scholarly journals Decision support system for predicting tsunami characteristics along coastline areas based on database modelling development

2010 ◽  
Vol 13 (1) ◽  
pp. 96-109 ◽  
Author(s):  
Iwan K. Hadihardaja ◽  
Hamzah Latief ◽  
Iyan E. Mulia
Keyword(s):  
Neural Network ◽  
Numerical Modelling ◽  
Early Warning ◽  
Early Warning System ◽  
Numerical Models ◽  
Near Field ◽  
Warning System ◽  
Database System ◽  
Tsunami Early Warning ◽  
Neural Network Learning

Tsunamis are extraordinary occurrences that are difficult to identify; most of the incidents have no recorded predictions and tsunamis are generally infrequent events with poor data acquisition. The development of a tsunami database system has become important for improving the management of information with regard to a tsunami early warning system for vulnerable communities along coastline areas. Numerical modelling is usually employed to simulate the wave height and travel time of a wave arriving at a coastline area. However, numerical modelling for tsunami prediction is too time-consuming to be useful as an early warning system for the mitigation of tsunami-related damage and loss. Therefore, this model was used to develop a tsunami database system, based on hypothetical data, in order to develop a recognition pattern for a neural network learning process that will improve the speed and accuracy of tsunami prediction. To improve the accuracy of numerical modelling and observation, an adjustment was established for an advanced training process which used a generalised regression neural network. In other words, the training and testing datasets were obtained by correcting near-field tsunami numerical models from hypothetical earthquakes. The case study was performed on part of the Southern Pangandaran coastline in West Java, Indonesia.

scholarly journals Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Ivana Sušanj ◽  
Nevenka Ožanić ◽  
Ivan Marović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Small Scale ◽  
Ann Model ◽  
Data Value ◽  
The Mean ◽  
Artificial Neural

In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN) as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error.

scholarly journals Near real-time GPS applications for tsunami early warning systems

2010 ◽  
Vol 10 (2) ◽  
pp. 181-189 ◽  
Author(s):  
C. Falck ◽  
M. Ramatschi ◽  
C. Subarya ◽  
M. Bartsch ◽  
A. Merx ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Tide Gauge ◽  
Warning System ◽  
Early Warning Systems ◽  
Time Data ◽  
Essential Information ◽  
Tsunami Early Warning ◽  
Land Mass

Abstract. GPS (Global Positioning System) technology is widely used for positioning applications. Many of them have high requirements with respect to precision, reliability or fast product delivery, but usually not all at the same time as it is the case for early warning applications. The tasks for the GPS-based components within the GITEWS project (German Indonesian Tsunami Early Warning System, Rudloff et al., 2009) are to support the determination of sea levels (measured onshore and offshore) and to detect co-seismic land mass displacements with the lowest possible latency (design goal: first reliable results after 5 min). The completed system was designed to fulfil these tasks in near real-time, rather than for scientific research requirements. The obtained data products (movements of GPS antennas) are supporting the warning process in different ways. The measurements from GPS instruments on buoys allow the earliest possible detection or confirmation of tsunami waves on the ocean. Onshore GPS measurements are made collocated with tide gauges or seismological stations and give information about co-seismic land mass movements as recorded, e.g., during the great Sumatra-Andaman earthquake of 2004 (Subarya et al., 2006). This information is important to separate tsunami-caused sea height movements from apparent sea height changes at tide gauge locations (sensor station movement) and also as additional information about earthquakes' mechanisms, as this is an essential information to predict a tsunami (Sobolev et al., 2007). This article gives an end-to-end overview of the GITEWS GPS-component system, from the GPS sensors (GPS receiver with GPS antenna and auxiliary systems, either onshore or offshore) to the early warning centre displays. We describe how the GPS sensors have been installed, how they are operated and the methods used to collect, transfer and process the GPS data in near real-time. This includes the sensor system design, the communication system layout with real-time data streaming, the data processing strategy and the final products of the GPS-based early warning system components.

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.

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