Simulation of Flood Water Level Early Warning System Using Combination Forecasting Model

2018 ◽  
pp. 207-217
Author(s):  
Kristine Bernadette Barrameda ◽  
Sang Hoon Lee ◽  
Su-Yeon Kim
Keyword(s):  
Water Level ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Forecasting Model ◽  
Flood Water ◽  
Combination Forecasting ◽  
Combination Forecasting Model

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.

Early-Warning Model for TECC Based on Gray Neural Network - Taking Laolongtou Scenic Area as an Example

2013 ◽  
Vol 397-400 ◽  
pp. 2435-2438
Author(s):  
Xiu Ping Yang ◽  
Er Chao Li
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Historical Data ◽  
Fuzzy Inference ◽  
Warning System ◽  
Forecasting Model ◽  
Error Signal ◽  
Single Feature ◽  
Warning Model

Based on fuzzy inference and gray neural network, indexes of early-warning system of carrying capacity in scenic spots is established and extract fuzzy rules based on historical data, simulate the early-warning system based on fuzzy inference, gray forecasting model is built for single feature index respectively, add a compensated error based on neural network. The prediction value equals to the output value of grey neural network model plus the compensated error signal. At last, takes Laolongtou scenic area as an example.

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.

Combination Forecasting of Power Load Based on Polynomial Trend Extrapolation and ARIMA Model

2012 ◽  
Vol 546-547 ◽  
pp. 357-362 ◽  
Author(s):  
Jing Jing Xia ◽  
Huan Qi ◽  
Zi Qi Wang
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Arima Model ◽  
Forecast Accuracy ◽  
Electricity Consumption ◽  
Warning System ◽  
Polynomial Trend ◽  
Combination Forecasting ◽  
Power Load ◽  
Fuel Storage

The power load forecasting is the core component of the early warning system for fuel storage margin in power system and an important guarantee to the early warning function to achieve. In this paper, one province's 2008 load data is chosen to forecast the electricity consumption in 2009. Firstly the two forecasting models of polynomial trend extrapolation and ARIMA are established, and then the combined model of them is used to forecast, that is, the final result is equal to the sum of the trend value by polynomial extrapolation and the non-trend D-value’s forecasting result by ARIMA. The results indicate that the combination forecasting make the forecast accuracy significantly improved and ensure the effective operation of the early warning system.

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.

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