A watershed based debris flow early warning system using sensor web enabling techniques in heterogeneous environments

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 5, NO. 6, DECEMBER 2012 1729

A Watershed-Based Debris Flow Early WarningSystem Using Sensor Web Enabling Techniques in

Heterogeneous EnvironmentsJen-Cheng Chiu, Chyi-Ren Dow, Member, IEEE, Cheng-Min Lin, Member, IEEE, Jyh-Horng Lin, and

Hsueh-Wei Hsieh

Abstract—Debris flow disasters have increased in Taiwan due tovarious environmental factors. These disasters often bring a lot ofrock and mud, causing a threat to the lives and properties of resi-dents in the affected areas. The weather is changeable due to moreand more extreme rainfall events. A monitoring system is neededto provide early-warning of debris flow disasters to reduce the lossof life and property. The number of installed precipitation sta-tions is not adequate for the current early-warning system.Rainfallpatterns are greatly affected by the variation in the topography.Therefore, the current system cannot fully integrate basin-widerainfall data and lacks information on spatial dependency betweenrainfall stations. This paper proposes a watershed-based debrisflow early-warning system that applies the OGC SWE standardsto design its architecture. The standardized data exchange mech-anism is used to integrate and share heterogeneous monitoring re-sources. A hierarchical architecture is proposed to build a widerange of precipitation stations. The system presents high densitydebris-flow-prone area monitoring. We propose dependency ag-gregation and SWE integration schemes that enable the system tocollect data from upriver under dependency relationship of debris-flow-prone streams and achieve automated early-warning of debrisflows. We use the SWE open source provided by 52North to im-plement the proposed watershed-based debris flow early-warningsystem. We develop a simulator using real rainfall data in Taiwanto compare to the current system. The experimental results demon-strate that our system can improve the sensing data problem andefficiently advance the warnings issue time.

Index Terms—Basin-wide, debris flow, heterogeneous environ-ments, OGC SWE.

I. INTRODUCTION

D EBRIS flow is a natural phenomenon involving a massiveamount of unconsolidated debris combined with water,

flowing downhill by gravity that tends to follow steep mountainchannels or riverbeds [7]. When a debris flow disaster occurs, it

Manuscript received June 01, 2011; revised August 29, 2011; acceptedNovember 24, 2011. Date of publication February 10, 2012; date of currentversion December 28, 2012. This work was supported by the National ScienceCouncil of Taiwan under NSC Grant 99-2625-M-035-006.J.-C. Chiu is with the Department of Information Engineering and Computer

Science, Feng Chia University, Taichung 407, Taiwan.C.-R. Dow is with the Department of Information Engineering and Computer

Science, Feng Chia University, Taichung 407, Taiwan (corresponding author,e-mail: [email protected]).C.-M. Lin is with the Department of Computer and Communication Engi-

neering, Nan Kai University of Technology, Nan Tou 542, Taiwan.J.-H. Lin is with the Department of Electrical Engineering, Nan Kai Univer-

sity of Technology, Nan Tou 542, Taiwan.H.-W. Hsieh is with the Department of Information Engineering and Com-

puter Science, Feng Chia University, Taichung 407, Taiwan.Digital Object Identifier 10.1109/JSTARS.2011.2181826

can carry a large amount of debris, washing off houses, gardensand causing a serious threat to the lives and properties of resi-dents living nearby the debris-flow-prone area. Three triggeringvariables are considered for a debris flow, a great quantity ofsoil and rocks, steep slopes and large amounts of water [6]. InTaiwan, rainfall is abundant and concentrated, so that whenevera heavy rain occurs or during a typhoon, rainfall often triggersdebris flows in the mountain areas [4], [27].In recent years, the government is embarking on a debris

flow system to monitor dangerous debris flow areas [13], [28].The system consists of statistical analysis information from de-bris flow observation stations [38], precipitation stations [36],radar and satellite stations [18], [19] in all regions. However,the radar reflectivity data only presents cloud water content. Itcannot be directly converted into the amount of rainfall. Be-sides, due to steep terrain and small land in Taiwan, the rain-fall forecast by radar is not so accurate than that of precipitationstations. Hence, we use rainfall data from precipitation stations.Furthermore, it is difficult to build a wide range of fixed precip-itation stations because of the steep topography in mountainousregions of Taiwan. Currently, the debris flowmonitoring systemin Taiwan consists of 153 precipitation stations and 20 debrisflow observation stations. The number of precipitation stationsis not enough to collect sufficient rainfall data to forecast thehappening of debris flow.Currently various monitoring systems [9] do not have

sensing data interoperability and the acquired information isstored in different servers. The different agencies have set uptheir monitoring instruments in the mountain areas to collectsurrounding monitoring information. However, these dedicatedsystems are often independent and non-relative. The currentmonitoring system is a semi-automatic system [21]. If thesystem needs to issue a warning, it must be manually operatedthrough decision-makers. Such an operation model cannot meetthe requirements of issuing warnings in time and simplifyingthe observation work. Debris flows may move very rapidlyand they can destroy properties and take lives suddenly andunexpectedly. A warning system is required to issue warningsin time and automatically to reduce the loss of lives and prop-erties. A timely early-warning message allows the residentsin the debris flow area to evacuate opportunely and safely.A number of precipitation stations are too far away from de-bris-flow-prone streams. When it rains heavily in the upstreammountain areas, the downstream areas may suffer a great waterdisaster such as in the Xiaolin village [37]. Unfortunately, thecurrent precipitation stations are unable to collect upstream

1939-1404/$31.00 © 2012 IEEE

1730 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 5, NO. 6, DECEMBER 2012

rainfall data precisely and the current debris flows monitoringsystem can therefore not react promptly.Considering the problems mentioned above, Wang et al. [24]

also discussed related challenges for Sensor Web. They pro-posed a hierarchical, automatic and basin-widewatershed-baseddebris flow early-warning system to solve sensor data collec-tion, integration and usability problems. Our system includes ahierarchical architecture for building a wide range of precip-itation stations and an OGC SWE (Sensor Web Enablement)standard [12] data exchange mechanism to solve the interoper-ability problem. SOS (Sensor Observation Service) [31], SAS(Sensor Alert Service) [33] and SPS (Sensor Planning Service)[32] are integrated to achieve automatic early-warning features.An aggregation scheme is proposed to establish relationship ofupstream and downstream and collect basin-wide rainfall data.The rest of this paper is organized as follows. Section II

introduces some related work and background on OGC SWEand rainfall-based debris flow occurrence models. Section IIIpresents our watershed-based debris flow early-warning systemarchitecture. Dependency aggregation and SWE integrationschemes are described in Section IV. System implementationand the prototype are described in Sections V and VI, respec-tively. The experiments and simulation results are describedin Section VII. Conclusions and some issues involving futurework are discussed in Section VIII.

II. RELATED WORK

In this section, we introduce some related studies in the samearea of research. First, we introduce the background of OGCSWE and how it is used to incorporate heterogeneous WSN(Wireless Sensor Network). Second, the current rainfall-baseddebris flow occurrence model is introduced.OGC SWE presents a reusable, scalable, extensible and in-

teroperable service oriented Sensor Web architecture [2], [3],[15], composed of a set of specifications, including SensorML(SensorModel Language) [29], O&M (Observation&Measure-ment) [30], SOS, SPS and SAS. The following describes thespecifications defined by OGC SWE.SensorML provides standard models and an XML (eXten-

sible Markup Language) encoding for describing any process,including the measurement process using sensors, and instruc-tions for deriving higher-level information based on observa-tion. It brings a provider-centric view of the information in aSensor Web system, complemented by an O&M view. The pur-pose of the O&M is to alleviate the need for sensor-specific for-mats for describing the data retrieved from the sensor networks.The SOS is responsible for returning observational data encodedin the O&M specification, which can be real-time data retrievedfrom a sensor network or archived data. SOS is a service re-sponsible for forwarding requests to the sensor network and re-trieving the recorded observational results. It acts as an interme-diary between the client and real time or archived sensor obser-vation data. SPS is responsible for providing a high level plan-ning, scheduling, tasking, collecting, processing, and archivingof requests for all services. A SWE client can submit a Sen-sorML encoded plan to the SPS, the plan must contain the ob-servation request, location of the sensors, duration of the re-quest and any other relevant metadata or post-measurement pro-

cessing requirements. SAS specification provides an interfacefor sensor nodes to advertise and publish alerts. Users can re-ceive alerts, when users have subscribed services and matchingspecific criteria.Several researches adopt the OGC SWE technology to solve

problems of heterogeneous data integration in debris flow mon-itoring architecture. Lee et al. [20] proposed an innovated SOSwith Web-based and GIS-based architecture, which is namedWSN Application Service Platform (WASP). The main goal ofWASP is to provide an easy and straightforward data presenta-tion for users. Users can query and obtain the valuable informa-tion and realize the meanings of these data through Web-basedinterfaces defined in SWE. Jan et al. [10] proposed a service-based system prototype for a debris flow early-warning systemthat adopted a centralized control flow approach to implementthe service-based architecture components. Chung et al. [5] pro-posed an open standard based debris flow monitoring architec-ture following the Service Oriented Architecture (SOA) para-digm. In these research efforts, there is a lack of cited work ad-dressing the debris flow warning in time, automatically and atthe basin-wide scale.The common rainfall parameters used in rainfall-based debris

flow occurrence models are the rainfall intensity , rainfall dura-tion , accumulated rainfall and previous accumulated rain-fall [11]. denotes accumulated rainfall during a rainfall.denotes the previous accumulated rainfall before a rainfall. Theamount of is also under a period of time such as 7 or 14 daysbased on different models. Based on the four rainfall parame-ters, debris-flow occurrence warning models can be classifiedinto five categories: model, model, model,

model and others. Shieh et al. [34] proposed rainfall inten-sity and accumulated rainfall parameter ( model) to assigncritical precipitation line. Wieczorek [25] proposed rainfall thattriggers the debris flow and rainfall duration ( model) toset up critical precipitation line. Basically, this scheme does nottake into account the influence of previous accumulated rainfall.Guzzetti et al. [8] proposed a global rainfall intensity-duration( model) threshold for the occurrence of debris flows. Janet al. [11] considered rainfall of the day, and to the accumulatedrainfall of the 14 days before the occurrence of the debris flow( model) to set up the critical precipitation line. Wilson[26] used rainfall of the day when debris flow occurs and theaverage rainfall for the whole year to set up critical precipita-tion lines. In addition, soil parameters can be used to managelandslide risk. Montrasio et al. [22] proposed SLIP (ShallowLandslides Instability Prediction) to use weight, saturation, co-hesion of soil parameters, and so on. However, the and

models are the most popular ones of the five debris-flowoccurrence warning models. The current scheme [39] uses the

model. Therefore, we use the current scheme and addparameter to set up critical precipitation lines. The detailedspecification of our model will be discussed in Section IV-C.

III. SYSTEM ARCHITECTURE

This section describes the watershed-based debris flow early-warning system architecture. The OGC SWE standard is used todesign the whole system architecture and to solve the problemof interoperability of debris flow monitoring resources. Due to

CHIU et al.: A WATERSHED-BASED DEBRIS FLOW EARLY WARNING SYSTEM USING SENSOR WEB ENABLING TECHNIQUES 1731

Fig. 1. Watershed-based debris flow early warning system architecture.

the need of collecting basin-wide rainfall data, we propose thesystem architecture shown in Fig. 1. The architecture consists ofsensing and actuation, data aggregation, SWE service and appli-cation layers. The detailed specifications of these four layers aredescribed below.The sensing and actuation layer includes entity debris flow

monitoring instruments such as a rainfall gauge, water levelsensor, geophone or trip-wire sensor [14] to monitor varioussigns before flows occur. These devices transfer sensed data toobservation stations throughwired or wireless techniques. In ad-dition, the notification system to issue debris flow warning mes-sages to local residents is also included in this layer such as localbroadcasting systems, electronic bulletin boards and short mes-sage systems. The data aggregation layer includes debris flowobservation stations and sensor network gateways. These gate-ways aggregate sensing data from various sensors at observa-tion stations as fixed, mobile and simple stations. These stationstransfer the sensed data to the debris flow monitoring systemthrough debris flow monitoring networks such as infrastructureor satellite networks. The observation stations of different de-ployment cost and at installed locations are considered in hier-archical deployment architecture as shown in Fig. 2. The fixedstations are built in downstream and debris-flow-prone streamsand are used for long-term monitoring. These stations have highimplementation cost, so the number of this kind of station is low.When rainfall reaches a certain value or in a typhoon period, mo-bile and simple stations are dispatched to aggregate monitoringdata. Mobile stations are dispatched to the places where vehi-cles can reach and simple stations are deployed where peoplecan go. The various sensors can be deployed upstream, becausethese sensors are inexpensive equipments.To achieve interoperability with various monitoring systems,

the SWE service layer is required to handle data transformation.This layer includes many standard services defined by OGCSWE. SOS collects or retrieves the data from each debris flowmonitoring station as accumulated rainfall or stream waterlevels. SPS controls debris flow monitoring instruments toassist in monitoring data collection. For instance, SPS can

Fig. 2. Cost and deployment of monitoring stations.

control the intervals between rainfall gauge samples or thefrequency of return data. We also use this service to controlvarious notification systems such as broadcasting systems.SAS offers services to make debris flow monitoring stationsannounce alert messages. For example, the debris flow mon-itoring system will send an alert through this service if therainfall intensity is more than 20 mm/hr or the water level ishigher than 40 cm. WEPS (Watershed Event Process System)is proposed in the application layer. The system is a core ofthe whole debris flow monitoring system. The system monitorsthe potential debris flow through services offered by the SWEservice layer. The WEPS monitors data collection as well asevent processing. When the warning issue condition is met, thesystem will control the notification system through SPS andissue the debris flow warning.

IV. DEPENDENCY AGGREGATION, SWE INTEGRATION ANDWARNING DECISION SCHEMES

This section introduces detailed aggregation and integratedschemes. Service aggregation schemes and basic definitions aredescribed in Section IV-A. SWE integration and warning deci-sion schemes are described in Sections IV-B and IV-C, respec-tively.

A. Dependency Aggregation Scheme

This section introduces the proposed watershed-basedsensing model. The debris flow occurrence has a closed re-lationship with hydrological and geological conditions in thecatchment area. Therefore, in order to improve the accuracyof the debris flow monitoring system, basin-wide monitoringdata collection is necessary. Because the monitoring datadistributed in different areas, we can collect those data undera dependency relationship with a debris-flow-prone stream toissue a warning after decision making. Therefore, we proposethe watershed-based sensing model.In Taiwan, most debris-flow-prone streams are formed by

many branches in upstream areas, brought together into a mainchannel. The rainfall in the upstream catchment area througheach branch converges to become the factor triggering a debris


Fig. 3. Basic definition of a stream.

flow disaster. Basin-wide monitoring can be used to effectivelyobtain the signs before the debris flow occurs.The debris-flow-prone stream is defined as a composition

of many branches in the watershed-based sensing model. Thebranch is a section between two forks in the channel. In addition,the model defines at least one debris flow monitoring station de-ployed in each branch of the stream. The debris flowmonitoringstations are equipped with debris flow monitoring instrumentsused to monitor events in each branch. Once the system estab-lishes the connected relationship, it can make observers collectinterrelated events in the stream watershed. Fig. 3 shows a dia-gram of the stream watershed.

denotes a watershed of the debris-flow-prone stream.It is an assembly of branches . Due to rainfall gauge ,water level sensor , trip-wire sensor and geophone ofdebris flow monitoring instruments deployed in each branch ofthe stream. Therefore, each branch is an assembly of variousmonitoring instruments.Moreover, the upstream-downstream relationships between

branches, can be described by the relationship between twoadjacent branches. describes the upstream-downstreambranch relationships in Fig. 3. Those branch relationshipsalso have implicit transitive relationships. We can infer otherrelationships to make use of these transitive relationships. Forinstance, we can understand that is upstream of , becauseof upstream of and upstream of .

We designed the watershed-based replanning mechanismaccording to the above stream definitions. This mechanismcan help the system to collect interrelated sensing data basedupon the dependency relationship between debris-flow-pronestreams. For instance, when the accumulated rainfall in branchis over a defined value, the system will also collect sensing

data from the correlation branches , asshown in Fig. 4.

Fig. 4. Example of watershed-based replanning mechanism.

Fig. 5. SWE integration scheme.

B. SWE Integration Scheme

Fig. 5 shows the SWE Integration Scheme. The system is di-vided into two phases, the initial phase and operating phase. Inthe initial phase, the observer can use the WEPS GUI interfaceto construct the relationships of a debris-flow-prone stream, set-ting the accumulated rainfall for the replanning mechanism andsetting rainfall conditions for issuing a warning. At the sametime, the WEPS automatically subscribes an alert for each de-bris flow monitoring station through SAS. The system then be-gins monitoring the streams that enter the operation phase. TheWEPS will receive alert messages when the debris flow mon-itoring station publishes an alert through SAS. In this phase,the replanning mechanism is executed and the WEPS collectsinterrelated sensing data based upon the dependency relation-ships through SOS. When the rainfall conditions for issuing awarning are satisfied, the WEPS triggers notification systems toissue debris flow warnings through SPS.

C. Rainfall Warning Decision Scheme

In Taiwan, debris flow disasters are mostly triggered by rain-fall. Therefore, we designed a rainfall warning decision schemeto evaluate whether to issue warning messages or not. We referto Jan et al. [11] to define the rainfall condition and draw thestate diagram as shown in Fig. 6. A circle denotes a state. Thisscheme is divided into four states. S0 denotes a sunny or cloudyday state with rainfall intensity less than 4 mm/hr. S1 denotes a


Fig. 6. State diagram of rainfall warning decision scheme.

light rainfall state with rainfall intensity exceeding 4 mm/hr andless than 30 mm/hr. S2 denotes a heavy rainfall state with rain-fall intensity exceeding 30 mm/hr. S3 denotes a warning issuestate. The arrow denotes the transition condition of each state.The transition condition from S0 to S1 holds when rainfall in-tensity equals 4 mm/hr. As soon as rain starts, the calculation ofrainfall and time begins in time. The transition condition fromS1 to S2 is when rainfall intensity exceeding 30mm/hr. The con-dition of S2 denotes re-calculating rainfall time. The transitioncondition of X4 is an accumulated rainfall equal to boundaryvalue based on different areas [39]. For example, the boundaryvalue in Taimali Township is 400 mm/hr. The transition con-dition of X5 is when accumulated rainfall equals 150 mm/hr.The transition condition of X6 is when rainfall duration equals4 hours. The current scheme uses condition X4 to issue debrisflow warnings. Our scheme adds two conditions X5 and X6 toenhance the warning issuing time.

D. Discussions of System Redundancy Problems

Even with the best system, the devices have potential failuresinWSN. Current studies proposed effective schemes in differentsystems. For example, Ammari [1] proposed stochastic k-Cov-erage scheme to ensure the quality of surveillance in WSN.The quality is ensured by using several sensors to monitor asingle place. When a sensor fails, another sensor will replace it.Similarly, if a gateway or server is broken, another gateway orserver will replace it. Regarding the data redundancy problem,the gateways are responsible for transmitting data from stationsto a server. When a gateway fails to transmits data, it can selectanother server from the server list to re-transmit data. If rainfalldata was lost, the radar data can be used as backup informationfor forecast rainfall.

V. SYSTEM IMPLEMENTATION

We used the OGC SWE standard to design the watershed-based debris flow early-warning system architecture. Using theSWE system provides uniform operations and a standard repre-sentation for sensor data that can be used by diverse WSN ap-plications. Therefore, the debris flow early-warning system canbe easily integrated and share heterogeneous debris flow moni-toring resources.We used SensorML to describe the debris flow monitoring

stations. The stations are equipped with a rainfall gauge, water

Fig. 7. Example of station description.

level sensor, geophone and trip-wire sensor debris flow moni-toring instruments. The monitoring instrument can be describedusing the component type and the debris flow monitoring sta-tion can be described using the system type which aggregatesindividual components. Fig. 7 shows a station description ex-ample. The keyword section contains terms that describe thedebris flow monitoring station as a composition of all compo-nents. The identification section is used to identify the debrisflow monitoring station. The inputs section describes the ob-served phenomena at the debris flow monitoring station. Theinputs of the debris flow monitoring station lead to four types ofoutputs that are generated by the monitoring instruments: pre-cipitation, earthquake sound, water level and debris flow.WEPS acts as a SWE client. The system subscribes to debris

flow monitoring station alerts through SAS. The subscriptionoperation is used to subscribe alerts that match specific criteria.Fig. 8 shows an example of a subscription request that describesa system that needs to receive alerts published by debris flowmonitoring stations when the rainfall intensity per hour is over20 mm or the water level is higher than 40 cm. Fig. 9 showsan example of an alert message. It describes the latitude andlongitude of debris flow monitoring stations, time of noticingthe alert and monitoring values of each monitoring instrument.The system can monitor the debris-flow-prone stream throughsubscription to alert message to WEPS.WEPS can retrieve more detailed monitoring data from

debris-flow-prone streams through SOS. The system can usethe GetObservation operation to retrieve monitoring data fromdebris flow monitoring stations. Fig. 10 shows an example of


Fig. 8. Example of SAS subscribe request.

Fig. 9. Example of SAS alert message.

Fig. 10. Example of SOS get observation request.

a GetObservation request that describes the system needed toquery 24 hours accumulated rainfall.When warning messages are necessary, WEPS will control

notification system through SPS to issue warning. The systemcan perform a DescribeTasking operation to obtain the func-tionality and controllable parameters of notification systems.SPS will return the description information about the notifica-tion systems. The notification systems can parse the informa-tion and get operation method. Fig. 11 shows an example ofthe DescribeTasking operation response that describes the SMSnotification system which supports two operation parametersincluding IssueWarning and MessageContent. In addition, theSubmit operation will deliver control instructions to the notifica-tion system. The notification system will execute correspondingfunctions according to instructions. Fig. 12 shows an exampleof the Submit request that controls the SMS notification systemto issue debris flow warnings.

Fig. 11. Example of SPS describe tasking response.

Fig. 12. Example of SPS submit request.

VI. SYSTEM PROTOTYPE

The prototype of our watershed-based debris flowearly-warning system is described in this Section. First ofall, Section VI-A introduces the setting of SWE services of52North open source implementation [35]. Section VI-B de-scribes GUI of WEPS for operation definition.

A. SWE Services

We use the service platform provided by 52North open sourceSWE implementation as our foundation. Our system includesSOS, SPS and SAS described as follows, respectively.SOS is set up on a personal computer with a Microsoft Win-

dows platform to query debris flow sensing data through theservice. Before that, we need to install PostgreSQL Databasewith PostGIS extension. PostGIS adds support for geographicobjects to the PostgreSQL database. In effect, PostGIS allowsPostgreSQL server to be used as a backend spatial database forGIS (Geographic Information Systems). SOS is a Web appli-cation deploys on Apache Tomcat Web server. WEPS performGetObservation operation will retrievemonitoring data encodedin the O&M specifications.SPS is set up on a personal computer with Linux platform to

issue debris flow warning through the service. Before that, we


need to install eXist XML database and OGCWNS (Web Noti-fication Service). eXist-db is an open source database manage-ment system built using XML technology. It stores XML dataaccording to the XML data model and features efficient, index-based XQuery processing. eXist-db provides a powerful envi-ronment for Web applications development based on XQueryand related standards. The Web Notification Service providesasynchronous notification of sensor events (tasks, observationof phenomena). We set up the 52North open source WNS im-plementation. WEPS performs submit operation to deliver con-trol instructions to the notification system for issuing warning.SAS is set up on a personal computer with a Microsoft

Windows platform to act as a debris flow monitoring stationand publish alert messages through the service. Before that, weneed to install PostgreSQL Database with PostGIS extensionand Ejabber server with a multiuser chat implementation. TheSAS uses the XMPP (Extensible Messaging and PresenceProtocol), a decentralized open XML-based protocol to pub-lish sensor data. Ejabberd is a free XMPP application server.Multiuser chat room can be used as a channel to communicatesensor alerts. Advertising operation can advertise monitoringresources to SAS which enables debris flow monitoring re-sources to provide alert services. WEPS performs subscribeoperation to subscribe alerts through SAS.

B. Watershed Event Process System

WEPS subscribes to debris flow monitoring messagesthrough SAS, retrieves monitoring data from debris-flow-pronestream through SOS and controls the notification systemthrough SPS to issue warnings. Because JAVA can complyacross different environments, we use it to develop the system.This system consists of a GUI (Graphical User Interface) anda database. There are three purposes for the GUI, includingto construct relationships for the debris-flow-prone stream,to set rainfall conditions for issuing warning, and to executeoperations of each SWE services. The GUI was designed intotwo tabs: Construct River and Deploy Device. For collectingbasin-wide monitoring data, the river network needs to beestablished. Current studies [16], [17], [23] have automaticprocedures for river network extraction from high resolutiontopography. We manually set up river network, including up-stream-downstream relationships and coordinates scope of eachbranch in Kaoping River by Construct River tab of the GUI.The system will combine automatic schemes to set up rivernetwork in the future. In order to develop an automatic system,an event-based definition of the debris-flow-prone stream is setup by Deploy Device tab in WEPS using SAS standards. WhenWEPS receives monitoring data, it can automatically processdebris flow events. A database stores the system information inPostgreSQL.Construct River tab is used by the observer to construct

the debris-flow-prone stream. Fig. 13 shows the ConstructRiver tab of the implemented GUI. The tab was designed intothree blocks. The stream information block is for displaying allbranches of the debris-flow-prone stream. The Add/Removebranch block is for editing the stream information. The observercan construct the stream under the actual situations. Stream

Fig. 13. The construct river tab used by the observer to define a river.

Fig. 14. Deploy device tab for observer search monitor resource.

map block is for displaying the stream map for a more intuitivevisualization of the watershed.Deploy Device tab is for the observer to set rainfall condi-

tions for issuing warnings and executing operations for eachSWE service. Fig. 14 shows the Deploy Device tab of the im-plemented GUI. The tab was designed into four blocks. Mon-itor resource list block is for displaying all monitor resourcesand the way for accessing resources of the debris-flow-pronestream. To add or remove device block is for register monitorresource operations. For instance, the observer registers a CCDcamera through SPS or advertises a rain gauge through SAS. Tosubscribe operation block is to subscribe alerts about observa-tion phenomenon. Stream map block used to display the debrisflow area and precipitation stations.

VII. EXPERIMENTAL RESULTS

In this section, experiments are designed to demonstrate howthe rainfall warning decision scheme shown in Section IV-Cand the watershed-based replanning mechanism can advancethe time for issuing warnings effectively. The experiments takeinto account several scenarios to simulate and generate numer-ical results for analysis and comparison.


Fig. 15. Time of issuing warning in central Taiwan.

A. Experimental Environment

The simulation results for the warning issuing time be-tween the schemes in the existing model and the proposedrainfall warning decision scheme are illustrated below. Forthe simulations, JAVA 2 is used to develop the simulatorwhich can be compiled across different environments. Thegoal is mainly to compare the warning issuing time betweendifferent types of rainfall warning models using historicalrainfall data. The simulation tools were developed under therainfall conditions of the proposed rainfall warning decisionscheme. In the simulations, experimental objects of Nan-fungVillage (Ren-ai Township, Nantou County), Bao-lai Village(Kaohsiung County), and King-lun Village (Taimali Township,Taitung County) in Taiwan were chosen. The current schemeuses accumulated rainfall value higher than 250, 400 and 400mm/hr to issue warning in Nan-fung, Bao-lai and King-lun,respectively. Furthermore, debris flow disasters that occurredin these regions during 2007, 2008 and 2009 typhoon seasonsare considered. The time of these disasters and the rainfall datafrom precipitation stations are also gathered. The rainfall dataserves as the input of the simulator. The simulator will outputthe warning issuing time.

B. Issuing Warning in Different Models

The time of issuing warning of the proposed rainfall warningdecision scheme was simulated. The Nan-fung Village, Ren-aiTownship, Nantou County encountered debris flow disastersin June 2007, September 2008 and August 2009 respectively.Fig. 15 shows a comparison with the scheme in the existingmodel and our scheme. The proposed scheme can advance thetime of issuing debris flow warning under the experimental re-sult.The Bao-lai Village, Kaohsiung County encountered debris

flow disasters in August 2007, July 2008 and August 2009 re-spectively. The rainfall data at the month of the debris flowdisaster inputs to the simulation tool. Fig. 16 shows a compar-ison between the scheme in the existing model and our scheme.The proposed scheme has better results. In 2008 and 2009 es-pecially, the existing model issued a warning after the disasteroccurrence. However, the proposed scheme issued a warning

Fig. 16. Time of issuing warning in southern Taiwan.

Fig. 17. Time of issuing warning in eastern Taiwan.

before the disaster occurs because this scheme uses rainfall in-tensity, rainfall duration and accumulated rainfall parameters asthe warning indicators.The King-lun Village, Taimali Township, Taitung County had

debris flow disasters in August 2007 and August 2009 respec-tively. Fig. 17 shows the experimental results comparing ex-isting model and our scheme. In 2007, the existing model didnot issue a warning, but our scheme provided 20 hours advancedwarning of the disaster.

C. Issuing Warnings Under Different Numbers of Conditions

This section presents the simulated influence of differentnumbers of precipitation stations. Simulation parameters fromBao-lai Village in Taiwan were chosen. Debris-flow-pronestreams and precipitation stations including Xinfa, Gaozhong,Fuxing, Xinan and Xiaoguanshan in nearby areas were shownin Fig. 18. The region had debris flow disasters in August 2007,July 2008 and August 2009, respectively. The current schemeuses accumulated rainfall R value higher than 400 mm/hr toissue warning in Xinfa. Our scheme refers the up-downstreamrainfall situation and the rainfall data were input into thesimulator to calculate the warning issuing time. Simulationsof our scheme were divided into four situations to evaluatewarning issuing time. The experimental results indicate that thewarning issuing time depends on the amount of stations issuingwarning.Fig. 19 shows the experimental result with debris flow events

in 2007 in which 1, 2, 3, and 4 denotes our scheme with any


Fig. 18. Debris flow region and precipitation stations.

Fig. 19. Number of precipitation station influence in 2007.

one, two, three, and four stations issuing debris flow warnings,respectively. The experimental result shows that the time of is-suing debris flow warning can be advanced by different situa-tions. For example, the predicted time of debris flow disaster inour scheme with any one of the five stations issuing warningsadvances 53 hours than the time of real happening. However,in the current scheme the predicted time of debris flow is 41.5hours ahead. When our scheme refers to more precipitation sta-tions, the time of issuing warning is more close to the time ofdebris flow disaster.Fig. 20 shows the experimental result with the debris flow

event in 2008. The experimental result shows that the proposedscheme can advance the warning issuing time. The currentscheme issues warning 2 hours late when debris flow occurred.Our scheme with any one of the five stations issuing warningsadvances 1.5 hours before debris flow disaster. The same,referring more precipitation stations is more close to the timeof debris flow disaster.Fig. 21 shows the experimental result with the event in 2009.

A simple conclusion can be made when observing these ex-perimental results. This debris flow monitoring system effec-tively collects rainfall data from multiple precipitation stationsin adjacent regions for early debris flow disaster warning. Inour scheme with any one of the five stations issuing warningsis earlier than the current scheme. Referring more precipitationstations is more close to the time of debris flow disaster.



VIII. CONCLUSIONS

This paper proposed a watershed-based debris flow early-warning system to collect basin-wide monitoring data to im-prove system accuracy. The system effectively monitors up-stream catchment areas with high density and obtains signalsbefore the occurrence of debris flows. Compared to the rainfallconditions presented in previous studies, we proposed a rain-fall warning decision scheme. This scheme uses rainfall inten-sity and accumulated rainfall parameters as the warning indica-tors. This scheme can advance the debris flow warning issuingtime comparing with the current scheme, so residents will havemore time to take refuge. The OGC SWE 1.0 standards wereapplied in this work to design the system architecture for en-abling integration and sharing of heterogeneous monitoring re-sources. The experimental results show that the rainfall warningdecision scheme effectively advances the warning issuing time.Moreover, to prove the feasibility of the proposed watersheddebris flow early-warning system, a system prototype was im-plemented. The experimental results illustrate that the proposedearly-warning system using historical rainfall data is better thanthe current scheme.Debris flow disaster occurrence warning could consider other

hydrological and geological factors. For instance, the warningdecision scheme could consider not only rainfall data but alsosensing data from other sensors such as geophone and waterlevel sensors to improve system accuracy, and provide fault tol-erance capability. Different rainfall conditions or rainfall esti-mation schemes should be considered as quantitative precipita-tion forecast to issue debris flow warnings more accurately. Thewarning decision scheme could combine with training mecha-nisms to make the system more adaptive to dynamic conditions


in order to issue debris flow warnings more effectively. In addi-tion, OGC SWE 2.0 standards could be implemented in WEPSto collect sensing data for different standards.

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Jen-Cheng Chiu was born in 1974. He received theM.S. degree in Information Engineering and Com-puter Science from Feng-Chia University, Taichung,Taiwan, in 2004. Currently, he is a Ph.D. studentin the Department of Information Engineering andComputer Science, Feng-Chia University, Taichung,Taiwan. His research interests include wirelesssensor networks and embedded systems.

Chyi-Ren Dow was born in 1962. He received theB.S. and M.S. degrees in information engineeringfrom National Chiao Tung University, Taiwan, in1984 and 1988, respectively, and the M.S. and Ph.D.degrees in computer science from the University ofPittsburgh, PA, in 1992 and 1994, respectively.Currently, he is a Professor in the Department

of Information Engineering and Computer Science,Feng Chia University, Taiwan. His research inter-ests include mobile computing, ad-hoc wirelessnetworks, agent techniques, fault tolerance, and

learning technology.


Cheng-Min Lin was born in 1964. He receivedthe B.S. and M.S. degrees in electronic engineeringfrom National Taiwan University of Science andTechnology, Taipei, Taiwan, in 1989 and 1991,respectively, and the Ph.D. degree in Departmentof Information Engineering and Computer Science,Feng-Chia University, Taichung, Taiwan, in 2001.Currently, he is a Professor in the Department

of Computer and Communication Engineering,Graduate Institute of Electrical Engineering andComputer Science, Nan Kai University of Tech-

nology. His research interests include embedded systems, mobile computing,wireless sensor network, and learning technology.

Jyh-Horng Lin was born in 1975. He received theB.S., M.S. and Ph.D. degrees in information engi-neering from Feng Chia University, Taiwan, in 1998,2000, and 2005, respectively.Currently, he is an Assistant Professor in the De-

partment of Electrical Engineering, Nan Kai Univer-sity of Technology, Taiwan. His research interests in-clude mobile computing, ad-hoc wireless networks,distributed systems, and computer algorithms.

Hsueh-Wei Hsieh was born in 1977. He received theM.S. degree in Information Engineering from Feng-Chia University, Taichung, Taiwan, in 2011.Currently, he is a staff member in the Department

of Computer Center, National United University,Miaoli, Taiwan. His research interests include wire-less sensor networks and computer network systems.

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A watershed based debris flow early warning system using sensor web enabling techniques in heterogeneous environments