Theoretical Framework
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Quezon City is generally prone to overflowing floods that lead to health and environmental issues. Hence, the researchers decided to create a Smart Drainage Obstruction Sensor (SDOS) to be implemented in Gawad Kalinga (GK), Quezon City. The primary aim is to determine whether the prototype is feasible for public implementation, therefore its sensitivity, key characteristics, and effectivity in flood prevention would be measured. This would add to the common gap in previous studies that only discussed if the prototype operates. The researchers believe that installing this sensor would inform and urge residents to enhance their waste management practices.
Procedural Framework
The research followed an experimental design that utilizes an automation process programmed by coding. Data was gathered through experimentation with the prototype and determining the sensor’s sensitivity in detecting bottles. Consequently, the data was inputted into Jamovi.com to obtain statistical data for analysis. These reflected community waste management practices and aimed to instill the initiative to implement concrete actions to improve it.
The procedural framework shown presents how the researchers intend to proceed with the experiment and test the prototype. It starts with the planning and the gathering of materials, then the construction and coding of the SDOS. The final steps are for the conducting of trials using the prototype and the gathering of data and results for further analysis.
Given that this research focused on testing the prototype’s sensitivity with bottles, no participants were needed for this research. Consequently, there is no sample size. This study utilized the systematic sampling method and data collection by conducting an identical process for each sample. To properly interpret the results, they will be segregated into the class range of the bottles before implementing a histogram to visualize the results.
Procedural Framework
For the analysis of the statistical data of the research, the group employed several statistical methods, specifically, Mean, Standard Deviation, and the One Way ANOVA test. These were used to ensure the efficacy of the SDOS and formulate conclusions about the gathered raw data.
Lastly, in completing the paper, the group observed academic integrity as they ensured that there would be no form of plagiarism in the paper by guaranteeing that all references were properly cited and credited.
Three different-sized bottles were used to simulate wastes in drainage systems to test the frequency at which each bottle was detected by the SDOS. The findings showed that with an average detection rate of 3.92, Bottle B was consistently detected. Bottles A and C, on the other hand, were inconsistently detected. This led to lower average rates of 3.02 and 3.03. These results imply that in comparison to other sizes of bottles or wastes, the SDOS is most effective at detecting wastes similar to the size of Bottle B.
Overall, the results of the study are statistically significant, proving that the implementation of the SDOS will significantly exceed traditional detection and management measures (Ha). In comparison to the traditional methods of determining obstructions that require resources such as time, manpower, and handheld materials, the SDOS effectively and efficiently detects waste within its parameters which notifies residents of the current
condition of the drainage systems and alarms the people via the immediate SMS message notification of obstructions found upon detection. This prompts residents to urgently respond by clearing the blockages, enhancing drainage maintenance, and improving waste management to prevent flooding.
The results show that although the SDOS is effective in detecting obstructions, the size of the obstructions still remains to be one of the main factors that can affect its ability to consistently detect obstructions of different kinds. In addition, the proximity of the ultrasonic sensor and the speed of the obstruction itself are also considered factors as to why the bottles were not detected at certain times.
The results of the study imply that the Smart Drainage Obstruction Sensor is effective in detecting bottles ranging from around 290ml to 1L. Although the data implies an inconsistency in the sensitivity of the SDOS’ sensor, the data is enough to fulfill the objectives of the study. This was achieved through the help of the three main components of the prototype; the ultrasonic sensor, float switch, and GSM Module. The ultrasonic sensor, together with the Arduino Uno is a huge help in enhancing flood prevention measures as it effectively detects blockages in drainage systems in real-time. The float switch on the other hand is responsible for detecting water levels that can help local authorities detect possible drainage overflow and flooding. Then, the data collected from both sensors will be transformed into alert messages through the help of the GSM Module. With that being said, the SDOS as a whole is effective in relaying messages on the current status of drainages which would be a great help in making effective and appropriate flood prevention measures according to the live data collected from it.
This study shows slight differences with existing research studies regarding the prototype set-up, type of sensor, type of waste measured, and data collection procedure. However, the findings are generally similar to each other. All of the studies used as references by the researchers have concluded that the ultrasonic sensor is the best to be used for this kind of prototype due tao its ability to detect materials of any kind. In this study, the researchers focused on the prototype’s sensitivity in detecting plastic bottles of varying sizes, while other studies focused on using paper, food wrappings, and other forms of waste commonly found in the streets and drainage systems. Since none of the existing research studies made use of a float switch to monitor liquid levels, the researchers decided to incorporate this device to further enhance the abilities of the SDOS prototype. The results of this study were able to prove the effectiveness of the float switch in monitoring sewage water levels in drainage systems to prevent overflows and backflows. However, just like any research out there, one limitation of this study is the channel through which the alert messages were sent. Originally, the researchers planned to use the Blynk app as the main application to receive the alert messages. However, due to coding limitations, the researchers opted to use a GSM module and Arduino Uno board instead as it was easier to use compared to the Blynk app which needed a unique and more complicated Wifi board called NodeMCU.
With these, the researchers recommend that future researchers try different sensors and set-ups to figure out which works best. Second, since the researchers in this study only measured the sensitivity of the ultrasonic sensor on bottles, it is recommended that others measure unique types of waste. Some of which could be aluminium foil, styrofoam, drywall, etc. Lastly, with these differences applied, future researchers would need to create or find different data collection procedures appropriate for the set-up and object/s aimed to be measured.
Samantha
"Nocturnal Writer"
Ianna
"Silent Slayer"
Chirsten
"Proofread Queen"
Juanna
"Go with the Flow"
Elaena
"Waterbender"
Elisha
"Arduino Mother Divine"
References
References
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