In the Philippines, flooding, which is typically produced by excessive rainfall and strong seas, is one of the most prevalent natural occurrences. This natural calamity cannot be avoided but the good thing is, we can practice ourselves to be prepared for it. After conducting an analysis regarding the needs of people residing in Barangay Frances, Calumpit, Bulacan, it was then decided to develop a project that can help lessen the difficulty they are experiencing when they evacuate. The system uses image processing as its flood detection method. It also uses several sensors for different purposes to make it more reliable to the users. These sensors used are the rain gauge, float switch, and flow rate meter sensor. It measures two of the important parameters in flood detection namely precipitation rate (mm/hr), flood level (ft), and the flow rate (L/hr). The data accumulated by the sensors are sent immediately to the Android application so it can be used by people living in the area to monitor the flood levels in real time. To measure the reliability of the system, the flood level taken from the automated system and conventional method were compared. A small mean squared error (MSE) of these 2 data which is 0.125 was achieved.
Keywords: Image processing; flood monitoring; flood detection; multiple linear regression; real time.Shrimp growth monitoring system using image processing techniques
[1] “ Floods ” , Philippine Atmospheric Geophysical and Astrono-mical Services Administration, http://bagong.pagasa.dost.gov. ph/learning tools/floods?fbclid=IwAR2gwRSs PZxKDZGGm_Eg7d1CjhnNXVWlooSLfnF_ h6IZ38DwCWG9g8utnUY, August 2020
[2] B. Bennett, M. Leonard, Y. Deng, and S. Westra, “An empirical investigation into the effect of antecedent precipitation on flood volume,” Journal of Hydrology, vol. 567, 2018, pp. 435-445. doi: 10.1016/j.jhydrol.2018.10.025.
[3] “About Project NOAH”, Official Gazette of the Republic of the Philippines, https://www. officialgazette.gov.ph/programs/about-project-noah/, July 2019
[4] J. R. Santillan, E. C. Paringit, R. V. Ramos, J. R. T. Mendoza, N. C. Española, and J. Alconis, “Near Real time flood extent monitoring in Marikina river philippines: Model parameterisation using remotely-sensed data and field measurements,” in 33rd Asian Conference on Remote Sensing 2012.
[5] D. Tang, F. Wang, Y. Xiang, H. You and W. Kang, “Automatic Water Detection Method in Flooding Area for GF-3 Single- Polarization Data,” in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018, pp. 5266-5269. doi: 10.1109/IGARSS.2018.8517886.
[6] K. Hiroi and N. Kawaguchi, “FloodEye: Real-time flash flood prediction system for urban complex water flow,” in 2016 IEEE SENSORS, 2016, pp. 1-3, doi: 10.1109/ICSENS.2016.7808626.
[7] H. B. Baydargil, S. Serdaroglu, J. Park, K. Park and H. Shin, “Flood Detection and Control Using Deep Convolutional Encoder-decoder Architecture,” in 2018 International Conference on Information and Communication Technology Robotics (ICT ROBOT), 2018, pp. 1-3. doi: 10.1109/ICT ROBOT.2018.8549916.
[8] K. P. Menon and L. Kala, “Video surveillance system for realtime flood detection and mobile app for flood alert,” in 2017 International Conference on Computing Methodologies and Communication (ICCMC), 2017, pp. 515-519, doi: 10.1109/ICCMC.2017.8282518.
[9] C. Moreno, R. Aquino, J. Ibarreche, I. Pérez, E. Castellanos, E. Álvarez, R. Rentería et al., “RiverCore: IoT device for river water level monitoring over cellular communications,” Sensors, vol. 19, no. 1, 2019. doi: 10.3390/s19010127.
[10] K. Sathita, H. Ochiai and H. Esaki, “RainWatch Project: Location-Awared Real Time Detection and Notification of Rain on Internet-Based Sensor Network,” in 2009 Ninth Annual International Symposium on Applications and the Internet, 2009, pp. 259-262. doi: 10.1109/SAINT.2009.59
[12] N. M. Arago, A. C. Thio-Ac, M. C. Apostol, I. J. E. De Guzman, A. E. D. Reyes, K. G. Rodriguez, and R. A. B. Toring, “Development of an Automated Cows In-Heat Detection and Monitoring System Using Image Recognition with GSM Based Notification System,” Journal of Computational Innovations and Engineering Applications (JCIEA), vol. 4, no. 2, 2020, pp. 9-15.
[13] L. K. S. Tolentino, J. W. F. Orillo, P. D. Aguacito, E. J. M. Colango, J. R. H. Malit, J. T. G. Marcelino, A. C. Nadora, and A.J.D. Odeza, “Fish freshness determination through support vector machine,” Journal of Telecommunication, Electronic and Computer Engineering (JTEC), vol. 9, no. 2-5, 2017, pp. 139-143.
[14] L. K. S. Tolentino, C. P. De Pedro, J. D. Icamina, J. B. E. Navarro, L. J. D. Salvacion, G. C. D. Sobrevilla, A. A. Villanueva, T. M. Amado, M. V. C. Padilla, and G. A. M. Madrigal, “Weight prediction system for nile tilapia using image processing and predictive analysis,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 8, 2020, pp. 399-406.
[15] I. C. Navotas, C. N. V. Santos, E. J. M. Balderrama, F. E. B. Candido, A. J. E. Villacanas, and J. S. Velasco, “Fish identification and freshness classification through image processing using artificial neural network,” ARPN Journal of Engineering and Applied Sciences, vol. 13, no. 18, 2018, pp. 4912-4922.
[16] R. O. Serfa Juan and J. Kim, “Photovoltaic
Cell Defect Detection Model based-on Extracted Electroluminescence Images using SVM Classifier,” in 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2020, pp. 578-582. doi: 10.1109/ ICAIIC48513.2020.9065065
[17] B. Kim, R. O. Serfa Juan, D.-E. Lee, and Z. Chen, “Importance of Image Enhancement and CDF for Fault Assessment of Photovoltaic Module Using IR Thermal Image,” Applied Sciences, vol. 11, no. 18, 2021, pp. 1-23.
[18] B. Kim, S.-W. Choi, G. Hu, D.-E. Lee, and R. O. Serfa Juan, “Multivariate Analysis of Concrete Image Using Thermography and Edge Detection,” Sensors, vol. 21, no. 21, 2021, pp. 1-24.
[19] S. Saifullah, R. I. Mehriddinovich, and L. K. Tolentino, “Chicken Egg Detection Based-on Image Processing Concept: A Review,” Computing and Information Processing Letters, vol. 1, no. 1, 2021, pp. 31-40.
[20] S. Saifullah, and A. P. Suryotomo, “Thresholding and hybrid CLAHE-HE for chicken egg embryo segmentation,” in 2021 International Conference on Communication & Information Technology (ICICT), pp. 268-273, 2021.
[21] A. Yudhana, S. Sunardi, and S. Saifullah, “Segmentation comparing eggs watermarking image and original image.” Bulletin of Electrical Engineering and Informatics, vol. 6, no. 1, 2017, pp. 47-53.
[22] S. Saifullah, S. Suhirman, A. T. Hidayat, and R. H. P. Sejati, “Otsu Method for Chicken Egg Embryo Detection based-on Increase Image Quality,” MATRIK: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 2, 2022, pp. 417-428.
[23] J. W. Orillo, J. Dela Cruz, L. Agapito, P. J. Satimbre and I. Valenzuela, “Identification of diseases in rice plant (oryza sativa) using back propagation Artificial Neural Network,” 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2014, pp. 1-6, doi: 10.1109/ HNICEM.2014.7016248.
[24] J. W. Orillo, G. J. Emperador, M. G. Gasgonia, M. Parpan and J. Yang, “Rice plant nitrogen level assessment through image processing using artificial neural network,” 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2014, pp. 1-6, doi: 10.1109/ HNICEM.2014.7016187.
[25] R. S. Concepcion II, S. C. Lauguico, J. D. Alejandrino, E. P. Dadios, and E. Sybingco, “Lettuce Canopy Area Measurement Using Static Supervised Neural Networks Based on Numerical Image Textural Feature Analysis of Haralick and Gray Level Co-Occurrence Matrix,” AGRIVITA, Journal of Agricultural Science, vol. 42, no. 3, 2020, pp. 472-486.
[26] R. Concepcion II, E. Dadios, E. Sybingco, and A. Bandala, “A Novel Artificial Bee Colony-Optimized Visible Oblique Dipyramid Greenness Index for Vision-Based Aquaponic Lettuce Biophysical Signatures Estimation.” Information Processing in Agriculture, 2022, pp. 1-22.
[27] L. K. S. Tolentino, S. O. Belarmino, J. G. N. Chan, O. D. Cleofas Jr, J. G. M. Creencia, M. E. L. Cruz, J. C. Geronimo, J. P. M. Ramos, L. A. C. Enriquez, J. F. C. Quijano, and E. Fernandez, “IoT-based Closed Algal Cultivation System with Vision System for Cell Count through ImageJ via Raspberry Pi,” International Journal of Advanced Computer Science and Applications (IJACSA) 12, no. 7, 2021, pp. 287- 294.
[28] M. H. Hamd and S. K. Ahmed, “Fourier descriptors for iris recognition,” International Journal of Computing and Digital Systems, vol. 6, no. 05, pp. 285-291, 2017
[29] Y. M. Akbar, A. Musafa, and I. Riyanto, “Image processing-based flood detection for online flood early warning system,” 2017. doi:10.31227/osf.io/ayn2c
[30] P. V. K. Borges, J. Mayer, and E. Izquierdo, “A probabilistic model for flood detection in video sequences,” in 2008 15th IEEE International Conference on Image Processing, pp. 13-16, 2008
[31] C. L. Lai, J. C. Yang, and Y. H. Chen, “A real time video processing based surveillance system for early fire and flood detection,” in 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007, pp. 1-6, 2007.
[32] T. M. Amado and F. R. G. Cruz, “Wireless Flood Monitoring Using Integrated Hydrological Sensors and Flood Prediction Via Artificial Neural Network,” in 8th AUN/SEED-Net Regional Conference on Electrical and Electronics Engineering, 2015, pp. 32-33.