An open drought monitoring system for the Deduru Oya basin in Sri Lanka in the context of the 4onse project.
This is a modal window.
The media could not be loaded, either because the server or network failed or because the format is not supported.
Formal Metadata
| Title |
| |
| Title of Series | ||
| Number of Parts | 295 | |
| Author | ||
| Contributors | ||
| License | CC Attribution 3.0 Germany: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor. | |
| Identifiers | 10.5446/43581 (DOI) | |
| Publisher | ||
| Release Date | ||
| Language |
Content Metadata
| Subject Area | ||
| Genre | ||
| Abstract |
| |
| Keywords |
00:00
Context awarenessResultantComputer animationLecture/Conference
00:20
System programmingOpen setContext awarenessIntegrated development environmentMathematical analysisPlastikkarteExecution unitSoftware maintenanceState observerSatelliteProjective planeContext awarenessMereologyRemote procedure callType theoryDifferent (Kate Ryan album)Computer programmingUniverse (mathematics)Observational studyOpen sourceMultiplication signStandard deviationSoftwareAreaSystem programmingMedical imagingIntegrated development environmentCAN busOpen setCombinational logicAddress spaceWordLie groupCore dumpSoftware developerLecture/Conference
02:57
Integrated development environmentMeasurementTemporal logicImage resolutionAngular resolutionSoftware maintenanceData managementLocal ringScale (map)System programmingOpen setMathematical analysisBit rateResultantProcedural programmingPoint (geometry)Population densityAngular resolutionSystem programmingSoftwareMeasurementImage resolutionMedical imaging2 (number)AreaSoftware maintenanceMetreFrequencyState observerWordComputer animation
04:23
FaktorenanalyseConsistencyMathematical analysisPerformance appraisalIntegrated development environmentSystem programmingOpen setCommunications protocolComputer hardwareSoftwareNatural languageNP-hardPhysical lawAdditionCategory of beingCommunications protocolLimit (category theory)SoftwareMetreMathematical analysisProjective planeProduct (business)Source codeDivisorFitness functionCoordinate systemOpen sourceLocal ringDifferent (Kate Ryan album)Computer hardwareXMLUML
06:22
Open setSystem programmingIntegrated development environmentMathematical analysisCommunications protocolStandard deviationDatabaseData modelData managementDisintegrationSystementwurfPrototypeImplementationPerformance appraisalSoftware testingComputer networkObservational studyMaxima and minimaWater vaporParameter (computer programming)Basis <Mathematik>File formatAreaTime zoneDivisorArmMereologyObservational studyLevel (video gaming)State of matterLoop (music)CASE <Informatik>MetrePerformance appraisalRow (database)SpacetimeNumberBit rateEndliche ModelltheorieNP-hardType theoryData managementProjective planeAreaSystem programmingTime zoneWater vaporMathematical analysisUniverse (mathematics)Lattice (order)WebsiteSoftware testingThomas BayesSquare numberBranch (computer science)Task (computing)Arithmetic meanDecision theoryFreewareStandard deviationDatabaseSubsetDifferent (Kate Ryan album)Artificial neural networkCartesian coordinate systemTerm (mathematics)XML
09:54
Installation artModul <Datentyp>WhiteboardOpen setSpacetimeComponent-based software engineeringIntegrated development environmentSystem programmingMathematical analysisProjective planeTraffic reportingChannel capacityLevel (video gaming)WhiteboardProduct (business)Commitment schemeTelecommunicationType theorySource codeField (computer science)MereologyMultiplication signSystem programmingOpen setDifferent (Kate Ryan album)Software developerComputer animation
11:24
InformationEntropie <Informationstheorie>FaktorenanalysePerformance appraisalComputer fontMathematical analysisSystem programmingOpen setIntegrated development environmentWebsiteTwitterDifferent (Kate Ryan album)SpacetimeSystem programmingInformation securityEntropie <Informationstheorie>Computer animation
11:56
Integrated development environmentMathematical analysisSystem programmingOpen setRing (mathematics)Student's t-testInformation securityIntegrated development environmentInformationSystem programmingQuicksortOpen sourceSanitary sewerSound effectSoftwareOpen setComputer animation
12:42
Price indexStandard deviationSeries (mathematics)FrequencyPressure volume diagramMathematical analysisSystem programmingOpen setIntegrated development environmentSatelliteImage resolutionoutputArchitectureSoftwareTemporal logicEvent horizonCondition numberAreaImplementationWindowAverageDifferent (Kate Ryan album)CurveFrequencyInformationState observerMathematical analysisSystem programmingCalculationFlow separationAreaDegree (graph theory)Exterior algebraStandard deviationSoftwareServer (computing)LaptopOpen sourceService (economics)Fitness functionPlotterEvoluteThermal conductivityUniform resource locatorResultantWeb 2.0Validity (statistics)SummierbarkeitElectronic visual displayFile formatGraph coloringSatelliteMultiplication signCASE <Informatik>AuthorizationAlgorithmOffice suiteSound effectACIDMusical ensembleProjective planeWater vaporChannel capacitySet (mathematics)Machine visionGoodness of fitScripting languageHypermediaVideoconferencingRight angleExtension (kinesiology)Process (computing)WebsiteObservational studyComputer animationProgram flowchart
18:26
Open setSystem programmingMathematical analysisProjective planeExtension (kinesiology)QuicksortSystem programmingComputer networkSoftwareSpeech synthesisWave packetShared memoryComputer animation
19:23
Observational studyLocal ringCommitment schemeInclusion mapStrategy gameGenderInternationalization and localizationSoftware maintenanceIntegrated development environmentSystem programmingOpen setMathematical analysisUniform resource locatorSheaf (mathematics)Selectivity (electronic)Different (Kate Ryan album)Software testingOpen setSpacetimeData miningComputer animation
19:48
Integrated development environmentMathematical analysisSystem programmingOpen setPublic domainProgrammer (hardware)Software frameworkModul <Datentyp>Extension (kinesiology)XML
20:16
Level (video gaming)Confidence intervalSoftwarePairwise comparisonKey (cryptography)Presentation of a groupProcedural programmingDegree (graph theory)Observational studyPoint (geometry)System programmingCASE <Informatik>Error messageGoodness of fitMultiplication sign2 (number)Component-based software engineeringInstallation artDialectLecture/ConferenceMeeting/Interview
Transcript: English(auto-generated)
00:07
Okay, I am the last speaker before lunch session. And thank you for being here, and I will present some of the preliminary results about drought monitoring in Sri Lanka.
00:23
In the context of a project which is funded by a program on the Swiss National Science Foundation. And is it a program for research for development? So the idea is to have some projects share with me in Switzerland universities and university from low-income
00:43
countries develop something that can address some global issues. And our project is about building a system to make monitoring of the weathers in low-income countries using only open technologies.
01:05
It means that we are using the acronym is for ONCE. It means four times open, a non-conventional system for sensing the environment. So which are the four open technologies that we're using?
01:21
We are using open source software. We use open standard. We use open hardware, and then we apply open data. And this is a short introduction to stress the fact that climate changes and drought is a very important problem.
01:43
And this is well reported by the World Meteorological Organization and is also part of the SDGs. And to observe data related to hydroclimatics,
02:03
we can use two different type of sensing. And we can have in-situ observation or remote observation. Remote observation relies on earth observation like satellites, monitoring, and they have some advantages and
02:22
some disadvantages. And the same for in-situ observation. The advantage for satellite data is that they are especially distributed. Of course, you collect images of a large area. A high coverage because they rotate around the globes and collect a very large area of data.
02:43
And it's very important things, they are maintained by external bodies. So generally the user doesn't have to pay for the maintenance of the satellites and the missions, and this is in charge of external bodies.
03:01
And as a drawback, there is a low temporal and spatial resolution because, for example, the returning period over the same area is not always, for example, every ten seconds.
03:20
And also the spatial resolution, even though you can have higher resolution images, still you have some meters at the soil, at the terrain. In-situ observation are generally point-wise measurements. And so are high-temporal resolution.
03:43
Generally with in-situ sensor you can collect high-frequency data. And they generally have high precision. And as a drawback, they have low spatial density.
04:01
Let's say, resolution is not the right word. So in a vast area, you generally don't have so many sensor. And the cost of maintenance of such kind of sensor is on your own. So if you have your own network, you are responsible to pay for the maintenance of the instruments and all of the system, not just use it.
04:24
And there are some factors that affect the limits, the usage of in-situ sensor in low-income countries, but this is also found in most developed countries. And there are limitation cost, because as I said, the cost is on your shoulder.
04:45
And there is hardware cost, but also software cost. General company when they sell you a weather station, they sell you the sensor, and this has a cost. But then sell you also the software to collect data from the sensor.
05:01
And this also is an additional cost. And not always this cost can be sustained. But then there are other problems that are not related to the cost. And are, for example, for accessibility. And the accessibility, for example, to local support, you can buy some instrument from foreign countries, from US.
05:23
But then the project ends, you don't have any local companies that sell these products. And then you cannot have a local support for replacement, for updating the source code, and all these kind of things. The third factor is about interoperability.
05:44
Is that generally companies use closed protocol, so different stations, different sensor have different protocol, different data formats. So it's difficult then to collect all the data and put everything together to make analysis. And generally they are proprietary solution and
06:02
there is no coordination at all. For example, I was in contact with people from the World Bank, and in Cuba they had something like six different monitoring networks for the climate. But they cannot join together because each project implemented its own, but then they are not talking each together.
06:23
And so we tried to address all these things implementing a solution fully open. So that we hope that we can overcome some of these limiting factors. And the partners are a subset from Switzerland, University of Moritova from Sri Lanka, and the Institute of Space Technology from
06:42
Pakistan. And how it work actually in Sri Lanka, which is our main test site for the project, whether data are not integrated and shared. This is at all. Data are mainly available as hard copies.
07:03
So they just write the numbers on papers, book, record book. There is no open standards at all. They never consider any open standard to be used. They're missing any database infrastructure generally. The access to this data is limited and is it not free.
07:22
And very often it's expensive, and so people that need to do any type of analysis on climate don't buy this data because it's too expensive. The stations that are located in Sri Lanka for 99% are manually operated. So it means that there is a people, a person that goes and
07:43
reads manually the level of water in the plume meters and then write on the papers and then transmit the data vocally over the phone to the central station that they will mark on some paper truck. So there is no automation and there is no sharing, and
08:02
the data are also expensive to be used. In the project timeline, there is the setup of the system, then the analysis of the system in terms of sustainability because we want to understand how this can be then applied to others. But then there is also the evaluation of how this data can actually be used
08:23
in reality. So we implemented two different case study application. And one is related to the management of tank artificial basins. The second one is about the monitoring of drought, which I am talking about today. This is the integrated system.
08:41
And how does it work? We have the sensor. This sensor transmit automatically over GPRS to a staging area. These staging area data are validated and goes in the storing area and then they are available for the users.
09:00
And we are in this branch, so we develop some modelings that automatically take this data and then make modelings about drought and then inform the litigation managers that are the main stakeholders of this task. In the study area is the Duroya basin that has a catchment of about
09:24
2,600 square kilometers and it falls under two climatic zone. Part of it is in a dry zone and part of it is in a wet zone. And there is one station belonging to the meteorological department and
09:42
three weather station to the irrigation departments. None of them automatically collect parameters, rainfall, temperature, humidity, and basic weather station data. The system that we developed, we implemented two different type of system.
10:05
Thinking about less professional but more replicable solution, which use just cables. We use Arduino as a main board, so this is open hardware, then we use sensors. And this is wire connected, it's more modular,
10:22
it's used common material to build it up and easy to replace the parts. And the other one is based on a PCB that partnerships with Anca developed. And everything is integrated so it's easier to deploy because you just go on the field, put the board and switch on.
10:41
But this is more professional, you need a higher level of capacity, electronical capacity to implement this and maintain it. And also for the production, you need to rely on a third company that prints the board for you. Everything, of course, is open. And if you go on the website foronsa.org, you can find all the schematics
11:04
of everything's source code and instruction tutorial, everything is open. We try to use all the principle of open science during this project. So we have open government of the project, everybody can join in the project. Everything, all the reports are freely available, tutorial,
11:23
everything is open. And then we decided where to put the stations, and we take into consideration different aspects, the security, the accessibility. We try to have one station for each sub-basin and the open spaces,
11:42
the strength of the signal for transmission, and also the rainfall entropy because rainfall is the main aspect that we want to monitor. So we want to put the station in the right place where the phenomena is more relevant. And at the end, we deploy the third station in the system.
12:02
As you see, 27 weather station and six ring gauges. And these are some pictures of the installation. We generally choose as much as possible schools for two reasons. One is about security. So the stations are in a place where there is always people
12:22
that can guarantee the security of the station. The other reason is that you can see also students, more young students. This is also an opportunity to somehow involve students and let them understand the importance of climatic of the informations
12:40
of the environment where they live. And here we come. What is it about then? We have the station there, we have all the system. The system use an open source standard and use an open source software. Use SOS as a standard software to collect information, the sensor observation service to deliver.
13:02
We have set up the stations, the system, automatically data goes from the station to the system. Then they are validated and they are available on the web using a standard format. And from this, we can implement some algorithms that automatically get the data from the systems and may perform some analysis.
13:24
And of course, we use for the drought the Standard Precipitation Index, which is a recommended index for the World Meteorological Office. How it works briefly, if you take different windows of time, 36 or 120 days, and you calculate
13:44
the sum of this precipitation. And you then display the frequency, the probability curve of this sum of average windows, moving windows sum. Then you come out with a Gaussian curve.
14:03
And depending on where you are, you can detect different severity of drought. This is the basic quick explanation. So time to time, every day, we can recalculate all the statistics and calculate where we are.
14:20
And how can we make this calculation if we need a longer time series, and we just installed the 30 stations. We try to use alternatives data, but data from the Meteorological Office of Sri Lanka was unable to be used because it's too difficult and
14:41
too long time to finish, to digitize. So we use Chirps dataset, which is daily aggregated, globally available. These are from satellite missions and is interpretation. And have 0.5 degrees resolution, and they are available as netCDF.
15:04
So we use this Chirps dataset for historical data in the area. And then for daily datasets, since the installation of the stations, we use the sensor observation service standard, and we perform some requests to collect the data.
15:23
The software, how we implemented it, has a first step to check how does it works and how feasible is it. We use the source as a server of the data. So we use the three main requests. They get capability to get the name of the stations that are in the area.
15:45
They describe sensor to get information about the sensor. And then get observation to actually download the data using some filters. To, for example, download the last week observation about rainfall. And then we use this Chirps dataset and
16:04
we set up a notebook script. Where we can perform the analysis and come out at the end with the plot and the classification of the period if we are in the route or not.
16:23
So basically these are the results. You can see in orange the Chirps dataset of precipitation. And in blue, you can see that there is a quite good fitness of this dataset when there is the overlapping.
16:41
And we could find out three different drought periods. And one occurred in the half of August. And one occurred in the beginning of December. And one classified as a very dry conduction.
17:04
Only one, this one in the end. So I quickly come to the conclusions and also the evolution. Maybe I can explain also the evolution. Here we have tested how the system works.
17:23
We need to make some more validation. So the quality assessment may be using Chirps dataset because it's difficult to make a validation if you don't have data, right? So we can compare the result with the same analysis by just using Chirps dataset.
17:46
We want to compare this system with the station that we have in Ticino and where we can have data so that we can validate the methodology, of course, in a different location. And then we're going to develop a web portal, which will be web-based with a map.
18:05
And the location of each stations that will display different color and different information, where they're clicked to show the plots of drought indexes or not. So that the irrigation department authorities can use this data to actually plan their intervention in case of drought, saving waters, and all these kind of things.
18:27
And yeah, I will finish with a couple of promotion, let's say. The first things that we are applying in the next couple of months for an extension of the project, there are some funds available for
18:43
fostering the adoption of the solution developed in the projects. And so if there is any low-income countries that want to participate in this sort of extension where we propose to make some local training of the trainers. And then they will deploy some station locally and
19:01
they will participate in a sort of creation of a community to these kind of systems. And the final goal is to create for low-income country a fire hydroclimatic data network where people can collaborate and work and share data that are fair. And available also like a system.
19:24
These are some of the selection criteria, but if anybody is interested or has any contact with people in the low-income countries, we are very happy to collaborate with. We tested it mainly in Sri Lanka, but
19:40
we will be very nice to test in different locations worldwide. Something in Africa and South America, for example, or in the Caribbean or something like this. And the last thing is that there is a special issue on Open Science Institute special domain, which I am coordinating. So if the deadline was 31 August, but we are going to extend because we have so far only seven papers.
20:05
So if somebody's interested, there will be at least a couple of months of extensions. Thank you very much. Time for questions.
20:25
No question? Good afternoon. Thank you very much for the good presentation. So approximately how much does it cost to deploy and
20:43
implement all these kind of or similar- Yes. So the cost of the components to build up a station is around 400 US dollar, the cost of the components. Then you have to add the cost of the installations and
21:03
you can have, depending on where you are, the cost of labels. But the comparison that we take as a reference is local stations at the regional level in Switzerland that have a similar accuracy.
21:22
Here, I didn't talk, but there is a paper that shows that this, such kind of system can reach the accuracy of, let's say, second level monitoring networks, which is not the high precise to make climate change studies. But here's, for example, temperature at 0.2 degrees of errors of accuracy.
21:44
So still a good accuracy to handle most of the practical cases. And the comparison is with the station is with some that cost about 7,000, the precision, and this one is going to be about 600 US dollar.
22:02
And then, yeah, it depends on the numbers. Okay, if no more question, we can go for lunch. Thank you very much.