Design of a Remote Real-time Groundwater Level and Water Quality Monitoring System for the Philippine Groundwater Management Plan Project

Recent technological advances allow us to utilize remote monitoring systems or real-time access of data. While the use of remote monitoring systems is not new, there are still numerous applications that can be explored and improved on, one such is groundwater level and quality monitoring. In the Philippines, the extraction of groundwater for both domestic use and industrial use are manually monitored by the government’s concerned agency and is done at least once per year. With this current setup, the real and significant state of the groundwater is not reflected in a way that is most valuable to the government and to the community. This project aims to design and develop a remote real-time groundwater level and quality monitoring system. It is intended to provide quantitative data for policy makers in addressing recurrent water shortages in the Philippines. This paper discusses the designed system composed of three modules: power module, sensors and control, and data visualization. These three modules provide real-time data from far-flung locations while being energy-sustainable. Dry runs of the system in a controlled environment yielded excellent results — average data accuracy of 96.63% for all six (6) groundwater quantity and quality parameters namely: pH, temperature, electrical conductivity, total dissolved solids, salinity, and static water level (SWL), and 90.63% data transmission reliability. Initial deployment of the system on one of the groundwater monitoring well in Metro Manila, Philippines returned a 91.16% data transmission reliability. The system is currently installed in 20 groundwater monitoring sites all-over the Philippines and is scheduled for more installations

Software and Data Visualization Platform for Groundwater Level and Quality Monitoring System

Rapid urbanization and increasing population come with the increased extraction and use of groundwater resources. To track the effect of these activities on groundwater level and quantity; a system for real-time monitoring is devised. In this paper; we present a software system design that enables a locally-developed groundwater level and water quality monitoring hardware setup to gather water quality parameter data; send it to a cloud server; and present organized data for better visualization. The hardware setup consists of an Arduino microcontroller. Upon deployment; the hardware setup is linked to an Android application that connects to the web-based platform

Short Term Wind Speed Forecasting : A Machine Learning Based Predictive Analytics

The challenges posed by the intermittence and uncertainty of renewable energy due to its variability and limited storage require accurate forecasts for economies looking to source a significant amount of energy from renewables. We report on the use of several supervised learning models such as Random Forest, Extremely Randomized Trees, Support Vector Regression and k-Nearest Neighbors Regression to forecast ahead of time wind speed measurements using data from the wind met masts located at Buguey, Ballesteros and Sta. Ana, Cagayan. Results show that in terms of predicting the next hour wind speed measurements for one day, the k-NNR model outperforms the other three models while the ET model have shown the highest predictive performance among the four models in prediction of the next hour wind speed measurements for one month and 20% of the total data. It is anticipated that the proposed ET model can be used as an effective wind speed prediction model as well as the k-NNR model. The common perception by energy companies in ASEAN that RE output is unpredictable needs to be rethought in the sight of the new AI techniques

Wireless Sensor Network for Soil Monitoring

This Wireless Sensor Network (WSN) for Soil Monitoring is a system consisting of nodes with sensors and telemetry capabilities. This system is meant for deployment in agricultural applications, specifically, in banana plantations affected and unaffected by Fusarium oxysporum f. sp. cubense Tropical Race 4 (TR4). We focused on monitoring soil parameters such as pH, temperature and moisture. Other environmental parameters such as air temperature, air humidity and ambient light were gathered. Each sensor node uses a GSM data-transmission module for more stable and robust method even in far-flung areas in the Philippines. Raw data from the sensor nodes are stored in a web server for processing and data visualization. Reliability of daily transmission is 87.28%

Design of an Automatic Temperature Screening System for Elevated Skin Temperature with Information Logging Capability

With the outbreak of the highly-contagious SARS-CoV-2 virus and its accompanying coronavirus disease 2019 (COVID-19), many government agencies adopted contact tracing to measure and mitigate the spread of the virus. Contact tracing aims to keep track of the individual\u27s movements and activities and identify all those who they come in contact with. This study is focused on designing a cost-effective, efficient, and accurate system for information logging and temperature screening with a complementary contact tracing feature. The system provides an automated, safe, and physical-distance-aware alternative to manual temperature measurement and data logging practiced by most commercial establishments. The system uses an Arduino and a Raspberry Pi, along with infrared temperature sensors utilizing proper calibration methods to yield temperature reading difference of 0.1 - 0.3 degree-Celsius taken at 10 cm distance. User identification is done by reading either specifically-registered RFID tags or system-generated identity-QR code. Temperature is subsequently read, date and time stamped, and logged into the system. This allows for automated and exact association of the user logged information with their corresponding temperature

Design of an Automatic Temperature Screening System for Elevated Skin Temperature with Information Logging Capability

With the outbreak of the highly-contagious SARS-CoV-2 virus and its accompanying coronavirus disease 2019 (COVID-19), many government agencies adopted contact tracing to measure and mitigate the spread of the virus. Contact tracing aims to keep track of the individual\u27s movements and activities and identify all those who they come in contact with. This study is focused on designing a cost-effective, efficient, and accurate system for information logging and temperature screening with a complementary contact tracing feature. The system provides an automated, safe, and physical-distance-aware alternative to manual temperature measurement and data logging practiced by most commercial establishments. The system uses an Arduino and a Raspberry Pi, along with infrared temperature sensors utilizing proper calibration methods to yield temperature reading difference of 0.1 - 0.3 degree-Celsius taken at 10 cm distance. User identification is done by reading either specifically-registered RFID tags or system-generated identity-QR code. Temperature is subsequently read, date and time stamped, and logged into the system. This allows for automated and exact association of the user logged information with their corresponding temperature