Sensornet Early-warning System Integration

Stefania Nanni

and Gianluca Mazzini

Area R&P, LepidaSpa, Viale della Liberazione 15, 40128 Bologna, Italy

Engineering Department, University of Ferrara, Via Saragat 1, 44122 Ferrara, Italy

Keywords: Sensornet Platform, Early-warning System, Hydrological Simulation Model, Meteorological Modeling,

Extreme Rainfall Phenomena.

Abstract: In order to increase the resilience of the regional territory to extreme rainfall phenomena, LepidaSpA has

enhanced an already existing IOT platform, Sensornet, created to manage heterogeneous sensor networks

extended all over the entire territory of the Emilia-Romagna Region, introducing some new data not related

to physical measures, such as rivers level or amount of rainfall, but to their forecast. The novelty and the

strategic importance of the project presented in this paper is the incremental integration within Sensornet

platform of virtual sensors, based on hydrological simulation models and meteorological modelling, sharing

the same data model initially defined for physical ones, thus making available not only the continuous

monitoring of phenomena and their evolution, but also the generation of early warning in case of critical

thresholds with a forecast up to 12/24 hours. The capability to detect forerunners constitutes a fundamental

requirement to increase the ability to recognize in advance critical scenarios and to support their

management.

1 INTRODUCTION

Sensornet is the Internet of Things Platform of the

Emilia-Romagna Region, collecting data and

information from thousands of objects distributed

across the territory, and building in time a digital

map of the reality we live in Figure 1.

Figure 1: Sensornet platform.

Whether they are generated by inclinometers for

landslide monitoring, hydrometric sensors for the

level river control, or inductive coils for the traffic

monitoring, data generated by the sensors define a

snapshot of a reality made of continuously updating

information, allowing a better knowledge of what is

happening in cities and in territories (Nanni and

Mazzini, 2015).

The platform collects measures taken in real time

from different sensors and handles automatic

reporting when critical conditions are detected such

as exceeding thresholds or rapidly evolving

phenomena (Figure 2).

The first part of this paper illustrates the needs

and objectives from which a new project, related to

the support management of extreme rainfall

phenomena, has been conceived, with a short

digression on the difference between hydrological

models to be used in the case of medium and large

rivers and in the case of the small ones.

Next section will describe how, after the flooding

event of 2014 involving the city of Parma of Emilia-

Romagna region, ArpaE (the Regional Agency for

Prevention, Environment and Energy of Emilia-

Romagna) has developed a hydrological simulation

model that allows to know in advance, with a given

degree of probability, the approach of critical

thresholds of the hydrometric level at some points of

observation of the main rivers of Emilia-Romagna

region.

The second part of the document outlines the

results obtained by the integration of forecast data

Nanni, S. and Mazzini, G.

Sensornet Early-warning System Integration.

DOI: 10.5220/0006533100770084

In Proceedings of the 7th International Conference on Sensor Networks (SENSORNETS 2018), pages 77-84

ISBN: 978-989-758-284-4

Figure 2: Hydrometric level and relative thresholds.

with the observed ones, related to the management

of extreme rainfall phenomena, within the Sensornet

platform.

The final part describes the future expected

developments.

2 THE STATE OF THE ART

Climate change is affecting all regions in Europe

causing a wide impact on society and environment.

Recently, extreme weather events as heat waves,

floods and droughts have caused rising damage costs

across Europe. In future, further impacts are

expected to rise societal vulnerability, potentially

causing high damage costs, as published by the

European Environment Agency (http://www.eea.

europa.eu/media/publications/climate-impacts-and-

vulnerability-2012).

Recent studies focused on climate change

projections over Northern Italy and Emilia-Romagna

region reveal that a peak of changes on minimum

and maximum temperature is expected during the

summer season at the end of the century (2071-

2099), when the increase in the average could reach

3.5- 4°C with respect to present climate 1961-1990.

On the other hand, significant changes on

precipitations are expected during summer, also at

the end of century, when a reduction up to 40% is

foreseen.

The most obvious effects of these climate

changes are the increase in the number of extreme

rain events and consequent flooding events, which

can have critical consequences especially in urban

areas where the concentration of the population and

services is higher (Figure 3).

Figure 3: Parma flash-flood October 2014.

Many studies are currently addressing the

problem of predicting critical environmental

phenomena, including flooding.

Some of them are not based on forecasting

system, but rather on real-time monitoring systems

(Baxter and Francis, 2000). or on certain

percutaneous parameters of the phenomena

(Chaczko and Ahmad, 2005, July).

In other cases, the forecasting system is based on

the integration of the predictive algorithm in

physical sensor networks, and is strictly bound to all

SENSORNETS 2018 - 7th International Conference on Sensor Networks

the problems and limitations associated with this

type of solution (Basha et al., 2008). In other cases,

the predictability aspect of the phenomenon is only

mentioned as one among many others involving in

the management of critical events (Basha and Rus,

2007, December).

The preparation and reaction to such disruptive

phenomena can increase the resilience of the

territory in short time as early warning of hazardous

conditions and in medium term as territorial

planning and preparation to emergency response.

The management of extreme rain events can not,

therefore, solely rely on traditional real time

monitoring systems, but must also include new

forecasting systems based on hydrological

simulation models and meteorological modeling.

3 RainBO LIFE

The analysis of climate variability over the

municipality of Bologna, as resulted from the

BlueAp LIFE project (Bologna Local Urban

Environment Adaptation Plan for a Resilient City

2012-2015), reveals important changes observed in

the main climatological variables.

During the last two decades, years with intense

precipitation have been frequently registered in

Bologna, having an important impact on the city and

its citizens.

The quantity of precipitation shows a slightly

negative trend during winter, spring, and summer

and a positive trend during autumn, over the period

1951-2011.

With regards to seasonal extreme of

precipitations, the dry days index presents a positive

tendency over 1951-2011 period, more intense

during summer.

Analysis performed on intense precipitation time

series evidence a slightly positive trend of the

frequency of days with intense precipitation (based

on 90th percentile as a threshold) in all season,

except on spring.

The flooding risk of small water courses is a

major problem in several urban areas (especially in

Italy): the constant growth of urbanization, with the

consequent decrease of soil permeability and loss of

space for river and stream beds, is leading to

increased flood hazard and vulnerability; in such

conditions, severe rainfall events over steep

catchments of limited area can produce dramatic

consequences; in addition, ongoing climate changes

are likely to increase the occurrence of severe

precipitation events, thus increasing flash flood

hazard.

Historical and recent records report that the

urban areas of Bologna located beneath the highland

are prone to severe flood events caused by small

water courses.

The most severe event occurred in 1932, when

rainfall of 134 mm within a few hours caused

flooding of a large urban area, including a portion of

the Ravone catchment area.

Another severe flood event occurred in the

Bologna area in 1955, while in 2002 a further flood

event affected several small municipalities nearby.

In all of these cases, the recorded hourly peak

intensity exceeded 50 mm/h.

Despite its relevance, the risk of flooding of

small water courses in urban areas is often

underestimated and few measures are taken for

prevention and mitigation (Grazzini et al., 2013).

The high level objective of RainBO LIFE project

(2016-2019), that is a follow-up of BlueApp one, is

the improvement of knowledge, methods and tools

for the characterisation and forecast of heavy rains

potential impact due to the hydrological response,

not only of medium and large basin, but also of the

small ones and for the evaluation of the vulnerability

of assets in the urban areas.

4 HYDROLOGICAL MODELS

4.1 Medium and Large Basins:

Random Forest Method

Following the flooding of the Baganza river in

Parma on October 2014 (Figure 3), caused by heavy

rains, which flooded several neighborhoods

southwest of the city, the Civil Protection Agency of

the Emilia-Romagna region required ArpaE the

development of a hydrological simulation model to

be able to recognize in advance the probability of

overcoming the three alert thresholds fixed for the

main rivers of Emilia-Romagna region: Warning

(threshold 1), Pre-alarm (threshold 2), Alarm

(threshold 3).

Hydrological modeling for medium and large

basins is based on a statistical method, Random

Forest, which uses decision trees.

The Random Forest model, applied to hydraulic

modeling, provides the probability of overcoming

the alert thresholds of some observation point of the

medium and large basins, for the next 6-8 hours,

depending on the dynamics of the river.

In particular, the Random Forest hydrological

model gives the following forecast data:

Sensornet Early-warning System Integration

1. Probability of not exceeding threshold 1

2. Probability of exceeding threshold 1

3. Probability of exceeding threshold 2

4. Probability of exceeding threshold 3

4.2 Small Basins: Criteria 3D Model

Forecasting models of heavy rainfall initiating flash

flood from small basins are different from other

models (e.g from large basins or waterways).

The size of the small basin results in very rapid

response times to heavy rainfall.

In other words, the time interval between the

start time of the precipitation and the span peak can

be reached in less than two hours, which in reality

would make the prediction of the event very difficult

and therefore the alert system.

For this reason, it is considered essential to

develop a hydraulic simulation model for small

basins, and the installation of specific measuring

points, allowing hydrometric observations to its

validation.

Figure 4: Ravone creek.

Criteria3D is a three-dimensional hydrological

model, which also simulates water infiltration into

the soil, developed by ArpaE-SIMC of Emilia-

Romagna region.

The model was developed starting from the study

of the river Ravone, which is one of the creek that

from the hills south of Bologna goes down to the

city (Figure 4).

In this basin, all those critical and valuable

factors that are present in the hilly waters, such as

the effects of the strong anthropization that currently

characterizes the end of the valley and the crossing

of the city, are also present. (Figure 5)

Figure 5: Ravone's suture and measure point.

Figure 6 shows the good result of the test of

simulation of the water level at the stream gauge of

Ravone in the event of 2015-02-05.

Figure 6: Criteria3D simulation of the water level at the

stream gauge of Ravone in the event of 2015-02-05.

5 METEOROLOGICAL

MODELING COSMO-LAMI

The numerical meteorological model limited area

Cosmo-Lami (https://www.arpae.it/dettaglio_general

e.asp?id=2584&idlivello=32 ), called Lami for

brevity, carried out in consortium between national

civil protection department, USAM Air Force, Harp

ArpaE Piemonte and ArpaE Emilia-Romagna,

provides numerical forecasts with a spatial

resolution at 7km and 2.8Km and temporal validity

respectively three and two days.

Predictions based on this model are carried out

twice a day, at 00 and at 12 UTC, at the

supercomputing Cineca center, in accordance to a

contract with the IdroMeteoClima Service and the

Department of National Civil Protection.

Data are provided in grid format, GRIB

(http://apps.ecmwf.int/codes/grib/format/grib1/overv

SENSORNETS 2018 - 7th International Conference on Sensor Networks

iew), and each file contains forecast data for various

meteorological parameters, including precipitation,

either at the surface or close to it, at an hourly or

three-hourly in dependence on the parameter.

The data relating to the meteorological modeling

can be used both at the level of maps to have, for

example, an overview of the precipitation forecast,

but also at the numerical level to have, for example,

the detail on hourly precipitation provided on a

given grid cell, as in the case of Ravone creek,

which constitutes the basic information for the

prediction of the hydrometric level corresponding

starting from the product simulations scenarios

resulted from the 3D hydrological model for small

basins.

Data from meteorological modelling, limited to

the Emilia-Romagna region, are distributed GRIB

format on the open date platform of ArpaE of

Emilia-Romagna.

6 SENSORNET

EARLY-WARNING SYSTEM

INTEGRATION

Sensornet early-warning system integration is based

on the integration of forecast data into the platform

through the configuration of new virtual sensors

based on hydrological simulation models and

meteorological modelling.

The integration of these new virtual sensors into

the Sensornet platform has been accomplished in a

simple and immediate way, using the same data

model defined for the physical sensors, without the

need for any extension or specialization and

providing the platform with a new feature crucial for

recognition and generation of early-warning reports.

6.1 Sensornet Forecast Data

Integration

Sensornet constitutes the monitoring subsystem of

RainBO platform.

In addition to the data coming from the

traditional real-time monitoring system of ArpaE,

mainly consisting of regional hydrometers stations

to measure temperature, rain and hydrometric levels,

it also integrates those belonging to the forecast

systems, for the scope illustrated before.

The integration of sensors data coming from

different monitoring systems in Sensornet is realized

through a federated approach whose main advantage

is to preserve the investments made on already

existing systems and to protect the technical,

technological and organizational autonomy of the

individual systems and of their owners.

The architecture implemented in Sensornet

platform provides an interconnection middleware

between the different data sources and the central

system, acting as a data collector from different

sources and a data normalizer towards the central

system, as shown in Figure7.

Figure 7: Flow of collection, standardization, storage and

access of the data.

It consists of a series of atomic modules for data

retrieval from individual sources and of their

manager, which oversees their activation and

coordination.

Each module contains the access rules and the

required commands for retrieving data from a

specific source or database and for storing them in a

standard format on the centralized database.

In order to acquire data from heterogeneous

sources and use them in a contextual and correlated

mode, a standardization process is necessary.

The creation of a standardized data stream is one

of the added values offered by Sensornet platform,

which transforms the data from the different sources

into a single standard format, regardless of the

technologies, the interfaces, formats and data type of

the sources (Nanni and Mazzini, 2015).

The integration of the hydrological simulation

models and meteorological modelling data in

Sensornet has been achieved through the definition

of "virtual" sensors, which, unlike the real, are not

associated with physically measured data, but to the

forecasted ones provided by the models.

This type of solution allowed to completely

integrate these new types of "virtual" sensors with

the real ones, while maintaining the consistency of

data and their modeling within the Sensornet

platform.

Sensornet Early-warning System Integration

6.1.1 Lami Virtual Sensor Integration

The new "virtual" Lami sensor has been defined in

Sensornet platform to allow the integration of the

GRIB data related to the modeling of the weather,

needed for the 3D hydrological simulation model for

small streams.

In the case of the Ravone stream, as in most

small streams, the size of the basin is contained in a

single cell of the reference grid, whose data can then

be represented by a sensor placed within the cell

itself.

From the datum for cumulative rain, collected

from GRIB data at the beginning of each run for

each cell, it is possible to calculate the

corresponding precipitation per hour.

Once a precipitation threshold has been

established, it is possible to determine when a

precipitation starts and when it finishes, to infer the

duration as well as the peak and the accumulated of

the corresponding event.

The algorithm to calculate the significant

parameters of a rainy event, starting from the data

for the cumulative hourly precipitation, can be

described as follows:

 starting from the GRIB data (the cumulative

hourly precipitation from the beginning of the

run), the hourly precipitation is extracted with

simple subtractions;

 the hours in which rain is expected and when it is

not (0-1) are calculated according to the

established threshold (normally 0,2 mm);

 depending on the distribution of 0 and 1, it is

estimated when a rainy event starts and when it

ends;

 at this point it is possible to calculate the

duration, the accumulated (constitutes from all

the hourly precipitation included in the event)

and the maximum intensity relative to the event

(peak).

The storage of the main parameters of a rainfall

event, calculated as it has been described, is made by

defining four corresponding measures associated

with each "virtual" Lami sensor:

 hourly precipitations

 event

 cumulative hourly precipitations

 peak

The integration of the new type of Lami virtual

sensor inside the Sensornet platform, indeed,

required the implementation of a new GRIB data

acquisition and processing module, the definition of

a new type of Lami sensor, to which the four

measures previously described are associated, and

the configuration of a new Lami type sensor at the

Ravone stream, identified by the coordinates of the

corresponding grid cell.

The integration of other sensors related to the

meteorological forecasts at another stream, simply

requires the configuration of another Lami type

virtual sensor associated with the coordinates of the

corresponding grid cell.

Figure 8 shows an example of graphing data for

the Lami sensor defined for the Ravone cell (lat

44.46 and lon 11.31) provided at 00:00 on

30/06/2017 and valid for 72 hours later.

In the example shown, the expected rainfall is

below the defined threshold (normally 0,2 mm) and

is therefore not a source of rain for a rainfall event.

Figure 8: Example of graphic data of Lami virtual sensor.

6.1.2 Random Forest Virtual Sensor

Integration

The new "virtual" Random Forest sensor has been

defined to allow the integration the data related to

hydrological modeling for medium and large basins.

The integration of the new type of Random

Forest virtual sensor inside the Sensornet platform

required the implementation of a new data

acquisition and processing module of the .csv files

(provided by ArpaE every 10 minutes on a

ftpServer, hosted by LepidaSpa), the definition of a

new type of Random Forest sensor, to which the

previously described four probabilities are

associated, and the configuration of two new

Random Forest type sensors in correspondence of

Parma and Baganza rivers, identified by the station

ID of the corresponding hydrometer.

The integration of other data related to

hydrological modeling for medium and large basins,

simply requires the configuration of others Random

Forest type virtual sensor associated to

corresponding .csv files.

SENSORNETS 2018 - 7th International Conference on Sensor Networks

Figure 9 shows a graphical representation data

for the Random Forest sensor defined for the

Baganza river provided at 14:40 of 18/07/2017 and

valid for 6 hours later.

In the example, the probability of the river level

of not exceeding threshold 1 in the point of

observation is equal to one, while the probability of

exceeding threshold 1, threshold 2, and threshold 3

is equal to zero.

Figure 9: Example of virtual sensor Random Forest data

graphication.

7 SENSORNET

EARLY-WARNING SYSTEM

INTEGRATION RESULTS

The term early warning (EW) indicates alarms that

arise in the time interval between the moment in

which phenomena potentially triggering a dangerous

event are observed and the time at which the event

happens.

Time scales characteristic of early warning are

different for different types of events:

 from seconds to tens of seconds for earthquakes;

 from minutes to hours for tsunamis;

 from hours to days for weather events;

 from hours to days to floods and landslides;

 from hours to weeks to volcanic eruptions.

The adoption of early warning (EW) methodologies

is considered as essential to cope with disasters (not

just natural) in a world where the population is not

only increasing, but it is concentrated in megacities

of several (or even tens) millions of inhabitants.

In fact, the EW appears as a keyword in all

documents addressing the problem of risk reduction,

both nationally and internationally (Baxter and

Francis, 2000).

With regards to hydraulic risk, the possibility of

early detection of extreme precipitation events and

their effects on the river level allows to recognize in

advance critical scenarios and to support their

management or, vice versa, to give evidence of the

absence of critical conditions.

Figure 8, for example, shows the rain forecast for

the next 72 hours on Ravone's cell.

The graph gives evidence of the expected rainfall

and its cumulated level, whose duration and

intensity are not sufficient to generate a significant

event to report.

Figure 9 shows that the probabilities of the level

of Baganza River to not exceeding threshold 1 is

one, while it is equal to zero the probability that it

exceeds any of the three defined alert thresholds, for

the next 6 hours.

Integration of forecast model data is the right

prerequisite for the creation of an early-warning

system that allows recognition and signaling of

critical thresholds over with an anticipation that

depends on the simulation model used.

In particular, the integration of forecast data

related to the hydrometric level for medium-sized

basins and rainfall events for small ones allows to

identify critical scenarios in advance with a margin

of some hours in the case of medium-sized basins

and up to a few days for those little ones.

According to the previous time scales the

forecast data integrated in Sensornet platform

provides the right conditions for the provision of an

early-warning system effective and useful for the

management of extreme rain events and floods

events.

8 CONCLUSIONS

RainBO LIFE is a very ambitious project that aims

to provide a support platform for the management of

extreme precipitation events both in the medium

term as territorial planning and preparation to

emergency response and in short time as early

warning of hazardous conditions.

Sensornet platform, that constitues RainBO

monitoring subsystem, was already integrating the

data of the major traditional monitoring systems, but

its modular, flexible and configurable architecture

allowed immediate integration of the forecasts ones,

from which it depends the increase of resilience of

urban areas through the early warning of hazardous

conditions.

The support for the management of extreme

precipitation events in the short term also includes

the integration of innovative technology-based

monitoring systems, such as microwaves links,

Sensornet Early-warning System Integration

which can provide rainfall measurement from the

attenuation level of radio signals of the base radio

stations of the cellular networks.

Once the radio data acquisition module will be

finalized, Sensornet will also integrate new virtual

sensors corresponding to the intermediate point of

the radio links, for which the signal attenuation data

stream will be available.

This new type of virtual sensors will be another

added value of Sensornet platform, and therefore of

the monitoring subsystem of the RainBO project.

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SENSORNETS 2018 - 7th International Conference on Sensor Networks