Modeling Sensor Knowledge of a National Hydrologic

Information System

Martin Molina and Javier Sanchez-Soriano

Department of Artificial Intelligence, Technical University of Madrid

Campus de Montegacedo S/N, 28660 Boadilla del Monte, Madrid, Spain

Abstract. In this paper we describe our experience in modeling and using sen-

sor knowledge of a national hydrologic information system in Spain. We devel-

oped a web application called VSAIH supported by a knowledge-based system

to analyze sensor data and to generate explanations that help users to make de-

cisions based on hydrologic behavior. In the paper, we describe the characteris-

tics of the infrastructure of hydrologic sensors and the representation we used to

model sensor knowledge to provide support to the VSAIH application. We also

describe

semi-automatic procedures that we applied to construct the final

model.

1 Introduction

SAIH is an information system based on an infrastructure of sensor devices and tele-

communications networks in the main river basins of Spain (SAIH is the Spanish

acronym for Automatic System Information in Hydrology). The main goal of the

SAIH system is to help to know in real time the state of the rivers. Currently, the most

important basins in Spain (Ebro, Tajo, Júcar, etc.) have installed this infrastructure

fully operational.

The SAIH information system is a good example of a system that includes a geo-

graphically distributed sensor network that records valuable data for different types of

goals (natural disasters, climate change, water management, energy production, etc.)

and actors (local governments, scientists, etc.). The current initiatives about sensor

web for globally distributed data acquisition [2] and semantic sensor web [9] may

provide solutions to improve the capabilities of sharing and analyzing sensor data as

well as potential interoperability between systems. This is especially important in the

hydrologic domain where there is specialized knowledge about the diverse physical

phenomena that is distributed among different local institutions.

In this paper we present our experience in modeling and using sensor knowledge

for the case of the SAIH information system. We developed the VSAIH application

that interprets and analyzes sensor data to provide explanations to help to make deci-

sions to different types of user. In the paper, we describe the SAIH Information Sys-

tem with the different types of sensors. We describe the VSAIH application that in-

terprets and explains sensor data according to different communicative goals. We also

describe the representation we used to model sensor knowledge and the semi-

automatic procedures that we applied to construct the model.

Molina M. and Sanchez-Soriano J..

Modeling Sensor Knowledge of a National Hydrologic Information System.

DOI: 10.5220/0003114500230031

In Proceedings of the International Workshop on Semantic Sensor Web (SSW-2010), pages 23-31

ISBN: 978-989-8425-33-1

 2010 SCITEPRESS (Science and Technology Publications, Lda.)

2 The SAIH Information System

The SAIH National Program (Spanish acronym for Automatic System Information in

Hydrology) was initiated in Spain at the end of the eighties [3]. The goal of this pro-

gram was to install sensor devices and telecommunications networks in the main river

basins to get on real time in control centers hydrologic information about the state of

the rivers. Currently, the most important basins in Spain (Ebro, Tajo, Júcar, etc.) in-

clude this infrastructure.

Fig. 1. Web application provided by the Spanish Ministry of Environment about hydrologic

data from the SAIH system at national level. This screen shows the geographical locations in

Spain where a user can consult real time data about water flows in rivers.

The SAIH system includes different types of sensors such as pluviometers that

record information such as rainfall at certain locations, sensors for water levels, and

sensors for flow discharge in reservoirs and flows in certain river channels. There are

nine SAIH control centers in Spain, one for each main basin (Ebro, Tajo, Júcar, Segu-

ra, etc.). Using the SAIH system, information is recorded periodically and sent to the

control centers (e.g, every hour, 30 minutes or 15 minutes).

Control centers process and store the data in local databases. In addition, the Min-

istry of Environment of Spain coordinates and integrates recorded data the informa

Fig. 2. Web application provided by the Ebro basin (Confederación Hidrográfica del Ebro)

about hydrologic data from the SAIH system. This screen shows the geographical locations in

the Ebro basin where a user can consult real time data about water flows and water levels in

rivers.

tion in a global database. Part of this information is accessible through web applica-

tions (see figures 1 and 2).

Figure 3 summarizes the number of sensors installed in different river basins. The

characteristics of the sensors are the following:

• Pluviometer (P): It is a device responsible for measuring the precipitation of rain

at the point of the basin in which it is located. The units are millimeters per hour.

These are the majority of SAIH sensors and represent nearly 55% of the total set

of sensors.

• Flow sensor (Q): A flow station is a device located on the riverbed to measure

its flow. It is measured in cubic meters per second.

• Level station (N, C): A level station is a device located at a reservoir or a river. A

level sensor measures the water level of the river or the dam on which it is lo-

cated. It is measured in meters with respect to sea level.

• Volume station (Y): A volume station is located at the dam of a reservoir to

measure the volume of water stored in it. For practical reasons, this is considered

as a sensor but actually it is deduced locally from the level of the reservoir. It is

measured in hectometers.

In many cases, sensors of different types share location and communications as

well as other functions (water and air quality, etc). Reservoirs usually have pluviome-

ters and level sensor beside others. Riverbeds usually have pluviometers together with

flow sensor or level sensor. The SAIH infrastructure also includes specific telecom-

munication devices (radio emitter-receiver systems, optical fiber networks, etc.) that

establish the communication between the sensors and the control center of the basin.

Basin

Sensor type

Total

P Q Y N C

Ebro Basin 247 148 60 60 0 515

Tajo Basin 191 57 45 48 44 385

Guadalquivir Basin 126 11 62 62 0 261

Guadiana Basin 183 33 12 20 0 248

Júcar Basin 124 38 23 23 0 208

Norte Basin 81 45 21 21 0 168

Sur Basin 99 0 12 12 15 138

Segura Basin 64 27 15 15 0 121

Cataluña Basin 56 40 12 12 0 120

Total 1171 399 262 273 59 2164

Fig. 3. Summary of available sensors in the SAIH system.

3 The VSAIH Application

VSAIH is a web application supported by a knowledge-based system [7] for generat-

ing multimedia descriptions that summarize the behavior of hydrologic networks

controlled by the SAIH system. We developed this system to help users that need to

interpret and analyze the behavior of rivers and make decisions according to prefixed

management goals. Our system generates presentations using different modes such as

text in natural language (as it is done by other data-to-text systems [8][11][4]) and,

also, dynamic illustrations (for example, animations, interactive geographic maps and

2D graphics).

VSAIH uses a system model with a representation of the hydrologic system based

on components and causal influences. VSAIH includes an abstraction generator that

uses the system model to find relevant data and condense it at an appropriate level of

abstraction. In addition, VSAIH includes a hierarchical planner to generate a presenta-

tion using a presentation model with discourse patterns as it is done by other multi-

media presentation systems

[1][5][10].

We developed for VSAIH a common system model that includes sensor know-

ledge. In addition, we developed three other presentation models according to three

different management goals: flood risk, water management, and sensor validation. For

example, the flood risk management goal is to avoid river floods. In this case, control

actions are oriented to operate reservoirs to avoid problems produced by floods and, if

problems cannot be avoided, to send information to public institutions in order to plan

defensive actions. For this goal, the summaries report relevant information of the river

basin from the point of view of potential or existing floods. In the following sections,

we describe more details about how we represented sensor knowledge for the VSAIH

application.

Fig. 4. Example of window presented by the VSAIH application.

4 Sensor Knowledge Representation

In order to represent sensor knowledge for the VSAIH application, we use a know-

ledge representation following a component-based approach. We use a formalization

based on many-sorted first order logic [6].

We use the following basic sorts: component represents a physical object of the

system (for example, a reservoir or a river), quantity is a quantitative property of a

component (e.g., the temperature or the pressure), and sensor is a device used to

measure observable quantities of components. More specific components can be re-

lated to more general components (with the is-a relation) by defining subsorts of the

sort component with the notation sort s: t (where s is subsort of t). For example, sort

reservoir: component defines the subsort reservoir of the sort component.

To characterize qualitative properties of the components we use the following

sorts: state represents the qualitative state of a component in the present moment (for

example, the state of a reservoir is empty), recent_state, represents the state of a com-

ponent in a recent time interval (e.g., the last 24 hours) and it is usually described in a

more abstract level than the state, trend, represents the trend of a state (for example,

with the set of values {increase, steady, decrease}) and quantification is a sort that

quantifies states for a given population (for example, with the set of values {all,

many, few}).

Predicate Description

part-of(x, y)

Component x is part of component y.

measure(x, y, z)

Sensor x measures the quantity y of component z.

cause(x, y, z, u, t)

Quantity x of component y is cause of quantity z of component u

with a delay t .

measurement(x, y)

Sensor x measures time series y.

value(x, y, t, v)

Quantity x of a single component y with t_scope t has the value v.

value(x, y, t, z, v)

Quantity x of a complex component y with t_scope t and c_scope z

has the value v.

state(x, y)

The state of component x is y.

recent_state(x, y)

The recent state of component x is y.

trend(x, y)

The trend of the state of component x is y.

quantification(x, y)

The quantification of the state of component x is y.

state_category(x,y,z)

The state z is a category of the state y of component x.

Fig. 5. Examples of predicates to represent knowledge about the dynamic system.

In order cope with different levels of abstraction, our representation also includes

the scope of certain affirmations. For this purpose, we use the concept of relative

scope to a specific domain. We use two sorts: t_scope which defines a temporal scope

and c_scope which defines the scope in a set of subcomponents that are part of a giv-

en component. For example, a possible value for temporal scope is max(n) that means

the maximum value for the last n hours.

Figure 5 shows a list of predicates to represent knowledge about the dynamic sys-

tem. For example, to represent structural relations we use the predicate part-of(x:

component, y: component) for the part-of relation and measure(x: sensor, y: quantity,

z: component) to relate sensors and quantities of components. The predicate cause(x:

quantity, y: component, z: quantity, u: component, t: number) represents a direct

causal influence between two quantities. The relation includes a temporal delay be-

tween the cause and effect.

To represent the value of a particular quantity we use the predicate value(x: quan-

tity, y: component, t: t_scope, v: value) for the case of a single component. This pre-

dicate defines the value for the quantity of a component with a particular temporal

scope. For example, value(temperature, tank-T3, current, 120) represents that the

current temperature of tank-T3 is 120 and value(volume, reservoir-R8, min(24), 18)

represents that the minimum volume of reservoir-R8 in the last 24 hours is 18. This

predicate also helps to represent historical information about behavior (e.g., average

values, maximum historical values, etc.). The predicate for the case of complex com-

ponents is value(x: quantity, y: component, t: t_scope, z: c_scope, v: value). It in-

cludes an additional argument for c_scope. For example, value(rain, Spain, current,

max, 27) represents that, at the present moment, the maximum rain in the set of points

(where rain is measured) that are part of Spain is 27.

To interpret the current state of a component we use the predicates state(x: com-

ponent, y: state), trend(x: component, y: state) and quantification(x: component, y:

quantification). For example the tuple <state(Spain, heavy-rain), trend(Spain, de-

crease), quantification(Spain, few)> represents that there is a decreasing heavy rain in

a few points of Spain. It is also possible to use the predicate recent_state(x: compo-

nent, y: state) for a recent time interval.

5 Model Development

The available information about SAIH sensors through the web application of the

Spanish Ministry of Environment includes basic information such as the identification

code, the sensor type (pluviometer, flow sensor, etc.) and the geographical location

(latitude, longitude in UTM format). However, in order to construct a model for the

VSAIH system, it is necessary to associate to sensors additional information that

currently is not present in this database. This includes, for example: geographical

administrations (provinces, regions, etc.), natural formations (rivers, lakes, etc.), his-

torical values (maximum value, average value, etc.), causal influences among sensors

due to downstream flow, standard names in natural language (the existing text de-

scriptions do not follow a standard approach), etc.

For this purpose, we applied a knowledge acquisition process supported by auto-

mated tools (developed in our own research group) using additional information

sources. Examples of these knowledge sources include (1) geographic information

such as raster files with digital elevation models and vector data files with rivers,

reservoirs, basins, dams, administrative limits (provinces, regions, etc.), (2) web ap-

plications with publicly available information, such as www.geonames.org that pro-

vides names for different locations and web pages with hydrologic information pro-

vided by local SAIH control centers.

Some of the automated procedures that we performed to build the model were the

following:

• Spatial Analysis. We used information provided by geographical data in raster

files and vector data files to create associations between model components. For

example, we used the geographic location of sensors to associate each sensor

(flow sensor or level sensor) to the corresponding river by using the vector data

files with multi-lines describing the shape of river channels. In addition, we es-

tablished causal relations, represented with the predicate cause(x, y), with spatial

analysis. We distinguished two different cases for causal relations: (1) causes as-

sociated to pluviometers, for each type of flow or level sensor we selected nearby

pluviometers using a prefixed maximum distance, (2) causes associated to river

channels, we analyzed geographic multi-lines of rivers and elevation models to

establish the causal relations based on a downstream influence of water flows and

water levels.

• Statistical Analysis of Historical Values. We consulted web pages and processed

databases to obtain historical values required for the model (average, maximum,

minimum, etc). These values are useful to select relevant values according to pre-

fixed goals.

• Text Processing for Sensor Names. We constructed for each sensor an appropri-

ate unique name in natural language. This task is essential to generate unders-

tandable text summaries. For this task, we used the complete information of the

sensor (for example, UTM coordinates, type of sensor, river, region, etc.) togeth-

er with a rule base (with conventions about names and certain heuristics) and the

web application www.geonames.org. For instance, we automatically constructed

the name [río Guadalquivir en Andújar] (river Guadalquivir at Andújar) for a

sensor that originally had the description [M10_GLQUVIR_AND].

As a result of this process, we developed a model that includes 14,337 elements

distributed in the following way: 1,864 values of sort sensor, 2,230 values of sort

component, 2,229 instances of predicate part_of(x, y), 1,864 instances of predicate

measure(x, y, z), 2,068 instances of value(x, y, t, v) (e.g., maximum value and average

value), 2,295 instances of cause(x, y, z, u, t) for pluviometers, 687 instances of

cause(x, y, z, u, t) for river channels.

The model was implemented in Prolog language. We evaluated the model with the

VSAIH application in continuous operation for more than one year with the help of

three experts in hydrology. The VSAIH application includes three other models (for

flood risk, water management and sensor validation) that share this common sensor

model. The current version generates summaries by processing every hour 44,736

numerical measures (for each sensor, a time series for the last 24 hours, a value per

hour).

6 Conclusions

In this paper we have described our experience in modeling and using sensor know-

ledge for the case of a national hydrologic information system. In the paper, we have

described how we developed sensor models for the VSAIH application that interprets

and analyzes sensor data to provide explanations to help to make decisions to differ-

ent types of users.

We applied a semi-automatic knowledge acquisition process to construct the mod-

el. In this process, we performed certain operations (spatial analysis, statistical analy-

sis and text processing) to capture and represent knowledge from different informa-

tion sources (geographical information systems, public web sites and specific data-

bases).

This domain is an example of a system that includes a geographically distributed

sensor network that records valuable data for different types of goals and users. Our

future work includes using techniques about data sharing and semantic web in this

domain. Standard semantic annotations for sensor knowledge (for example, historical

values, geographic information, causal influences and standards about names) can be

useful to help in the automatic creation and maintenance of models that use sensor

data for specific purposes. We expect that the semantic sensor web approach may

provide solutions to improve the capabilities of sharing knowledge between different

institutions and users interested in hydrologic information (e.g., scientists, local gov-

ernments, coordination groups, etc.).

Acknowledgements

This work was possible thanks to the support of the Ministry of Environment of Spain

(Dirección General del Agua, Ministerio de Medio Ambiente, Medio Rural y Mari-

no). The work was also partially supported by the Ministry of Science and Innovation

of Spain within the VIOMATICA project (TIN2008-05837/TIN).

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