Flood Prediction System Using IOT & Artificial Neural Network

Authors

DOI:

https://doi.org/10.21015/vtse.v12i1.1603

Abstract

Floods pose significant challenges as one of nature's most devastating disasters, making the development of accurate forecast model’s complex. This issue has led to severe consequences such as crop loss, population displacement, damage to infrastructure, and disruption of essential services. Advanced research on flood prediction models has played a crucial role in providing policy recommendations, mitigating risks, reducing human casualties, and minimizing property damage caused by floods. In this context, we propose an Internet of Things (IoT)-based flood prediction and forecasting model that prioritizes energy efficiency. Given the limited battery and memory capacity of IoT sensor nodes, we employ an energy-saving strategy within the fog layer, leveraging data diversity to minimize energy consumption. Additionally, cloud technology offers an effective storage solution. To accurately calibrate flood phases, we investigate climatic factors such as humidity, temperature, rainfall, as well as water body parameters including water flow and elevation. Neural networks are commonly used in constructing forecast systems, as they can replicate the complex calculations involved in flood physical processes, resulting in improved performance and cost-effectiveness. In our approach, the Artificial Neural Network (ANN) technique is employed for flood forecasting, and the effectiveness of different algorithms, such as Logistic Regression and Decision Tree, is assessed by comparing them to ANN. Accuracy values are computed using a classification report assessment, and graph parameters are carefully evaluated. Ultimately, our proposed system utilizes the ANN technique to train a predictive model by examining the dataset. This model generates real-time flood risk forecasts through a user-friendly graphical interface.

References

REFRENCES

A. A. Ghapar, S. Yussof, and A. A. Bakar, “Internet of Things (IOT) Architecture for Flood Data Management,” Int. J. Futur. Gener. Commun. Netw., vol. 11, no. 1, pp. 55–62, 2018, doi: 10.14257/ijfgcn.2018.11.1.06. DOI: https://doi.org/10.14257/ijfgcn.2018.11.1.06

W. M. Shah, F. Arif, A. A. Shahrin, and A. Hassan, “The implementation of an IOT-based Flood Alert System,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 11, pp. 620–623, 2018, doi: 10.14569/ijacsa.2018.091187.

B. E. Pengel et al., “Flood early warning system: Sensors and internet,” IAHS-AISH Publ., vol. 357, pp. 445–453, 2013.

R. A. Krueger, “Focus Groups : A Practical Guide For Applied Research Description : Title : Focus Groups : A Practical Guide for Applied Research,” 2014.

A. Kevin, “That ’ Internet of Things ’ Thing,” RFiD J., p. 4986, 2010.

P. Khatri, “IOT strategic research and use case scenario: A direction to the smart life,” Glob. Sci-Tech, vol. 10, no. 4, p. 235, 2018, doi:

5958/2455-7110.2018.00033.2.

N. E. Petroulakis, I. G. Askoxylakis, and T. Tryfonas, “Life-logging in smart environments: Challenges and security threats,” IEEE Int. Conf. Commun., pp. 5680–5684, 2012, doi: 10.1109/ICC.2012.6364934. DOI: https://doi.org/10.1109/ICC.2012.6364934

P. Vijai and P. B. Sivakumar, “Design of IOT Systems and Analytics in the Context of Smart City Initiatives in India,” Procedia Comput. Sci., vol. 92, pp. 583–588, 2016, doi: 10.1016/j.procs.2016.07.386. DOI: https://doi.org/10.1016/j.procs.2016.07.386

S. Gangopadhyay and M. K. Mondal, “A wireless framework for environmental monitoring and instant response alert,” Int. Conf. Microelectron. Comput. Commun. MicroCom 2016, no. January 2016, 2016, doi: 10.1109/MicroCom.2016.7522535. DOI: https://doi.org/10.1109/MicroCom.2016.7522535

N. A. Maspo, A. N. Harun, M. Goto, M. N. M. Nawi, and N. A. Haron, “Development of internet of thing (IOT) technology for flood prediction and early warning system (EWS),” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 4S, pp. 219–228, 2019.

P. Mitra et al., “Flood forecasting using Internet of things and artificial neural networks,” 7th IEEE Annu. Inf. Technol. Electron. Mob. Commun. Conf. IEEE IEMCON 2016, 2016, doi: 10.1109/IEMCON.2016.7746363. DOI: https://doi.org/10.1109/IEMCON.2016.7746363

S. Fang et al., “An integrated system for regional environmental monitoring and management based on internet of things,” IEEE Trans. Ind. Informatics, vol. 10, no. 2, pp. 1596–1605, 2014, doi: 10.1109/TII.2014.2302638. DOI: https://doi.org/10.1109/TII.2014.2302638

M. Ancona, A. Dellacasa, G. Delzanno, A. La Camera, and I. Rellini, “An ‘Internet of Things ’ Vision of the Flood Monitoring Problem,” Ambient 2015, Fifth Int. Conf. Ambient Comput. Appl. Serv. Technol., no. c, pp. 26–29, 2015, [Online]. Available: https://www.thinkmind.org/index.php?view=article&articleid=ambient_2015_1_50_40065.

Y. Hirabayashi et al., “Global flood risk under climate change,” Nat. Clim. Chang., vol. 3, no. 9, pp. 816–821, 2013, doi: 10.1038/nclimate1911. DOI: https://doi.org/10.1038/nclimate1911

T. Perumal, M. N. Sulaiman, and C. Y. Leong, “Internet of Things (IOT) enabled water monitoring system,” 2015 IEEE 4th Glob. Conf. Consum. Electron. GCCE 2015, pp. 86–87, 2016, doi: 10.1109/GCCE.2015.7398710. DOI: https://doi.org/10.1109/GCCE.2015.7398710

P. Rawat, K. D. Singh, H. Chaouchi, and J. M. Bonnin, “Wireless sensor networks: A survey on recent developments and potential synergies,” J. Supercomput., vol. 68, no. 1, pp. 1–48, 2014, doi: 10.1007/s11227-013-1021-9. DOI: https://doi.org/10.1007/s11227-013-1021-9

M. I. O. Al-Lami and M. Ilyas, “Improving scheduling Function for IEEE802.15.4e Time Slotted Channel Hopping for Wireless Sensor Networks,” 4th Int. Symp. Multidiscip. Stud. Innov. Technol. ISMSIT 2020 - Proc., 2020, doi: 10.1109/ISMSIT50672.2020.9255135.

D. Standard, W. Personal, and A. Networks, “75.4: a,” pp. 12–19, 2001.

M. Siekkinen, M. Hiienkari, J. K. Nurminen, and J. Nieminen, “How low energy is bluetooth low energy? Comparative measurements with ZigBee/802.15.4,” 2012 IEEE Wirel. Commun. Netw. Conf. Work. WCNCW 2012, pp. 232–237, 2012, doi: 10.1109/WCNCW.2012.6215496. DOI: https://doi.org/10.1109/WCNCW.2012.6215496

H. Afzaal and N. A. Zafar, “Cloud computing based flood detection and management system using WSANs,” ICET 2016 - 2016 Int. Conf. Emerg. Technol., 2017, doi: 10.1109/ICET.2016.7813213. DOI: https://doi.org/10.1109/ICET.2016.7813213

J. Abbot and J. Marohasy, “Input selection and optimisation for monthly rainfall forecasting in queensland, australia, using artificial neural networks,” Atmos. Res., vol. 138, pp. 166–178, 2014, doi: 10.1016/j.atmosres.2013.11.002. DOI: https://doi.org/10.1016/j.atmosres.2013.11.002

L. Li, H. Xu, X. Chen, and S. P. Simonovic, “Streamflow forecast and reservoir operation performance assessment under climate change,” Water Resour. Manag., vol. 24, no. 1, pp. 83–104, 2010, doi: 10.1007/s11269-009-9438-x. DOI: https://doi.org/10.1007/s11269-009-9438-x

J. Adamowski, H. F. Chan, S. O. Prasher, B. Ozga-zielinski, and A. Sliusarieva, “Comparison of multiple linear and nonlinear regression , autoregressive integrated moving average , artificial neural network , and wavelet artificial neural network methods for urban water demand forecasting in Montreal , Canada,” vol. 48, no. i, pp. 1–14, 2012, doi: 10.1029/2010WR009945. DOI: https://doi.org/10.1029/2010WR009945

S. A. Fuselier et al., “Low energy neutral atoms from the heliosheath,” Astrophys. J., vol. 784, no. 2, 2014, doi: 10.1088/0004-637X/784/2/89. DOI: https://doi.org/10.1088/0004-637X/784/2/89

S. Jun and K. Oh, “An evolutionary statistical learning theory,” Int. J. Comput. Intell., vol. 3, no. 3, pp. 249–256, 2006, [Online]. Available: http://www.waset.org/publications/5739.

M. S. Reis and P. M. Saraiva, “Integration of data uncertainty in linear regression and process optimization,” AIChE J., vol. 51, no. 11, pp. 3007–3019, 2005, doi: 10.1002/aic.10540. DOI: https://doi.org/10.1002/aic.10540

G. De’ath and K. E. Fabricius, “Classification and Regression Trees: A Powerful Yet Simple Technique for Ecological Data Analysis,” Ecology, vol. 81, no. 11, p. 3178, 2000, doi: 10.2307/177409. DOI: https://doi.org/10.2307/177409

M. S. Tehrany, B. Pradhan, and M. N. Jebur, “Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS,” J. Hydrol., vol. 504, pp. 69–79, 2013, doi: 10.1016/j.jhydrol.2013.09.034. DOI: https://doi.org/10.1016/j.jhydrol.2013.09.034

B. Choubin, H. Darabi, O. Rahmati, F. Sajedi-Hosseini, and B. Kløve, “River suspended sediment modelling using the CART model: A comparative study of machine learning techniques,” Sci. Total Environ., vol. 615, pp. 272–281, 2018, doi: 10.1016/j.scitotenv.2017.09.293. DOI: https://doi.org/10.1016/j.scitotenv.2017.09.293

P. S. Yu, T. C. Yang, S. Y. Chen, C. M. Kuo, and H. W. Tseng, “Comparison of random forests and support vector machine for real-time radar-derived rainfall forecasting,” J. Hydrol., vol. 552, pp. 92–104, 2017, doi: 10.1016/j.jhydrol.2017.06.020. DOI: https://doi.org/10.1016/j.jhydrol.2017.06.020

X. Zhang, L. Liu, L. Xiao, and J. Ji, “Comparison of machine learning algorithms for predicting crime hotspots,” IEEE Access, vol. 8, pp. 181302–181310, 2020, doi: 10.1109/ACCESS.2020.3028420.

C. Chen, Q. Hui, W. Xie, S. Wan, Y. Zhou, and Q. Pei, “Convolutional Neural Networks for forecasting flood process in Internet-of-Things enabled smart city,” Comput. Networks, vol. 186, p. 107744, 2021, doi: 10.1016/j.comnet.2020.107744.

S. N. Yang and L. C. Chang, “Regional inundation forecasting using machine learning techniques with the internet of things,” Water (Switzerland), vol. 12, no. 6, 2020, doi: 10.3390/W12061578.

N. R. Prasad, S. Almanza-Garcia, and T. T. Lu, “Anomaly detection,” Comput. Mater. Contin., vol. 14, no. 1, pp. 1–22, 2009, doi: 10.1145/1541880.1541882. DOI: https://doi.org/10.1145/1541880.1541882

Y. Zhang, N. Meratnia, and P. Havinga, “Outlier detection techniques for wireless sensor networks: A survey,” IEEE Commun. Surv. Tutorials, vol. 12, no. 2, pp. 159–170, 2010, doi: 10.1109/SURV.2010.021510.00088. DOI: https://doi.org/10.1109/SURV.2010.021510.00088

P. P. Ray, M. Mukherjee, and L. Shu, “Internet of Things for Disaster Management: State-of-the-Art and Prospects,” IEEE Access, vol. 5, no. i, pp. 18818–18835, 2017, doi: 10.1109/ACCESS.2017.2752174. DOI: https://doi.org/10.1109/ACCESS.2017.2752174

J. A. Hernández-Nolasco, M. A. W. Ovando, F. D. Acosta, and P. Pancardo, “Water level meter for alerting population about floods,” Proc. - Int. Conf. Adv. Inf. Netw. Appl. AINA, vol. 2016-May, pp. 879–884, 2016, doi: 10.1109/AINA.2016.76 DOI: https://doi.org/10.1109/AINA.2016.76

.

V. Ashok Kumar, B. Girish, and K. R. Rajesh, “Integrated Weather & Flood Alerting System,” Int. Adv. Res. J. Sci. Eng. Technol., vol. 2, no. 6, pp. 21–24, 2015, doi: 10.17148/IARJSET.2015.2606.

D. Satria, S. Yana, R. Munadi, and S. Syahreza, “Design of information monitoring system flood based internet of things (IOT),” Emerald Reach Proc. Ser., vol. 1, pp. 337–342, 2018, doi: 10.1108/978-1-78756-793-1-00072.

K. Gill, S. H. Yang, F. Yao, and X. Lu, “A ZigBee-based home automation system,” IEEE Trans. Consum. Electron., vol. 55, no. 2, pp. 422–430, 2009, doi: 10.1109/TCE.2009.5174403. DOI: https://doi.org/10.1109/TCE.2009.5174403

S. Jayashree, S. Sarika, A. L. Solai, and S. Prathibha, “A novel approach for early flood warning using android and IOT,” Proc. 2017 2nd Int. Conf. Comput. Commun. Technol. ICCCT 2017, pp. 339–343, 2017, doi: 10.1109/ICCCT2.2017.7972302. DOI: https://doi.org/10.1109/ICCCT2.2017.7972302

P. Singh, G. Bathla, R. K. Singh, and A. Aggarwal, “A Novel Approach for Wireless Home Automation System using IOT,” CEUR Workshop Proc., vol. 3058, pp. 0–2, 2021.

S. Bande and V. V. Shete, “Smart flood disaster prediction system using IOT & neural networks,” Proc. 2017 Int. Conf. Smart Technol. Smart Nation, SmartTechCon 2017, pp. 189–194, 2018, doi: 10.1109/SmartTechCon.2017.8358367. DOI: https://doi.org/10.1109/SmartTechCon.2017.8358367

Downloads

Published

2024-03-31

How to Cite

Wajid, M., Abid, M. K., Asif Raza, A., Haroon, M., & Mudasar, A. Q. (2024). Flood Prediction System Using IOT & Artificial Neural Network. VFAST Transactions on Software Engineering, 12(1), 210–224. https://doi.org/10.21015/vtse.v12i1.1603

Issue

Vol. 12 No. 1 (2024): January-March

Section

Articles

License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-By) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

                                      

This work is licensed under a Creative Commons Attribution  License CC BY