scholarly journals Image Processing-based Flood Detection for Online Flood Early Warning System

2017 ◽  
Author(s):  
Yudha Maulana Akbar ◽  
Akhmad Musafa ◽  
Indra Riyanto
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Water Level ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Raspberry Pi ◽  
Edge Points

This paper will discuss the design of an online flood early warning system. This system will use a single board computer Raspberry-PI as the main controller, and a webcam to capture image. This system is integrated to Twitter. In hardware section, Raspberry-PI has main tasks as an image processor and do an update request to Twitter. In software section, OpenCV will be used as Image Processing software. Some method which used in this system is: 1) Region of Interest: this method is to create a portion of an image that you want to filter or perform some other operation on. Brightness and contrast: these methos is used in order to get brighter and better image before next process. 3) Grayscale and threshold: this method is to create an object segmentation. Otsu-thresholding is used on this step. 4) Edge detection: edge detection algorithm to find edge points on a (relatively) horizontal water line and point of dam’s height. By using these methods, the system can read and monitor the water level in the dam. If the water level exceeds the specified threshold, this system will generate an early warning of impending floods by doing update time line (text and image) of water level conditions to Twitter. The public will get the information if they following early warning system’s Twitter. Simulation test results show the system can read water level with an accuracy nearing 96%.

scholarly journals Internet-Based Flood Detection System (Iot) and Telegram Messenger Using Mcu Node and Water Level Sensor

2020 ◽  
Vol 4 (1) ◽  
pp. 230-235
Author(s):  
Novianda Nanda Nanda ◽  
Rizalul Akram ◽  
Liza Fitria
Keyword(s):  
Water Level ◽  
Early Warning ◽  
High Intensity ◽  
Rainy Season ◽  
Early Warning System ◽  
Detection System ◽  
Warning System ◽  
Level Sensor ◽  
Flood Detection ◽  
Internet Of Thing

During the rainy season, several regions in Indonesia experienced floods even to the capital of Indonesia also flooded. Some of the causes are the high intensity of continuous rain, clogged or non-smooth drainage, high tides to accommodate the flow of water from rivers, other causes such as forest destruction, shallow and full of garbage and other causes. Every flood disaster comes, often harming the residents who experience it. The late anticipation from the community and the absence of an early warning system or information that indicates that there will be a flood so that the community is not prepared to face floods that cause a lot of losses. Therefore it is necessary to have a detection system to provide early warning if floods will occur, this is very important to prevent material losses from flooded residents. From this problem the researchers designed an internet-based flood detection System of Things (IoT). This tool can later be controlled via a smartphone remotely and can send messages Telegram messenger to citizens if the detector detects a flood will occur.Keywords: Flooding, Smartphone, Telegram messenger, Internet of Thing (IoT).

scholarly journals GPS water level measurements for Indonesia's Tsunami Early Warning System

2011 ◽  
Vol 11 (3) ◽  
pp. 741-749 ◽  
Author(s):  
T. Schöne ◽  
W. Pandoe ◽  
I. Mudita ◽  
S. Roemer ◽  
J. Illigner ◽  
...  
Keyword(s):  
Water Level ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Deep Ocean ◽  
Ocean Bottom ◽  
Tsunami Early Warning ◽  
Tsunami Waves ◽  
Gps Technology ◽  
Water Level Measurements

Abstract. On Boxing Day 2004, a severe tsunami was generated by a strong earthquake in Northern Sumatra causing a large number of casualties. At this time, neither an offshore buoy network was in place to measure tsunami waves, nor a system to disseminate tsunami warnings to local governmental entities. Since then, buoys have been developed by Indonesia and Germany, complemented by NOAA's Deep-ocean Assessment and Reporting of Tsunamis (DART) buoys, and have been moored offshore Sumatra and Java. The suite of sensors for offshore tsunami detection in Indonesia has been advanced by adding GPS technology for water level measurements. The usage of GPS buoys in tsunami warning systems is a relatively new approach. The concept of the German Indonesian Tsunami Early Warning System (GITEWS) (Rudloff et al., 2009) combines GPS technology and ocean bottom pressure (OBP) measurements. Especially for near-field installations where the seismic noise may deteriorate the OBP data, GPS-derived sea level heights provide additional information. The GPS buoy technology is precise enough to detect medium to large tsunamis of amplitudes larger than 10 cm. The analysis presented here suggests that for about 68% of the time, tsunamis larger than 5 cm may be detectable.

scholarly journals Early warning flood detector adopting camera by Sobel Canny edge detection algorithm method

2021 ◽  
Vol 22 (3) ◽  
pp. 1796
Author(s):  
Satryo B. Utomo ◽  
Januar Fery Irawan ◽  
Rizqi Renafasih Alinra
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Water Level ◽  
Real Time ◽  
Early Warning ◽  
Detection System ◽  
Water Levels ◽  
Level Gauge ◽  
Canny Edge Detection ◽  
Canny Edge

Early warning of floods is an essential part of disaster management. Various automatic detectors have been developed in flood mitigation, including cameras. But reliability and accuracy have not been improved. Besides, the use of monitoring devices has been employed to monitor water levels in various water building facilities. The early warning flood detector was carried out with a sensor camera using an orange ball that floats near the water level gauge in a bounding box. This approach uses the integration of computer vision and image processing, namely digital image processing techniques, with Sobel Canny edge detection (SCED) algorithms to detect quickly and accurately water levels in real-time. After the water level is measured, a flood detection process is carried out based on the specified water level. According to the results of experiments in the laboratory, it has been shown that the proposed approach can detect objects accurately and fast in real-time. Besides, from the water level detection experiment, good results were obtained. Therefore, the object detection system and water level can be used as an efficient and accurate early detection system for flood disasters.

scholarly journals Design and Implementation of Real-Time Flood Early Warning System (FEWS) Based on IoT Blynk Application

ELKHA ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 113
Author(s):  
Hasbi Nur Prasetyo Wisudawan
Keyword(s):  
Water Level ◽  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Disaster Risk ◽  
Disaster Mitigation ◽  
Average Temperature ◽  
Flood Warning ◽  
Flood Warning System

Disaster occurrence in Indonesia needs attention and role from all parties including the community to reduce the risks.  Disaster mitigation is one of the ways to reduce the disaster risk through awareness, capacity building, and the development of physical facilities, for example by applying disaster mitigation technology (early warning system, EWS). EWS is one of the effective methods to minimize losses due to disasters by providing warning based on certain parameters for disasters which usually occur such as floods. This research promotes a real-time IoT-based EWS flood warning system (Flood Early Warning System, FEWS) using Arduino and Blynk as well as Global System for Mobile Communication network (GSM) as the communication medium. The steps for implementing FEWS system in real locations are also discussed in this paper. Parameters such as water level, temperature, and humidity as well as rain conditions that are read by the EWS sensor can be accessed in real-time by using android based Blynk application that has been created. The result of the measurement of average temperature, humidity, and water level were 28.6 oC, 63.7 %, and 54.5 cm. Based on this analysis, the parameters indicated that the water level is in normal condition and there are no signs indicating that there will be flooding in the 30 days observation.  Based on the data collected by the sensor, FEWS can report four conditions, namely Normal, Waspada Banjir (Advisory), Siaga Banjir (Watch), and Awas Banjir (Warning) that will be sent immediately to the Blynk FEWS application user that has been created.

scholarly journals Utilization of the IoT-based early warning system (EWS) for early detection of floods in Setu District, South Tangerang City

2021 ◽  
Vol 6 (8) ◽  
pp. 1414-1419
Author(s):  
Wahyu Sejati ◽  
Ning Adiasih ◽  
Tjhwa Endang Djuana
Keyword(s):  
Early Detection ◽  
Water Level ◽  
Community Service ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Ultrasonic Sensor ◽  
Detection Tool ◽  
Flood Detection

Cisadane River is the largest river whose overflow often causes flooding in several locations in South Tangerang City. One of them is located in Pesona Serpong Housing, Setu District, South Tangerang City. The Cisadane Environmental Echo Community (GEMALA) is a community that cares about the sustainability of the Cisadane River. This community service aims to improve understanding of river maintenance and socialize the IoT-based Early Warning System (EWS) tool to the GEMALA community as an early flood detection tool. The method used is to use an ultrasonic sensor HC-SR04 which will measure the water level of the river and will send a signal via the Telegram messaging application. At the end of this activity, an IoT-based Early Warning System (EWS) tool was produced that could be utilized by the GEMALA community as an early flood detection tool.

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.

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