IEEE Systems, Man and Cybernetics Magazine - October 2022 - 33

roc Plot
puter science and engineering at Vellore Institute of Technology,
Vellore-632 014, Tamil Nadu, India.
1
0.8
0.6
0.4
0.2
0.2
0.4
0.6
False-Positive Rate
Random Prediction (AUROC = 0.5)
KNN (AUROC = 0.978)
SVM (AUROC = 0.984)
DT (AUROC = 0.788)
RF (AUROC = 0.927)
Figure 9. An ROC plot of Konkan and Goa.
roc Plot
1
0.8
0.6
0.4
0.2
0.2
0.4
0.6
False-Positive Rate
Random Prediction (AUROC = 0.5)
KNN (AUROC = 0.983)
SVM (AUROC = 1)
DT (AUROC = 0.842)
RF (AUROC = 0.994)
Figure 10. An ROC plot of Andhra Pradesh.
About the Authors
R. Balamurugan (balacse05@gmail.com) earned his Ph.D.
degree in data mining from Anna University, Chennai, India,
in 2016. He is an associate professor of computer science
and engineering at Vellore Institute of Technology, Vellore-632
014, Tamil Nadu, India.
Kshitiz Choudhary (kshitiz2603@gmail.com)
earned his B.Tech. in computer science and engineering
from Vellore Institute of Technology, Vellore-632 014,
Tamil Nadu, India. He is affiliated with the same
institution.
S.P. Raja (avemariaraja@gmail.com) earned his Ph.D.
degree in image processing from Manonmaniam Sundaranar
University, Tirunelveli. He is an associate professor of com0.8
1
0.8
1
References
[1]
M. Zarei, O. Bozorg-Haddad, S. Baghban, M. Delpasand, E. Goharian, and H. A.
Loáiciga, " Machine-learning algorithms for forecast-informed reservoir operation
(FIRO) to reduce flood damages, " Sci. Rep., vol. 11, 2021, Art. no. 24295.
[2] " Using AI to predict floods and save lives. " INDIAai. https://indiaai.gov.in/casestudy/using-ai-to-predict-floods-and-save-lives
(Accessed: 2022).
[3] A. O. Hashi, A. A. Abdirahman, M. A. Elmi, and S. Z. Mohd, " A real-time flood detection
system based on machine learning algorithms with emphasis on deep learning, " Int. J. Eng.
Trends Technol., vol. 69, no. 5, pp. 249-256, 2021, doi: 10.14445/22315381/IJETT-V69I5P232.
[4] H. Abdul-Kader and M. Mohamed, " Hybrid machine learning model for rainfall
forecasting, " J. Intell. Syst. Internet Things, vol. 1, no. 1, pp. 5-12, 2021, doi: 10.54216/
JISIoT.010101.
[5] K. Kunverji, K. Shah, and N. Shah, " A flood prediction system developed using various
machine learning algorithms, " in Proc. 2021 4th Int. Conf. Adv. Sci. Technol. (ICAST).
[6] S. Sankaranarayanan, M. Prabhakar, S. Satish, P. Jain, A. Ramprasad, and A. Krishnan,
" Flood prediction based on weather parameters using deep learning, " J. Water
Climate Change, vol. 11, no. 4, pp. 1-18, 2019, doi: 10.2166/wcc.2019.321.
[7] J.M. Opella and A. Hernandez, " Developing a flood risk assessment using support vector
machine and convolutional neural network: A conceptual framework, " in Proc. 2019
IEEE 15th Int. Colloq. Signal Process. Appl. (CSPA), pp. 260-265, doi: 10.1109/CSPA.2019.
8695980.
[8] S. Park and D. Lee, " Prediction of coastal flooding risk under climate change
impacts in South Korea using machine learning algorithms, " Environ. Res. Lett., vol.
15, no. 9, p. 10, 2020, doi: 10.1088/1748-9326/aba5b3.
[9] D.S. Rani, G. Jayalakshmi, and V.P. Baligar, " Low cost IoT based flood monitoring
system using machine learning and neural networks: Flood alerting and rainfall prediction, "
in Proc. 2020 Int. Conf. Innov. Mech. Ind. Appl. (ICIMIA), pp. 261-267, doi:
10.1109/ICIMIA48430.2020.9074928.
[10] C. Ren, H. Jung, S. Lee, and D. Jeong, " Coastal waste detection based on deep
convolutional neural networks, " Sensors, vol. 21, no. 21, pp. 1-17, 2021, doi: 10.3390/
s21217269.
[11] A. Sahoo, S. Samantaray, and D. K. Ghose, " Prediction of flood in Barak river using
hybrid machine learning approaches: A case study, " J. Geol. Soc. India, vol. 97, no. 2,
pp. 186-198, 2021, doi: 10.1007/s12594-021-1650-1.
[12] N. Jeerana, N. O. Nikitin, and A. Kalyuzhnaya, " Urban pluvial flood forecasting
using open data with machine learning techniques in Pattani basin, " Proc. Comput.
Sci., vol. 119, pp. 288-297, Dec. 2017, doi: 10.1016/j.procs.2017.11.187.
[13] M. Rahman, N. Chen, and A. Washakh, " Flood susceptibility assessment in Bangladesh
using machine learning and multi-criteria decision analysis, " Earth Syst. Environ.,
vol. 3, no. 3, pp. 585-601, 2019, doi: 10.1007/s41748-019-00123-y.
[14] S. A. A. El-Magd, B. Pradhan, and A. Alamri, " Machine learning algorithm for flash
flood prediction mapping in Wadi El-Laqeita and surroundings, Central Eastern Desert, "
Egypt Arabian J. Geosci., vol. 14, no. 4, p. 323, 2021, doi: 10.1007/s12517-021-06466-z.
[15] M. S. Tehrany, S. Jones, and F. Shabani, " Identifying the essential flood conditioning
factors for flood prone area mapping using machine learning techniques, " Catena,
vol. 175, pp. 174-192, Apr. 2019, doi: 10.1016/j.catena.2018.12.011.
[16] A. Mosavi, P. Ozturk, and K. Chau, " Flood prediction using machine learning models:
Literature review, " Water, vol. 10, no. 11, pp. 1-41, 2018, doi: 10.3390/w10111536.
[17] " Rainfall dataset, " India Meteorological Department, New Delhi, India. Accessed:
2022. [Online]. Available: https://mausam.imd.gov.in/
[18] " India flood inventory: HydroSense Lab, IIT Delhi. " GitHub. https://github.com/
hydrosenselab/India-Flood-Inventory (Accessed: 2022.)
October 2022 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE 33
True-Positive Rate
True-Positive Rate
https://indiaai.gov.in/case-study/using-ai-to-predict-floods-and-save-lives https://indiaai.gov.in/case-study/using-ai-to-predict-floods-and-save-lives https://mausam.imd.gov.in/ https://github.com/hydrosenselab/India-Flood-Inventory https://github.com/hydrosenselab/India-Flood-Inventory
IEEE Systems, Man and Cybernetics Magazine - October 2022

Table of Contents for the Digital Edition of IEEE Systems, Man and Cybernetics Magazine - October 2022
