Abstract
Most developing countries depend on conventional water quality monitoring methods which are usually expensive, complicated, and time-consuming. In recent years, stationary and portable water quality monitoring and a mobile surface vehicle have increased the utilization of on-site water measurements and monitoring. The first has the disadvantage of small coverage area while the second has its cost and operational complexity. This paper addresses these issues by placing materials and equipment used in fixed online water quality monitoring and using a customized and low-cost unmanned surface vehicle. The measurements are taken automatically on the equipment onboard the unmanned surface vehicle (USV), transmitted wirelessly to a PC-based remote station or nearby stations and saved there in a dedicated database. The overall system comprises a commercial water quality sensor, a GSM and Zigbee module for a wireless communication system, a low-cost mobility platform, and the location/positioning system. During testing, all captured data like water quality parameters, location, and other essential parameters were collated, processed and stored in a database system. Relevant information from the USV can be viewed on a smartphone or a computer. The USV was also tested to conduct unmanned water quality measurements using the pre-inputted navigation route which shows a good result in navigation and data transmission. Water bodies with calm water such as lakes and rivers can use the USV, in a stand-alone mode or as a part of a networked sensor system.
Highlights
Because of population increase, industrialization and climate change, water demand is increasing at an alarming rate [1,2,3]
There is a growing demand to implement a mechanism to enhance the effectiveness of existing water quality monitoring methods
This study focuses on the design of an unmanned mobile vehicle for water quality monitoring with consideration of the actual requirements and scenarios of the target area which is Lake Mainit, Philippines
Summary
Industrialization and climate change, water demand is increasing at an alarming rate [1,2,3]. Water quality information gathering uses traditional or manual methods for various reasons ranging from lack of technology capacity, human resources and financial constraints [5,6,7]. These techniques or methodologies are usually accurate and inexpensive, but suffer from various disadvantages like the loss of vital information during transport, limited samples, and cost-effectiveness. They cannot perform real-time monitoring which could mitigate or prevent uncertain happenings in a particular area [8]
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