Download EnviroInfo 2002: Hydroinformatic Web Application and Web Service

EnviroInfo 2002 (Wien)
Environmental Communication in the Information Society - Proceedings of the 16th Conference
Copyright © IGU/ISEP, Wien 2002. ISBN: 3-9500036-7-3
Hydroinformatic Web Application and Web Service
for Real-time Water Level Presentation and
Short-term Prediction
Bunchingiv Bazartseren, Gerald Hildebrandt, K.-Peter Holz1
Abstract
Modern urbanization tends to cause a fast response time between a heavy precipitation
and consequent runoff processes in a river basin. Therefore, it is even more essential
nowadays to enable citizens to have a rapid and flexible access to information on the
prevention or restoration measures in cases of flood, on the basis of the cutting edge
advances of the Information and Communication Technology (ICT). The paper contains
a description of a real-time Web application and service for water level observation,
processing, presentation and a short term prediction of a river water level in the area of
interest. The Artificial Neural Networks (ANN) are implemented for cost-effective water
level prediction for a short horizon. On the whole, the Web applications and services
should form a part of a publicly accessible Web based flood crisis management system.
1.
Introduction
A prediction of high water situation is one of the most essential hydrological tasks
for a river basin management and is mainly performed by means of traditional
conceptual and deterministic models using predicted precipitation. Due to ever
increasing urbanization and the consequential short hydrological response in the
river basin, the information on the prevention or restoration measures in case of
flood ought to be even faster and flexibly accessible to general public.
In this study, it was attempted to develop a Web-application and service for water
level observation, processing, presentation and short term prediction as a part of the
flood disaster management system. The study area forms a part of Oder River, east
of Germany. The water level presentations, as well as the prediction models are
implemented as Web application using Java Servlet technology. A cost-effective and
rapid-responding on-line tool by the Artificial Neural Network (ANN) models has
been implemented at two different cities along the Oder River. The current flood
1
Lehrstuhl für Bauinformatik, BTU Cottbus, Universitätsplatz 3-4, 03044 Cottbus, Germany
www.bauinf.tu-cottbus.de
605
disaster management system is being developed within the framework of the
European Commission funded project OSIRIS (Operational Solutions for the
management of Inundation Risks in the Information Society). The sections 2 and 3 of
the paper describe the study area, applied methodology and technology. The section
4 deals with the actual implementation of Web application and service, followed by
the conclusion.
2.
Study area and data
The area under investigation is the Oder River on the eastern border of Germany.
The sixth biggest confluence to the Baltic Sea, Oder is a trans-boundary river. The
water level observations used for a real-time presentation and prediction for cities of
Frankfurt/Oder and Schwedt are allocated real-time at a sampling interval of 15 min
from a public data server on the Internet, provided by a relevant authority and stored
into a local database. To predict the water level at two biggest cities in the study
area, the observations from two consecutive gauging stations located 11 to 30 km
upstream have been used, considering the travel time. For an off-line setup of neural
networks, approximately one year of observation has been used.
3.
Methodology and techniques
The Artificial Neural Networks (ANN) are applied for water level prediction. The
water level presentations, as well as the prediction models are implemented as Web
application using the Java Servlet technology. The presentation of the collected and
predicted water level data is implemented in Scalable Vector Graphics (SVG). The
Simple Object Access Protocol (SOAP) has been used to deploy the water level
prediction as a Web service.
3.1
Web application and Web service techniques
3.1.1 Java Web Services
The Java Web Services Developer Pack (JWSDP) is applied as an integrated toolkit
that in conjunction with the Java platform allows building, testing and deploying
XML applications, Web applications, and Web services. The Java WSDP provides
Java standard implementations of existing key Web services standards including
WSDL and SOAP, as well as important Java standard implementations for Web
application development. These Java standards allow sending and receiving SOAP
messages, and quickly build and deploy Web applications based on the latest
Servlet/JSP specification. The Apache Tomcat Servlet container is used as official
reference implementation for the Java Servlet technology.
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3.1.2 Scalable Vector Graphics (SVG)
SVG is the description of a two-dimensional vector graphic as an XML application.
Any program such as a Web browser that recognizes XML can display the image
using the information provided in the SVG format. Vector graphics is the expression
of an image using mathematical statements rather than bit-pattern description.
Scalable emphasizes that vector graphic images can easily be made scalable. Thus,
the SVG format enables the viewing of an image on a computer display of any size
and resolution, whether a tiny LCD screen in a cell phone or a large CRT display in a
workstation.
3.1.3 Simple Object Access Protocol (SOAP)
The primary use of SOAP is for different programs, possibly written in different
languages and running on different platforms, to communicate with each other.
SOAP is a Remote Procedure Call (RPC) protocol that uses standard Internet
protocols for transport - either HTTP for synchronous calls or SMTP for
asynchronous calls. SOAP uses XML for the envelope (i.e. the format of data
transmitted). Since Web protocols are installed and available for use by all major
operating system platforms, HTTP and SOAP provide an already at-hand solution to
link disparate systems within and external to a corporate network. SOAP specifies
exactly how to encode a HTTP header and an XML file so that an application in one
computer can call an application in another computer and pass it information. It also
specifies how the called program can return a response.
3.2
Prediction technique - Artificial neural networks
The ANN is a set of computational units, which aimed to emulate the learning
behavior of a human brain. So called Multi-Layer Perceptron (MLP) networks with
one hidden layer are applied in this study. Each layer consists of artificial neuron
units. In the hidden and output layer of the MLP, the weighted sum of the input
vector is passed to linear or non-linear activation function to get an output from a
node. Upon optimizing certain weight or connection strength between those neurons
to the minimum error between the target and computed values, the ANN can learn or
generalize the input/output relations on the basis of the existing observation data set.
Thus, accuracy of the ANN solution highly depends on the quality and quantity of
data set. In the current study, the ANN models have been trained to provide the
predicted water level on the basis of the hydrological conditions upstream.
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4.
Water level presentation & prediction
4.1
Off-line prediction of water level
Carefully elaborated off-line study on water level prediction at the cities of
Frankfurt/Oder and Schwedt by the ANN models resulted in a satisfactory
performance for a short prediction horizon. The inputs to the prediction models for
both locations are water level observations at two consecutive gauging stations
upstream. The travel time has been taken into account considering various prediction
horizons. The performance indices for the prediction models are the Root Mean
Squared Error (RMSE). The prediction models have resulted in a high accuracy for
different lead time (see summary in Table 1).
Table 1. The summary of prediction performance
Frankfurt(O)
Schwedt
training
0.981
0.684
t+1
verification
2.765
1.015
training
1.235
0.580
t+6
verification
3.149
1.005
t+12
training verification
2.274
4.388
0.713
2.273
The ANN models trained off-line predict the water stage on the basis of easily
available information, which are only hydrological conditions upstream. The on-line
water level predictor can be used as an alternative or complement to conventional
modeling approaches. The disadvantage of such a prediction approach is that the
ANN models need to be occasionally trained over, whenever the new extreme values
of water stage are observed.
4.2
Web application
A real-time dynamic and interactive Web application for water level presentation has
been developed with functionalities for data collection from a public data server
(www.elwis.de), saving into a local database, processing the user’s requests and
presenting it in a tabular and graphical view (www.ist-osiris.org:8092/op). Upon the
user’s request on gauging station, time interval and time slice for presentation, the
Java Servlet generates simultaneously a HTML page, which produces a tabular and
SVG view of the observed water level. The water level observation can be requested
from 1 hour up to 1 year for presentation (see Figure 1).
The trained ANN models are implemented as an on-line water level predictor and
integrated into a real-time water level presentation, from which the inputs to ANN
model are allocated. The water level presentation gives user a possibility to compare
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the predicted and observed values in the past for different prediction horizon and
period, as well. The water level prediction is made for 1 to 12 hours of time span.
Since, the implementation was not aimed the prediction models to be trained or setup
on-line, the neural network models have to be trained over from time to time, when
new extreme values are observed.
Fig. 1: Water level presentation & prediction, Web application
4.3
Web service
The Web application for waterlevel presentation & prediction became a Web service
by adding a SOAP interface to the existing application. Writing an RPC based SOAP
service is a very trivial undertaking by either creating a new code artifact in ones
preferred programming language or just using existing code, as done for OSIRIS.
The code artifact does not have to know anything about SOAP, as either a method or
a script function that exists has to be exposed within the artifact. For the waterlevel
presentation & prediction application an existing Java class, that has a method called
getWaterLevelPrediction(), just has been exposed as a SOAP service on a RPC
router.
The Web Service urn:OSIRIS-ICMS-WaterLevelPrediction simulates a
waterlevel prediction at the gauging stations Frankfurt/Oder and Schwedt for a
leadtime of 1, 6 or 12 hours on the basis of the implemented ANN models. The
exposed method getWaterLevelPrediction() expects the name of the gauging station
and the leadtime as parameter and returns the predicted waterlevel (see Figure 2).
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The browser based interface of the Web application and the corresponding Java
Servlet allows the method invocation only by a manual input. With the added SOAP
interfaces it is possible to invoke the exposed method directly from any other
application written in different languages and running on different platforms.
Fig. 2: Water level prediction, Web service summary and generic SOAP client
5. Conclusions
There is a need for a rapidly accessible on-line system to help general public, as well
as the decision makers to plan emergency and restoration measures during high water
situations. A Web application for a real-time water level presentation was developed
as a part of a flood disaster management system. The system should enable an
average citizen with an Internet access to monitor the flooding events likely to occur
in their area of interest and take the means of prevention measures suggested by the
relevant authorities. A cost-effective and rapid-responding on-line tool by the ANN
models has been implemented for water level prediction for a short horizon and
integrated into the above Web application and service. The prediction based only on
hydrological conditions upstream has produced a satisfactory accuracy. However,
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the data from further upstream are needed to enable predictions for a longer time
span.
It is expected that an increased use of Web services will be seen in the near future.
Web services will be a valuable solution to use a variety of system environments and
languages. SOAP will be used as glue in connecting a Web service with the
application calling it. The SOAP can solve most inter-operability problems
connected with the Web services. It provides a way of working at the same layer
with different technologies and alleviates maintenance and communication.
Moreover, it enables to potentially reach everybody with a device that can receive
Web services, such as a Web browser or a cell phone.
Bibliography
Apache Software Foundation: Apache XML Project, Apache SOAP implementation,
xml.apache.org/soap
Apache Software Foundation: Apache XML Project, Batik - Java based toolkit for Scalable
Vector Graphics (SVG), xml.apache.org/batik
Apache Software Foundation: Jakarta Project, Apache Tomcat 4 Servlet/JSP container,
jakarta.apache.org/tomcat
Bazartseren, B., Holz, K.P. (2001): Flood prediction using neural networks and neuro-fuzzy
approach, Proceedings of the 7th International Conference on EANN, Cagliary, Italy
pp.70-78
Bundesanstalt fuer Gewaesserkunde: Elektronisches Wasserstrassen-Informationssystem
ELWIS, www.elwis.de (in German)
Erlich, M.: Operational Solutions for the management of Inundation Risks in the Information
Society (OSIRIS), www.ist-osiris.org
Haykin, S. (1999): Neural networks: A comprehensive foundation, 2nd edition, Prentice Hall
New Jersey
Bazartseren, B., Hildebrandt, G., and Schoenefeldt, K.: OSIRIS Demonstrator for
Frankfurt/Oder, www.ist-osiris.org/ffoder
Bazartseren, B., Hildebrandt, G.: OSIRIS-ICMS Waterlevel presentation & prediction
(Web application), www.ist-osiris.org:8092/op
Hildebrandt, G.: OSIRIS-ICMS Waterlevel Prediction (Web service),
www.ist-osiris.org/ffoder/op/opSOAP.html
Hildebrandt, G.: Web Services Management Platform, dcms.bauinf.tu-cottbus.de/wsm
Hildebrandt, G.: SOAP RPC Router, dcms.bauinf.tu-cottbus.de:8090/soap/servlet/rpcrouter
Sun Microsystems: Java Technology & Web Services, java.sun.com/webservices
Wilson, G., Khondker, M.-H., (2000): Data selection for a flood forecasting neural network,
in: Proc. 4th ICHI’00, Iowa, IA, USA
World Wide Web Consortium (W3C): Scalable Vector Graphics (SVG),
www.w3.org/Graphics/SVG
World Wide Web Consortium (W3C): Simple Object Access Protocol (SOAP),
www.w3.org/TR/SOAP
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Copyright © IGU/ISEP, Wien 2002. ISBN: 3-9500036-7-3