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Forecasting Water Level Of Jhelum River Of Kashmir Valley India, Using Prediction And Earlywarning System

https://doi.org/10.24057/2071-9388-2019-169

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Abstract

The hydrological disasters have the largest share in global disaster list and in 2016 the Asia’s share was 41% of the global occurrence of flood disasters. The Jammu and Kashmir is one of the most flood-prone regions of the Indian Himalayas. In the 2014 floods, approximately 268 people died and 168004 houses were damaged. Pulwama, Srinagar, and Bandipora districts were severely affected with 102, 100 and 148 km 2 respectively submerged in floods. To predict and warn people before the actual event occur, the Early Warning Systems were developed. The Early Warning Systems (EWS) improve the preparedness of community towards the disaster. The EWS does not help to prevent floods but it helps to reduce the loss of life and property largely. A flood monitoring and EWS is proposed in this research work. This system is composed of base stations and a control center. The base station comprises of sensing module and processing module, which makes a localised prediction of water level and transmits predicted results and measured data to the control center. The control center uses a hybrid system of Adaptive Neuro-Fuzzy Inference System (ANFIS) model and the supervised machine learning technique, Linear Multiple Regression (LMR) model for water level prediction. This hybrid system presented the high accuracy of 93.53% for daily predictions and 99.91% for hourly predictions.

About the Authors

Mirza Imran
B.S. Abdur Rahman Crescent Institute of Science and Technology
India

Department of Computer Applications

Vandalur Ch-600048



Abdul Khader P. Sheikh
B.S. Abdur Rahman Crescent Institute of Science and Technology
India

Department of Computer Applications

Vandalur Ch-600048



References

1. Ahern M., Kovats R.S., Wilkinson P., Few R. & Matthies, F. (2005). Global Health Impacts of Floods : Epidemiologic Evidence. Epidemiologic Reviews (27), 36-46, DOI: 10.1093/epirev/mxi004.

2. Antanasijevi D.Z., Pocaj V.V., Povrenovi D.S., Risti M. Đ. & Peri A.A. (2013). PM 10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Science of the Total Environment, (443), 511-519, DOI: 10.1016/j.scitotenv.2012.10.110.

3. Ashrit R. (2010). Investigating the Leh ‘cloudburst’. National Centre for Medium Range Weather Forecasting. Ministry of Earth Sciences, India.

4. Basha E. & Rus D. (2007). Design of Early Warning Flood Detection Systems for Developing Countries. Information and Communication Technologies and Development. ICTD 2007, 1-10, DOI: 10.1109/ICTD.2007.4937387.

5. Berz G., Kron W., Loster T., Rauch E. & Schimetschek J., Schmieder J., Siebert A., Smolka A., Wirtz A. (2001). World Map of Natural Hazards – A Global View of the Distribution and Intensity of Significant Exposures. Natural Hazards, (23), 443-465.

6. Bhatt C.M., Rao G.S., Farooq M., Manjusree P., Shukla A., Sharma S.V.S.P., Dadhwal V.K. (2017). Satellite-based assessment of the catastrophic Jhelum floods of September 2014, Jammu & Kashmir, India. Geomatics, Natural Hazards and Risk, 8(2), 309-327, DOI:10.1080/19475705.2016.1218943.

7. Bhatt C.M., Srinivasa Rao G., Manjushree P. & Bhanumurthy V. (2010). Space based disaster management of 2008 Kosi floods, North Bihar, India. Journal of the Indian Society of Remote Sensing, 38(1), 99-108, DOI: 10.1007/s12524-010-0015-9.

8. Cavallo E. and Noy I. (2011) Natural Disasters and the Economy – A Survey. International Review of Environmental and Resource Economics, (5), 63-102, DOI: 10.1561/101.00000039.

9. Chandran R., Ramakrishnan D., Chowdary V., Jeyaram A. & Jha A. (2006). Flood mapping and analysis using air-borne synthetic aperture radar: A case study of July 2004 flood in Baghmati river basin, Bihar. Current Science, 90(2), 249-256.

10. Dar A.A. and Anuradha N. (2018). An Application of Taguchi L9 Method in Black Scholes Model for European Call Option, International Journal of Entrepreneurship, 22(1), 1-13.

11. Do H.N., Vo M., Tran V., Tan P.V. and Trinh C.V. (2015). An early flood detection system using mobile networks. In 2015 international conference on advanced technologies for communications (ATC), 599-603. IEEE.

12. Guha-sapir D., Hoyois P. and Below R. (2017). Annual Disaster Statistical Review 2016: The numbers and trends. Review Literature And Arts Of The Americas, 1-50, DOI: 10.1093/rof/rfs003.

13. IPCC. (2001). Third Assessment Report (TAR) of the Intergovernmental Panel on Climate Change, Parts 1, 2 and 3, Cambridge University Press, Cambridge, UK. (1, 2, 3).

14. IPCC. (2014). Climate Change 2014 Impacts, Adaptation, and Vulnerability Part B: Regional Aspects, Cambridge University Press, Cambridge, UK.

15. Iqbal M. (2017). Ethnographers Lens Reviews Life in a Conflict Zone Kashmir. International Journal of Research Culture Society, 1(8) 80-86.

16. Islam A., Islam T., Syrus M.A. and Ahmed N. (2014). Implementation of Flash Flood Monitoring System Based on Wireless Sensor Network in Bangladesh, 3rd International Conference on Informatics, Electronics & Vision 2014.

17. Jang J.R. (1993). ANFIS : Adaptive-Network-Based Fuzzy Inference System. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.

18. Joseph F., Hair Jr., William C. Black, Babin B.J. and Anderson R.E. (2014). Multivariate Data Analysis, 7th ed. Pearson education limited, Eidenburgh gate, Harlow.

19. Mathur D.K. (2019). Application of Geospatial technologies in flood vulnerability analysis. Disaster Advances, 12(12), 40-45.

20. Kansal M.L., Kumar P. and Kishore K.A. (2016). Need of Integrated Flood Risk Management (IFRM) in Bihar. In National conference on Water Resources and Flood Management with special reference to Flood Modelling (WRFM-2016), Surat.

21. Kaur A., Ghawana T. and Kumar N. (2019). Preliminary Analysis of Flood Disaster 2017 in Bihar and Mitigation Measures. In Proceedings of International Conference on Remote Sensing for Disaster Management, 455-464.

22. Kim S., Kim J. & Park K. (2009). Neural Networks Models for the Flood Forecasting and Disaster Prevention System in the Small Catchment. Disaster Advances, 2(3), 51-63.

23. Kiran K.S., Manjusree P. & Viswanadham M. (2019). Sentinel-1 SAR Data Preparation for Extraction of Flood Footprints-A Case Study. Disaster Advances, 12(12), 10-20.

24. Kumar S., Sahdeo A. and Guleria S. (2013). Bihar Floods 2007: A Field Report. National Institute of Disaster Management, Ministry of Home Affairs, Government of India, New Delhi.

25. Mcbean G. (2002). Climate Change and Extreme Weather : A Basis for Action, Natural Hazards, 31, 177-190.

26. Mishra A.K., Chandra S., Rafiq M., Sivarajan N. and Santhanam K. (2016). An observational study of the Kanchipuram flood during the northeast monsoon season in 2015,Weather, 9-10, DOI: 10.1002/wea.3271.

27. Mishra A.K. (2015). A study on the occurrence of flood events over Jammu and Kashmir during September 2014 using satellite remote sensing. Natural Hazards, 78(2), 1463-1467, DOI: 10.1007/s11069-015-1768-9.

28. Mishra A. K. and Rafiq M. (2017b). Analyzing snowfall variability over two locations in Kashmir , India in the context of warming climate. Dynamics of Atmospheres and Oceans, 79 (May), 1-9, DOI: 10.1016/j.dynatmoce.2017.05.002.

29. Mishra A.K., Nagaraju V., Rafiq M. and Chandra S. (2019). Evidence of links between regional climate change and precipitation extremes over India. Weather, 74(6), 218-221.

30. Mishra A.K., Meer M.S. and Nagaraju V. (2019b). Satellite-based monitoring of recent heavy flooding over north-eastern states of India in July 2019. Natural Hazards, 97(3), 1407-1412.

31. Mishra A.K. and Rafiq M. (2019). Rainfall estimation techniques over India and adjoining oceanic regions. Current Science, (January), DOI:10.18520/cs/v116/i1/56-68.

32. Mishra A.K. and Rafiq M. (2017a). Towards combining GPM and MFG observations to monitor near real time heavy precipitation at fine scale over India and nearby oceanic regions. Dynamics of Atmospheres and Oceans, 80 (October), 62-74, DOI: 10.1016/j.dynatmoce.2017.10.001.

33. Natividad J.G. and Mendez J.M. (2018). Flood Monitoring and Early Warning System Using Ultrasonic Sensor. IOP Conference Series: Materials Science and Engineering, 325(1). DOI: 10.1088/1757-899X/325/1/012020.

34. Pal I., Singh S., and Walia A. (2013). Flood Management in Assam , INDIA : A review of Brahmaputra floods 2012. International Journal of Scientific and Reasearch Publications, 3(10), 1-5, www.ijsrp.org/research-paper-1013/ijsrp-p2214.pdf.

35. Pengel B., Shirshov G.S., Krzhizhanovskaya V.V., Melnikova N.B., Koelewijn A.R., Pyayt A.L. and Mokhov I.I. (2013). Flood early warning system: sensors and internet. Floods: From Risk to Opportunity (January), 445-453, DOI: 10.1177/1745691612459060.

36. Rafiq M. and Mishra A.K. (2016) Investigating changes in Himalayan glacier in warming environment: a case study of Kolahoi glacier. Environmental Earth Sciences. 1; 75(23):1469.

37. Rafiq M. and Mishra A.K. (2017). A study of heavy snowfall in Kashmir, India in January 2017. Weather, 99 (January), 23-25.

38. Rafiq M., Mishra A.K., Romshoo S.A. and Jalal F. (2019). Modelling Chorabari Lake outburst flood , Kedarnath , India. Journal of Mountain Science, 16 (October 2018), 64-76.

39. Rafiq M., Rashid I. and Romshoo S.A. (2012). Estimation and validation of Remotely Sensed Land Surface Temperature in Kashmir Valley. Journal of Himalayan Ecology & Sustainable Development, 9, 1-13.

40. Rezaeianzadeh M., Tabari H., Yazdi A.A., Isik S. & Kalin L. (2013). Flood flow forecasting using ANN, ANFIS and regression models. Neural Computing and Applications, 25(1), 25-37, DOI: 10.1007/s00521-013-1443-6.

41. Romshoo S.A., Altaf S., Rashid I., Dar R.A. (2018). Climatic, geomorphic and anthropogenic drivers of the 2014 extreme flooding in the Jhelum basin of,of Kashmir, India. Geomatics, Natural Hazards and Risk, 9(1), 224-248.

42. Roy J.K., Gupta D. & Goswami S. (2012). An improved flood warning system using WSN and artificial neural network. 2012 Annual IEEE India Conference, (INDICON 2012), (December), 770-774, DOI: 10.1109/INDCON.2012.6420720.

43. Singh Y., Deep K. and Niranjan S. (2012). Multiple Criteria Clustering of Mobile Agents in WSN,International Journal of Wireless & Mobile Networks , 4(3), 183-193.

44. Singh O. and Kumar M. (2013). Flood events, fatalities and damages in India from 1978 to 2006. Natural Hazards, 69, 1815-1834, DOI: 10.1007/s11069-013-0781-0.

45. Seenirajan M., Natarajan M., Thangaraj R. and Bagyaraj M. (2017) Study and Analysis of Chennai Flood 2015 Using GIS and Multicriteria Technique. Journal of Geographic Information System, 9, 126-140, DOI: 10.4236/jgis.2017.92009.

46. Sunkpho J. and Ootamakorn C. (2011). Real-time flood monitoring and warning system. Songklanakarin Journal of Science and Technology, 33(2), 227-235, DOI: 10.1016/s0959-8049(11)72694-6.

47. Thayyen R.J., Dimri A.P., Kumar P. and Agnihotri G. (2013). Study of cloudburst and flash floods around Leh, India, during August 4-6, 2010. Natural Hazards, 65(3), 2175-2204, DOI: 10.1007/s11069-012-0464-2.

48. Younes M.K., Nopiah Z.M., Basri N.E.A., Basri H., Abushammala F.M., Maulud K.N.A. and Maulud K.N.A. (2015). Solid waste forecasting using modified ANFIS modeling. Journal of the Air & Waste Management Association, 65(10), 1229-1238, DOI: 10.1080/10962247.2015.1075919.


For citation:


Imran M., P. Sheikh A. Forecasting Water Level Of Jhelum River Of Kashmir Valley India, Using Prediction And Earlywarning System. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2020;13(2):35-42. https://doi.org/10.24057/2071-9388-2019-169

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ISSN 2071-9388 (Print)
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