Firewatch Monitoring Sensor Development in Indonesia
"Learn about the progress of firewatch monitoring sensor development in Indonesia, including background, challenges faced, collaboration efforts, and the need for automated weather stations, prediction models, and social awareness. Explore the connection between Indonesian fires and meteorological conditions, aiming to improve early detection and response to forest fires."
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Presentation Transcript
Progress of development firewatch monitoring sensor in Indonesia Basuki Suhardiman basuki@Itb.ac.id Institut Teknologi Bandung
Outline Background Design Model Protyping and Testing model Comparation model Works and collaboration
Background In 2015 , there were huge fire in forestry and impact many peoples around the world There was limited information and data related to the fire in Forestry especially related with public data Fire in the forestry No AWS No sensor connected The MODIS Data came from satellite, within 24 hours Land Clearing on Coconut palm oil plantation More than 10 Million Hectar on Indonesia
Meteorogical,Climatological and Geophyics Agency Limited AWS (Automated Weather Station) own by Government (BMKG) Limited publish and public data (online) Not yet open the data http://202.90.199.132/aws-new/
Globalfires report on March 2019 http://fires.globalforestwatch.org/report/index.html#aoitype=GLOBAL&reporttype=glo balcountryreport&country=Indonesia&dates=fYear-2019!fMonth-3!fDay-18!tYear- 2019!tMonth-3!tDay-25
IDeate By combining the satellite data with meteorological records, Field established that Indonesian fires often occur in association with the ultra-dry conditions in the region that accompany El Ni o, a major cyclical weather system. These dry out the underlying peat to the point at which it can catch fire and can t be extinguished until monsoon rains arrive months later. A next step might be to refine the predictions further by including the combined effects of high temperatures and low humidity on peat moisture, says Field. He also found that less than 4 millimeters of rain per day during the annual dry season, from August to November combined with El Ni o conditions seems to be a tipping point beyond which fires can to suddenly take off. https://www.newscientist.com/article/2099496-indonesian-fires-sent-huge-smoke-plume-halfway-around-the- globe/?utm_medium=Social&utm_campaign=Echobox&utm_source=Facebook&utm_term=Autofeed&cmpid=SOC%7c NSNS%7c2016-Echobox#link_time=1470082665
Define What we (Indonesia) need it ? Automated Weather Station Rainfall Temperature Humidity Air quality Index and Polution Standard index measurements NOx, SOx , CO , NH3 , O3 , CH4 and PM 2.5 (10) Sensors and sensors network Prediction models (computing capacity) Socialize to peoples https://en.wikipedia.org/wiki/Pollutant_Standards_Index
Contraints/obstacle In the field There is no power Limited converage by telecommunications system No sensor available Source of the fire More than 50 km from nearest town
Design Model Build prototype for AWS+sensor base on Arduino Board (I/O) Communication GSM/GPRS/Wifi Ethernet USB/Storage System Sensors Temperature Wind speed Wind direction Humidity Rainfall rate CO CH4 ? Board Mqtt client Mqtt server Repository server Raw data Clean data Build the website API etc
Prototype 1.0 (2017) Forestry (School) TCP/IP GSM/GPRS Modul ITB Web Server For collecting data And Portal Arduinno Microcont roller GPS modul USB /Storage AWS -Wind Direction -Wind speed -Temperature - Humidity - Rain fall CO sensor And other sensors Middle Ware Network of Science Community NASA/NOAA web server ,etc
IOT Architecture (Design Model) Choose of the purpose
Running Operational Test Start on July 2017 Nov 2017 10 Variables measures Data per minutes Cause of hardware failure AWS sensor on Humidity sensors, CO2 dan CH4 Started again on Feb 2018 Problems with CO2 sensor , CO and Methane (CH4)
Problem Occurred 2019 , fix the sensor temperature/humidity sensor Modified and Put two Humidity and temperature sensor Sensors Availabilty in continous operation in long term Humidity sensor has broken Calibration methods Compare with other Sensor network Standard Calibration Methane (CH4) CO CO2 Sensor1 Temp, Hum Sensor2 Temp, Hum
Comparision to other model Interlab AIT (Thailand) build similar Sensor Base on Udoo microcontroller PM 2.5 Temperature and relative humidity Communication wifi Web base enable configuration Put the data on canarin.net/seahazemon/map.html
Sea-Hazemon Sensor 3 Sensor node PM 2.5 microgrm/m3 Temperature Relative Humidity (RH) Wifi enable 1 sensor put in Bandung 2 sensor put in Sumatra s Area Have to connect to Internet via Wifi
Status of Sensors AIT 34 Sensor located in Bandung AIT 35 Located in Palembang ( Sumatra) 1 sensor is not yet working ( Internet accessibility problem) https://canarin.net/seahazemon/map.html
Sea-hazemon Starting in Oct 2018 Put in Bandung (ITB) (Java Area) AIT35 Put in Palembang (Regional Ministry office for forestry and env) (Sumatra Area) AIT36 Put in Riau area (not yet operation) , problem with Internet connection
Comparing technology AWS-ITB Arduinno Microprocessor 8 Input digital 8 Input analog AWS (Automatic Weather Station ) Windspeed Wind direction Rainfall cauge (rate) GPS GPRS/GSM for telecommunication Sensors CH4 , CO SeaHazemon Udoo Microprocessor Temperature Relative Humidity Pm 2.5 micron Wifi enable ( connect to Internet) Web Interface Ready for many sensors
canarin.net/seahazemon/map. html se information All information is need was ready on the web Infographic enable
Problems occurs (Humidity) Humidity sensor There is no data after 3 month operation ( Failure of sensor) Problem with Humidity /Temperature sensor Same sensor problem for Humidity with ITB s prototype
Future Work For SeaHazemon AIT with support from TEINCC will provide 50 sensors next year to Indonesia Discuss about the location Discuss about the possible problems Telecommunication obstacle Internet Obstacle Sensor availability Accuracy of the data Discuss the format of the raw data Discuss the repository server Operational Issues on the sensor network Reliability and availability of the sensor Calibration method, and verification of the data
Challenge for development and operational Sensor network. Calibration of the equipment Sensor Reliability Format of the raw data (sensor) Repository for the data Web Portal API
Works and collaboration Building Portal for collacting the data for the AWS Prototype.1 Build the Repository server Raw Sensors data Build the database for AWS Prototype.1 Collaboration with Ministry of Forestry and Environment (Indonesia) Put the Prototype.1 and Sea-hazemon on the field (Wild fire areas)
Disaster Mitigation Competence Center (DMCC) Focus on Case Study for Simulation on Dust Transportation around Indonesia Provide some data Dissemination on education and research community Tsunami Impact analysis using iCOMCOT portal
Future (Next) works Build the webbase enable and repository data Development with Sea-hazemon project for deployment sensor around Indonesia for Air Quality monitoring (PM 2.5) Development and deploy AWS(Protype 1.0) for delivering data and build model with Ministry of Forestry and Environment Build the case study with DMCC (facilitate for I-Comcot Tsunami and WRF-chem Simulation)
Thank you ASGC, DMCC+ and EOSC-hub
