The GeoAlert – Satellite Imagery based Flood Risk Assessment (FRA) system using Machine Learning Using Satellite Imagery to detect water flooding by means of various bands of spectrum.

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Parvej Saleh
Rajdeep Purkayastha
Srivathshan KS


Assessment of flood risk zonation and landscape vulnerability to flood are crucial perspective in flood risk management. It is a standout amongst the most genuine catastrophic events in North East, India. The mighty Brahmaputra being one of the significant streams of Asia, is a trans-boundary waterway which flows through China, India and Bangladesh. Floods are an exceptionally regular event during the monsoon season. Deforestation in the Brahmaputra watershed has brought increased siltation levels, flash floods and soil erosion in basic downstream natural environment. To detect changes in land cover and NDVI rapidly and precisely, we have utilized remote sensing technology and Geographic Information Systems (GIS). In this paper, Landsat 8 Operational Land Imager (OLI) data were utilized to access landscape vulnerability to flood inundation and flood risk in Kamrup district of Assam, India. Flood inundation outline was prepared based on water and land pixels on images. We have proposed an automated flood alert generating and continuous monitoring system which analyses the recent images by extracts the number of geo-tagged pixels of water body (extracts area and contour of water and land regions) and generate when the number of pixels of water crosses the threshold, our system generates an alert. Based on the area of the water regions on the map we could estimate flood affected regions comparing with the existing data of land and water cover regions reported by the government and there by alert the officials for preventive measures. Different metrics were experimented to extract more accurate area of water logging regions. With the historical information of rainfall and area of water logging regions we could apply machine learning models to predict the possible inundation in next 48 hours. This will be more effective to move people to safe high-altitude regions during the time of flood.

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SalehP., PurkayasthaR., & KSS. (2020). The GeoAlert – Satellite Imagery based Flood Risk Assessment (FRA) system using Machine Learning: Using Satellite Imagery to detect water flooding by means of various bands of spectrum. Probyto AI Journal, 1(01). Retrieved from


[1] Salmon, B.P.; Kleynhans, W.; van Den Bergh, F.; Olivier, J.C.; Grobler, T.L.; Wessels, K.J. Land cover change detection using the internal covariance matrix of the extended Kalman filter over multiple spectral bands. IEEE J. Sel. Topics Appl. Earth Observations Remote Sens. 2013, 6, 1079–1085.
[2] Demir, B.; Bovolo, F.; Bruzzone, L. Updating land-cover maps by classification of image time series: A novel change-detection-driven transfer learning approach. IEEE Trans. Geosci. Remote Sens. 2013, 51, 300–312.
[3] Volpi, M.; Petropoulos, G.P.; Kanevski, M. Flooding extent cartography with Landsat TM imagery and regularized Kernel Fisher’s discriminant analysis. Comput. Geosci. 2013, 57, 24–31.
[4] Brisco, B.; Schmitt, A.; Murnaghan, K.; Kaya, S.; Roth, A. Sar polarimetric change detection for flooded vegetation. Int. J. Digit. Earth 2013, 6, 103–114.
[5] Kaliraj, S.; Muthu Meenakshi, S.; Malar, V.K. Application of remote sensing in detection of forest cover changes using geo-statistical change detection matrices—A case study of devanampatti reserve forest, tamilnadu, India. Nat. Environ. Polluti. Technol. 2012, 11, 261–269.
[6] Markogianni, V.; Dimitriou, E.; Kalivas, D.P. Land-use and vegetation change detection in plastira artificial lake catchment (Greece) by using remote-sensing and GIS techniques. Int. J. Remote Sens. 2013, 34, 1265–1281.
[7] Bagan, H.; Yamagata, Y. Landsat analysis of urban growth: How Tokyo became the world’s largest megacity during the last 40 years. Remote Sens. Environ. 2012, 127, 210–222.
[8] Raja, R.A.A.; Anand, V.; Kumar, A.S.; Maithani, S.; Kumar, V.A. Wavelet based post classification change detection technique for urban growth monitoring. J. Indian Soc. Remote Sens. 2013, 41, 35–43.
[9] Dronova, I.; Gong, P.; Wang, L. Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China. Remote Sens. Environ. 2011, 115, 3220–3236.
[10] Zhu, X.; Cao, J.; Dai, Y. A Decision Tree Model For Meteorological Disasters Grade Evaluation of Flood. In Proceedings of 4th International Joint Conference on Computational Sciences and Optimization 2011, Kunming and Lijiang, Yunnan, China, 15–19 April 2011; Institute of Electrical and Electronics Engineers: New York NY, USA, 2011; pp. 916–919.
[11] Ridd, M.K.; Liu, J. A comparison of four algorithms for change detection in an urban environment. Remote Sens. Environ. 1998, 63, 95–100.
[12] Lu, S.; Wu, B.; Yan, N.; Wang, H. Water body mapping method with HJ-1A/B satellite imagery. Int. J. Appl. Earth Obs. Geoinf. 2011, 13, 428–434.
[13] Desmet, P.J.J.; Govers, G. A GIS procedure for automatically calculating the USLE LS factor on topographically complex landscape units. J. Soil Water Conserv. 1996, 51, 427–433.
[14] Zhou, W.; Wu, B. Assessment of soil erosion and sediment delivery ratio using remote sensing and GIS: A case study of upstream chaobaihe river catchment, north China. Int. J. Sediment Res. 2008, 23, 167–173.
[15] Du, Z.; Linghu, B.; Ling, F.; Li, W.; Tian, W.; Wang, H.; Gui, Y.; Sun, B.; Zhang, X. Estimating surface water area changes using time-series Landsat data in the qingjiang river basin, China. J. Appl. Remote Sens. 2012, 6, doi:10.1117/1.JRS.6.063609.
[16] Sun, F.; Sun, W.; Chen, J.; Gong, P. Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery. Int. J. Remote Sens. 2012, 33, 6854–6875.
[17] Water Body Extraction from Multi-Source Satellite Images. Available online: (accessed on 21 June 2003).
[18] Xu, H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 2006, 27, 3025–3033.
[19] Water Body Extraction And Change Detection Based on Multi-Temporal SAR Images. Available online: (accessed on 21 January 2014).
[20] Zhou, H.; Hong, J.; Huang, Q. Landscape and water quality change detection in urban wetland: A post-classification comparison method with IKONOS data. Procedia Environ. Sci. 2011, 10, 1726–1731.
[21] Tang, Z.; Ou, W.; Dai, Y.; Xin, Y. Extraction of water body based on Landsat TM5 imagery—A case study in the Yangtze river. Adv. Inf. Comm. Technol. 2013, 393, 416–420
[22] Li, W.; Du, Z.; Ling, F.; Zhou, D.; Wang, H.; Gui, Y.; Sun, B.; Zhang, X. A comparison of land surface water mapping using the normalized difference water index from TM, ETM+ and ALI. Remote Sens. 2013, 5, 5530–5549.
[23] McFeeters, S.K. Using the normalized difference water index (NDWI) within a geographic information system to detect swimming pools for mosquito abatement: A practical approach. Remote Sens. 2013, 5, 3544–3561.
[24] McFeeters, S.K. The use of the normalized difference water index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 1996, 17, 1425–1432.
[25] Alesheikh, A.A.; Ghorbanali, A.; Nouri, N. Coastline change detection using remote sensing. Int. J. Environ. Sci. Technol. 2007, 4, 61–66.
[26] Lopez-Caloca, A.; Tapia-Silva, F.O.; Escalante-Ramirez, B. Lake Chapala change detection using time series. Remote Sens. Agric. Ecosyst. Hydrol. 2008, 7104, 1–11.
[27] El-Asmar, H.M.; Hereher, M.E. Change detection of the coastal zone east of the Nile Delta using remote sensing. Environ. Earth Sci. 2011, 62, 769–777.
[28] Xu, Y.B.; Lai, X.J.; Zhou, C.G. Water surface change detection and analysis of bottomland submersion-emersion of wetlands in Poyang Lake reserve using ENVISAT ASAR data. China Environ. Sci. 2010, 30, 57–63
[29] Landsat 8 Data Users Handbook
[30] Guerschman J P, Warren G, Byrne G, Lymburner L, Mueller N and Van Dijk A I 2011 MODIS based standing water detection for flood and large reservoir mapping: algorithm development and applications for the Australian continent CSIRO: Water for a Healthy Country National Research Flagship Report (Canberra)
[31] Flood monitoring and damage assessment in Thailand using multi-temporal HJ-1A/1B and MODIS images. S L Zhou and W C Zhang

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