Abstract
High quality, accessible Digital Elevation Model (DEM) datasets play a major role in monitoring the changes in the Earth’s surface. This study proposes a novel method to increase the vertical precision of CARTOSAT 10 m DEM by blending it with publicly available SRTM (Shuttle Radar Topography Mission) DEM using machine learning methods. Machine learning methods such as Genetic Programming (GP) and Artificial Neural Networks (ANN) are applied to the SRTM-1 DEM and the CARTOSAT DEM in India to generate DEM of improved vertical accuracy. Quantifiable results show that proposed approach improve the vertical accuracy, considering the reference as Ground control Points (GCPs) elevation from Differential Global Positioning System (DGPS) survey data. Significant improvements of 47 and 35% in RMSE are offered by generated DEMs compared to the SRTM-1 and CARTOSAT respectively.
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References
Adamowski J, Karapataki C (2010) Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: evaluation of different ANN learning algorithms. J Hydrol Eng 15:729–743. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000245
Ahmed K, Shahid S, Bin HS, Wang XJ (2015) Multilayer perceptron neural network for downscaling rainfall in arid region: a case study of Baluchistan, Pakistan. J Earth Syst Sci 124:1325–1341. https://doi.org/10.1007/s12040-015-0602-9
Ajvazi B, Czimber K (2019) A comparative analysis of different dem interpolation methods in gis: case study of rahovec, kosovo. Geod Cartogr 45:43–48. https://doi.org/10.3846/gac.2019.7921
Ali Ghorbani M, Khatibi R, Aytek A, Makarynskyy O, Shiri J (2010) Sea water level forecasting using genetic programming and comparing the performance with artificial neural networks. Comput Geosci 36:620–627. https://doi.org/10.1016/j.cageo.2009.09.014
Alizadeh M, Ngah I, Hashim M, Pradhan B, Pour A (2018) A hybrid analytic network process and artificial neural network (ANP-ANN) model for urban earthquake vulnerability assessment. Remote Sens 10(6):975. https://doi.org/10.3390/rs10060975
Belayneh A, Adamowski J, Khalil B, Ozga-Zielinski B (2014) Long-term SPI drought forecasting in the Awash River basin in Ethiopia using wavelet neural networks and wavelet support vector regression models. J Hydrol 508:418–429. https://doi.org/10.1016/j.jhydrol.2013.10.052
Cook AJ, Murray T, Luckman A, Vaughan DG, Barrand NE (2012) A new 100-m digital elevation model of the antarctic peninsula derived from ASTER global DEM: methods and accuracy assessment. Earth Syst Sci Data 4:129–142. https://doi.org/10.5194/essd-4-129-2012
Das S, Pardeshi SD (2018) Comparative analysis of lineaments extracted from Cartosat, SRTM and ASTER DEM: a study based on four watersheds in Konkan region, India. Spat Inf Res 26:47–57. https://doi.org/10.1007/s41324-017-0155-x
de Vos NJ, Rientjes THM (2009) Correction of timing errors of artificial neural network rainfall-runoff models. In: Abrahart RJ, See LM, Solomatine DP (eds) Practical hydroinformatics. Water science and technology library, vol 68. Springer, Berlin, Heidelberg, pp 101–112
du Toit JI, du Toit MJ, van Sittert CGCE, Vosloo HCM (2020) Geographical information system software as in-house chemical indexing database for catalyst screening of alkene metathesis catalysts. Catal Today 342:187–196. https://doi.org/10.1016/j.cattod.2019.01.032
Farr TG, Kobrick M (2000) Shuttle radar topography mission produces a wealth of data. Eos (Washington DC) 81:583–585. https://doi.org/10.1029/EO081i048p00583
Foster J (2001) Review: discipulus: a commercial genetic programming system. Genet Program Evolvable Mach 2:201–203. https://doi.org/10.1023/A:1011516717456
Freitas HRA, Freitas CC, Rosim S, Oliveira JRF (2016) Drainage networks and watersheds delineation derived from TIN-based digital elevation models. Comput Geosci 92:21–37. https://doi.org/10.1016/j.cageo.2016.04.003
Guntu RK, Maheswaran R, Agarwal A, Singh VP (2020) Accounting for temporal variability for improved precipitation regionalization based on self-organizing map coupled with information theory. J Hydrol 590.
Guntu RK, Yeditha PK, Rathinasamy M, Perc M, Marwan N, Kurths J, Agarwal A (2020) Wavelet entropy-based evaluation of intrinsic predictability of time series. Chaos An Interdiscip J Nonlinear Sci 30:033117. https://doi.org/10.1063/1.5145005
Heumann BW, Liesch ME, Bogen NR, Meier RA, Graziano M (2020) The contiguous United States in eleven zip codes: identifying and mapping socio-economic census data clusters and exemplars using affinity propagation. J Maps 16:57–67. https://doi.org/10.1080/17445647.2020.1736193
Kişi Ö (2008) Stream flow forecasting using neuro-wavelet technique. Hydrol Process 22:4142–4152. https://doi.org/10.1002/hyp.7014
Lima AR, Cannon AJ, Hsieh WW (2013) Nonlinear regression in environmental sciences by support vector machines combined with evolutionary strategy. Comput Geosci 50:136–144. https://doi.org/10.1016/j.cageo.2012.06.023
Luedeling E, Siebert S, Buerkert A (2007) Filling the voids in the SRTM elevation model - a TIN-based delta surface approach. ISPRS J Photogramm Remote Sens 62:283–294. https://doi.org/10.1016/j.isprsjprs.2007.05.004
Maheswaran R, Khosa R (2011) Multi resolution genetic programming approach for stream flow forecasting. In: Panigrahi BK, Suganthan PN, Das S, Satapathy SC (eds) Swarm, evolutionary, and memetic computing. SEMCCO 2011. Lecture notes in computer science, vol 7076. Springer, Berlin, Heidelberg, pp 714–722
Maheswaran R, Khosa R (2012) Wavelet-Volterra coupled model for monthly stream flow forecasting. J Hydrol 450–451:320–335. https://doi.org/10.1016/j.jhydrol.2012.04.017
Maheswaran R, Khosa R, Gosain AK, Lahari S, Sinha SK, Chahar BR, Dhanya CT (2016) Regional scale groundwater modelling study for Ganga River basin. J Hydrol 541:727–741. https://doi.org/10.1016/j.jhydrol.2016.07.029
Makarynska D, Makarynskyy O (2008) Predicting sea-level variations at the Cocos (Keeling) Islands with artificial neural networks. Comput Geosci 34:1910–1917. https://doi.org/10.1016/j.cageo.2007.12.004
Moreira A, Krieger G, Hajnsek I, et al (2004) TanDEM-X: a terraSAR-X add-on satellite for single-pass SAR interferometry. In: IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS ‘04. Proceedings. 2004. IEEE, pp 1000–1003
Mukherjee S, Joshi PK, Mukherjee S, Ghosh A, Garg RD, Mukhopadhyay A (2012) Evaluation of vertical accuracy of open source digital elevation model (DEM). Int J Appl Earth Obs Geoinf 21:205–217. https://doi.org/10.1016/j.jag.2012.09.004
Nourani V, Komasi M, Mano A (2009) A multivariate ANN-wavelet approach for rainfall-runoff modeling. Water Resour Manag 23:2877–2894. https://doi.org/10.1007/s11269-009-9414-5
Okkan U, Fistikoglu O (2014) Evaluating climate change effects on runoff by statistical downscaling and hydrological model GR2M. Theor Appl Climatol 117:343–361. https://doi.org/10.1007/s00704-013-1005-y
Patel A, Katiyar SK, Prasad V (2016) Performances evaluation of different open source DEM using differential global positioning system (DGPS). Egypt J Remote Sens Sp Sci 19:7–16. https://doi.org/10.1016/j.ejrs.2015.12.004
Pham HT, Marshall L, Johnson F, Sharma A (2018) A method for combining SRTM DEM and ASTER GDEM2 to improve topography estimation in regions without reference data. Remote Sens Environ 210:229–241. https://doi.org/10.1016/j.rse.2018.03.026
Rajasekhar M, Raju GS, Raju RS, Ramachandra M, Kumar BP (2018) Data on comparative studies of lineaments extraction from ASTER DEM, SRTM, and Cartosat for Jilledubanderu River basin, Anantapur district, A. P, India by using remote sensing and GIS. Data Br 20:1676–1682. https://doi.org/10.1016/j.dib.2018.09.023
Rana VK, Suryanarayana TMV (2019) Visual and statistical comparison of ASTER, SRTM, and Cartosat digital elevation models for watershed. J Geovisualization Spat Anal 3:12. https://doi.org/10.1007/s41651-019-0036-z
Rawat KS, Mishra AK, Sehgal VK, Ahmed N, Tripathi VK (2013) Comparative evaluation of horizontal accuracy of elevations of selected ground control points from ASTER and SRTM DEM with respect to CARTOSAT-1 DEM: a case study of Shahjahanpur district, Uttar Pradesh, India. Geocarto Int 28:439–452. https://doi.org/10.1080/10106049.2012.724453
Rawat KS, Singh SK, Singh MI, Garg BL (2019) Comparative evaluation of vertical accuracy of elevated points with ground control points from ASTERDEM and SRTMDEM with respect to CARTOSAT-1DEM. Remote Sens Appl Soc Environ 13:289–297. https://doi.org/10.1016/j.rsase.2018.11.005
Reuter HI, Nelson A, Jarvis A (2007) An evaluation of void-filling interpolation methods for SRTM data. Int J Geogr Inf Sci 21:983–1008. https://doi.org/10.1080/13658810601169899
Robinson N, Regetz J, Guralnick RP (2014) EarthEnv-DEM90: a nearly-global, void-free, multi-scale smoothed, 90m digital elevation model from fused ASTER and SRTM data. ISPRS J Photogramm Remote Sens 87:57–67. https://doi.org/10.1016/j.isprsjprs.2013.11.002
Sachindra DA, Ahmed K, Rashid MM, Shahid S, Perera BJC (2018) Statistical downscaling of precipitation using machine learning techniques. Atmos Res 212:240–258. https://doi.org/10.1016/j.atmosres.2018.05.022
Sahay RR, Srivastava A (2014) Predicting monsoon floods in Rivers embedding wavelet transform, genetic algorithm and neural network. Water Resour Manag 28:301–317. https://doi.org/10.1007/s11269-013-0446-5
Sefercik UG, Alkan M, Buyuksalih G, Jacobsen K (2013) Generation and validation of high-resolution DEMs from Worldview-2 stereo data. Photogramm Rec 28:362–374. https://doi.org/10.1111/phor.12038
Selle B, Muttil N (2011) Testing the structure of a hydrological model using genetic programming. J Hydrol 397:1–9. https://doi.org/10.1016/j.jhydrol.2010.11.009
Setti S, Rathinasamy M, Chandramouli S (2018) Assessment of water balance for a forest dominated coastal river basin in India using a semi distributed hydrological model. Model Earth Syst Environ 4:127–140. https://doi.org/10.1007/s40808-017-0402-0
Shanmuganathan S (2016) Studies in computational intelligence 628 artificial neural network modelling, pp 145–159. https://doi.org/10.1007/978-3-319-28495-8
Sivapragasam C, Maheswaran R, Venkatesh V (2008) Genetic programming approach for flood routing in natural channels. Hydrol Process 22:623–628. https://doi.org/10.1002/hyp.6628
Sridhar S, Maheswaran R, Radha D and Sridhar V (2020) Attribution of hydrologic changes in a tropical river basin to rainfall variability and land use change: A case study from India, Journal of Hydrol.Engg. 25(8).
Turcotte R, Fortin J-P, Rousseau A et al (2001) Determination of the drainage structure of a watershed using a digital elevation model and a digital river and lake network. J Hydrol 240:225–242. https://doi.org/10.1016/S0022-1694(00)00342-5
Valeriano MM, Rossetti DF (2017) Regionalization of local geomorphometric derivations for geological mapping in the sedimentary domain of central Amazônia. Comput Geosci 100:46–56. https://doi.org/10.1016/j.cageo.2016.12.002
Venkatesh K, Ramdas P, Sankar Rao L et al (2020) Comparison of different Digital Elevation Models for Drainage Morphometric Parameters: A case study. (Accepted in Arabian Journal of Geosciences)
Vu MT, Aribarg T, Supratid S, Raghavan SV, Liong SY (2016) Statistical downscaling rainfall using artificial neural network: significantly wetter Bangkok? Theor Appl Climatol 126:453–467. https://doi.org/10.1007/s00704-015-1580-1
Wendi D, Liong S-Y, Sun Y, Doan CD (2016) An innovative approach to improve SRTM DEM using multispectral imagery and artificial neural network. J Adv Model Earth Syst 8:691–702. https://doi.org/10.1002/2015MS000536
Yeditha PK, Venkatesh K, Rathinasamy M, Agarwal A (2020) Forecasting of extreme flood events using different satellite precipitation products and wavelet-based machine learning methods. Chaos 30(063115):1–25. https://doi.org/10.1063/5.0008195
Yue L, Shen H, Zhang L, Zheng X, Zhang F, Yuan Q (2017) High-quality seamless DEM generation blending SRTM-1, ASTER GDEM v2 and ICESat/GLAS observations. ISPRS J Photogramm Remote Sens 123:20–34. https://doi.org/10.1016/j.isprsjprs.2016.11.002
Zheng X, Xiong H, Gong J, Yue L (2015) A robust channel network extraction method combining discrete curve evolution and the skeleton construction technique. Adv Water Resour 83:17–27. https://doi.org/10.1016/j.advwatres.2015.05.003
Acknowledgments
Dr. R. Maheswaran gratefully acknowledges the funding received from the Department of Science and Technology, Water Technology Initiative under the project DST/WTI/DD/2k17/0079. The authors also acknowledge Surveycon Ltd. Visakhapatnam for providing the DGPS survey data used in this study.
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Kasi, V., Yeditha, P., Rathinasamy, M. et al. A novel method to improve vertical accuracy of CARTOSAT DEM using machine learning models. Earth Sci Inform 13, 1139–1150 (2020). https://doi.org/10.1007/s12145-020-00494-1
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DOI: https://doi.org/10.1007/s12145-020-00494-1