time-series analysis. Forecast information of water level is important information that encourages residents to evacuate. Generally, flood forecasting is conducted by using runoff analysis. However, in developing countries, there are not enough hydrological data in a basin. Therefore, this study assumes where poor hydrologic data basin and evaluates it through reproducibility and prediction by using time series analysis which statistical model with the water level data and rainfall data. The model is applied to the one catchment of the upper Tone River basin, one of the first grade river in Japan. This method is possible to reproduce hydrograph, if the observation stations exist several points in the basin. And using the estimated parameters from past flood events, we can apply this method to predict the water level until the flood concentration time which the reference point and observation station. And until this time, the peak water level can be predicted with the accuracy of several 10cm. Prediction can be performed using only water level data, but by adding rainfall data, prediction can be performed for a longer time.</p>

scholarly journals Sistem Monitoring Ketersediaan Air pada Perangkat Cuci Tangan Portable berbasis IoT

2021 ◽  
Vol 9 (2) ◽  
pp. 455
Author(s):  
DADAN NUR RAMADAN ◽  
SUGONDO HADIYOSO ◽  
INDRARINI DYAH IRAWATI
Keyword(s):  
Internet Of Things ◽  
Water Level ◽  
Real Time ◽  
Junior High School ◽  
Sensor Reading ◽  
Web Information System ◽  
Level Data ◽  
Water Level Data ◽  
Cloud Database ◽  
Monitoring Devices

ABSTRAKPada studi ini diimplementasikan sebuah sistem untuk memantau ketinggian air di dalam drum secara online real-time menggunakan platform Internet of Things (IoT). Sistem ini terdiri dari sensor ultrasonik untuk estimasi ketinggian air, kemudian data tersebut dikirim ke firebase cloud database, untuk diakses oleh perangkat monitoring atau mengakses halaman website. Level air yang tersisa direpresentasikan dalam nilai persen (%). Rata-rata kesalahan pembacaan sensor adalah tidak lebih dari 2%. Delay pengiriman yang digenerate adalah 39,06 ms, sesuai dengan rekomendasi ITU-T untuk komunikasi real-time. Sistem informasi web dapat menampilkan data ketinggian air dalam bentuk numerik dan grafik. Sistem ini telah diterapkan di sekolah menengah pertama Al-Azhar kota Bandung dan diharapkan dapat diperluas penerapannya.Kata kunci: drum, ketinggian air, real-time, IoT ABSTRACTIn this study, a real-time online monitoring of the water level in the drum was implemented using the internet of things (IoT) platform. This system consists of ultrasonic sensors to estimate the water level, then the data is sent to the Firebase cloud database, to be accessed by monitoring devices or accessing a website page. Water level is represented as a percent (%). The average sensor reading error is not more than 2%. The generated delivery delay is 39.06 ms, according to ITU-T recommendations for real-time communication. The web information system can display water level data in numerical and graphic form. This system has been implemented in Al-Azhar junior high school in Bandung and it is hoped that its application can be expanded.Keywords: drums, water level, real-time, IoT

scholarly journals Real-time monitoring of water level and storage dynamics of irrigation tank using IoT

2020 ◽  
Vol 3 (1) ◽  
pp. 392-400
Author(s):  
Muthiah Krishnaveni ◽  
S. K. Praveen Kumar ◽  
E. Arul Muthusamy ◽  
J. Kowshick ◽  
K. G. Arunya
Keyword(s):  
Water Level ◽  
Real Time ◽  
Web Application ◽  
Low Cost ◽  
The Internet ◽  
Volume Curve ◽  
Level Data ◽  
Water Level Data ◽  
Irrigation Tank ◽  
Tank Water

Abstract The internet of things (IoT), an emerging technological marvel, consists of a group of physical objects such as vehicles, machines and sensors to monitor and transfer data over the internet with much less human to machine interaction. It relies on a host of technologies like application programming interfaces (API), which in turn, help the devices to get connected with the internet. Efficient irrigation tank management requires a strong database on continuous water level dynamics for irrigation decision-making. Real-time tank water level monitoring is possible through an IoT device by integrating sensors and microcontroller that can send the water level data to the cloud. Google sheet is used to store the water level data that can be viewed using a web application as well as a mobile application. The contour map of the study tank is used to develop the stage (water level) vs volume curve. The volume of water present in the tank at any time can be arrived at for any tank water level using the above curve. The developed device can provide real-time continuous water level data with low cost and simple infrastructure, thus aiding tank water management.

Predicting macrophyte states in a small temporarily open/closed estuary

2012 ◽  
Vol 63 (7) ◽  
pp. 616 ◽  
Author(s):  
T. Riddin ◽  
J. B. Adams
Keyword(s):  
Salt Marsh ◽  
Water Level ◽  
Submerged Macrophytes ◽  
High Water ◽  
Water Levels ◽  
Level Data ◽  
Water Level Data ◽  
Present Distribution ◽  
Macrophyte Habitats

Temporarily open/closed estuaries (TOCEs) shift between abiotic states associated with mouth status. The aim of this study was to assess whether macrophyte states could be identified based on the dominant cover abundance of different species representative of specific habitats. A 5-year dataset of monthly macrophyte cover was assessed for the East Kleinemonde Estuary in South Africa. Three macrophyte states were identified: namely open and tidal (predominantly intertidal salt marsh); closed and low water level (predominantly salt marsh); and closed and high water level (with submerged macrophytes). The threshold water level for the change from salt marsh to submerged macrophytes was 1.6 m above mean sea level (amsl). A fourth state where macroalgae were dominant was identified for high salinity conditions (above 30 PSU). It was then possible to examine simulated water level data for different inflow scenarios to determine how often these macrophyte states occurred. Available macrophyte habitat was also calculated for different water levels using a spatial model written in Modelbuilder (ArcGIS 9.3.1). Both methods used to predict available macrophyte habitats are rapid, requiring only information on the elevation range of the main habitats, as well as present distribution and bathymetric maps. These predictive techniques are useful in the determination of the ecological water requirements of small estuaries.

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