第31回日本情報地質学会講演会 講演要旨集 21-22頁 2020年

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Development of Geo-IoT Platform for Water Quality Monitoring空白
Niroshan BANDARA , Mitsunori UEDA* and Venkatesh RAGHAVAN***

空白*Graduate School for Creative Cities, Osaka City University,
3-3-138 Sugimoto, Sumiyoshi-ku, Osaka 558-5858, Japan. E-mail: niroshansnj@gmail.com**Graduate School of Engineering, Osaka City University, 3-3-138 Sugimoto, Sumiyoshi-ku,
Osaka 558-5858, Japan.

Key words: Water Quality, Mobile Data Collection, Open Data Kit (ODK), IoT

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１．Introduction
Water Quality Monitoring (WQM) is identified as amajor discipline in the field of environmental monitoring.
Numerous challenges in WQM have identified in recent
scientific literature (Kamaruidzaman et al., 2019).
Conventional methods for WQM are time consuming andexpensive task (Chen et al., 2020). Proliferation of mobile
phones and development of the Internet of Things (IoT)
have increased ubiquity of data collection by integrating
sensors and smartphones. Major development from thisstudy is to develop a platform by integrating mobile
phones and sensors which can measure the chemicalparameters of water. The quality of water bodies such asrivers, ponds, and lakes can be evaluated by monitoring
parameters such as pH, temperature, and Electrical
Conductivity (EC), which are the most commonly usedindicators to monitor water quality. These parameters
are recoded along with the location information. The
system consists of spatial data acquisition, analysis andsharing and by following OGC standards which enables
spatial data proliferation.

２．Method and System Components
Open Data Kit (ODK, https://opendatakit.org/) thatprovides tools to facilitate collection and transmission ofgeoreferenced data to a centralized server is used for datacollection and aggregation. ODK consists of threemodules; namely ODK Build, ODK Collect and ODKAggregate. ODK collect is capable of recording GPSlocation, text, multi-media content such as image, video,audio and barcodes. External sensor recordings areinserted to ODK Collect using Sensor app(https://github.com/niroshansb/sensors_app) which is an
Android mobile application developed by this study.
The system consists with sensor device which includes
microcontroller, sensor signal converter modules andBluetooth module. Components and the connectivity ofdevices are illustrated in Figure 1. Conventional glass
electrode sensor has been used to measure pH (StockKeeping Unit (SKU): SEN0161 from dfrobot.com). ThepH sensor is capable of measuring values from 0 to 14
with ±0.1 (25 ℃) accuracy. The DS18B20 temperature
sensor (SKU:DFR0198 from dfrobot.com) which provides
9 to 12-bit temperature readings with ±0.5℃ accuracy

for －55 ~ 125℃ ranges over a 1-Wire communicationbus is selected to measure the temperature. EC ismeasured using analog electrical conductivity meter(SKU:DFR0300 from dfrobot.com). EC k=1 sensor isselected due to its suitability in water culture,
aquaculture for inland water bodies with the detection
range of 0 to 2,000mS/m along with ±5% accuracy(DFRobot EC Sensor wiki, 2020). Recommended
detection range for EC sensor is 100~1500 mS/m.

Figure 1: Schematic diagram of system components

Obtainedsensor
measurement
valuesofphysiochemical parameters of water and were firsttransferred via Bluetooth from sensor device to themobile phone. Transferred data were inserted into thefields in ODK Collect form. The data is achieved in ODKAggregate using the PostgreSQL database backend. AWeb-GIS client was implemented for visualization of fielddata with the integration of GeoServer and OpenLayers
JavaScript library. Furthermore, the collected data ispublished using OGC WFS standard. The workflow of thedeveloped system is elaborated in Figure 2.

Figure 2: Data flow of developed system

22(a)(b)

pH Values

Electric Conductivity

60

109

R² = 0.9247

50

DFRobot/Atlas

R² = 0.7882
R² = 0.8191

8740

R² = 0.5518

302066789101020253035Horiba
pH values compari son with Data set -240455055Horiba

pH values compari son with Data set -3(c)EC values with DFRobot productEC values with Atlas productTemperature
36
R² = 0.96

34DFRobot

Laboratory experiment was carried out to evaluate
system performance and accuracy as shown in Figure 3.
Ten water samples were collected randomly from ponds
and Yamato river near Osaka City University. Thesensors are calibrated as per the instructions given by
the manufacturer. Values were recorded using ODKCollect app. Completed ODK Collect forms were uploaded
to the server. Furthermore, same samples were measured
using Horiba-D-74 water quality instrument. The
measurements show high coefficient of determination(R2) for both pH (0.99) and EC (0.89). The experiment
reveals the suitability of the system for field datacollection.

DFrobot

３．Performance and Accuracy Assessment

R² = 0.9628

323028262424262830323436

HoribaTemp from EC probe

Temp from pH probe

Figure 5: (a) pH values,(b) Electric conductivity in mS/m, (c) Temperature in Celsius

５. Conclusion

Figure 3: Experiment setups in the laboratory

4. Data Collection and ResultsThe system is deployed to measure physiochemicalparameters of water in different locations along theYamato river, as a field experiment. Total 20 samples
were measured on the site from both the developed
system and Horiba-D-74.
Locations and data collection method is shown inFigure 4. Samples were collected in a standard method
for water sample collection. The correlation between pH,
EC and temperature values obtained from Horiba-D-74
and developed system is illustrated in Figure 5. pHvalues are varying from 7 to 9 while EC has a significant
variation. The temperature show consistent values for all
20 samples. Considerable changes and low R2 (0.55) isnoticed while measuring the EC with DFRobot sensor
due to degradation of probe. Therefore, the EC sensor
was replaced with Atlas Scientific EC probe(ENV-40-EC-K1.0 from atlas-scientific.com) which has±2% accuracy for 0.5 to 20,000 mS/m measuring range
and data were collected again from the same experimentsites. Data from Atlas EC probe consistent measurement
and high R2 (0.92) compared to Horiba-D-74.
(a)

(b)

This study was conducted to identify the user
experience and limitations of the developed system whenusing for field data collection. Developed system has
several advantages such as capability of visualizing
readings on the interactive web interface based on
different locations near real-time. It enables monitor andidentify the changes from remote locations to the field.
Moreover, sensors can be replaced or added easily to thesystem. Additionally, data can be recorded and sent onsite. Similarly, data can be recorded offline as well. In
such situation, data can be transmitted when theinternet is available. More importantly, this system hasthe capability of changing and adding parameters in datacollection from both human observation and externalsensor input data simultaneously.

6. Future Development

This system allows the fast and automated data
aggregation, sharing and visualization. Study identified
that in-situ water quality monitoring is not adequate toinvestigate water quality changes. Therefore, thecapability of the system will be extended by adding
continuous water quality measuring devices. The data
will be recorded in the same system from both in-situ andcontinuous monitoring.

Bandara,N.,

Reference

Raghavan,V.,

Yoshida,D.

(2016)Development of Field Data Monitoring andEvaluation Platform using Customizable Mobile
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Chen F. L., Yang B. C., Peng S. Y., Lin T. C., (2020)

Applying a deployment strategy and data analysis
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Sensor Networks, vol.16, no.6.
Kamaruidzaman N. S., Rahmat S. N., (2020) Water
Figure 4: (a) Sample collections and measuring method(b) Data collection points displayed on Web interface-GIS Client

Monitoring System Embedded with Internet ofThings (IoT) Device: A Review, IOP Conf. Series:

Earth and Environmental Science, vol.498, pp.1206812074.