Volume 4, Issue 1 (9-2019)                   NMCE 2019, 4(1): 30-38 | Back to browse issues page

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Norouzi E, Behzadi S. Evaluating machine learning methods and satellite images to estimate combined climatic indices. NMCE. 2019; 4 (1) :30-38
URL: http://nmce.kntu.ac.ir/article-1-231-en.html
Assistant Professor in Survey Engineering, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran. , behzadi.saeed@gmail.com
Abstract:   (300 Views)
The reflections recorded on satellite images have been affected by various environmental factors. In these images, some of these factors are combined with other environmental factors that cannot be distinguished. Therefore, it seems wise to model these environmental phenomena in the form of hybrid indicators. In this regard, satellite imagery and machine learning methods can play a unique role in modeling and data mining of climatic phenomena as a result of their significant advantages, including their availability and analysis. Therefore, addressing the improvement and expansion of machine learning methods and modeling algorithms using remote sensing data is inevitable. In this study, 7 well-known machine learning algorithms are applied with different initial data to show that satellite images are able to estimate the combined indices more accurately. A new index (HT) is also defined by combining the quantities of relative humidity and temperature. Then, machine learning algorithms are trained for each of these three quantities. For each of the temperature and relative humidity quantities, four optimal bands were selected using the PCA method, then a combination of these optimal bands was determined for the HT index. Two criteria are used to validate the results: Root Mean Square Error (RMSE) statistic and comparing the map of the interpolation method with the result of this study. RMSE values show that satellite imagery could have a high ability to model and estimate composite indices. Classification-KNN-Coarse and Ensemble-Bagged Trees with accuracy of 79.8626 % and 84.9281% are identified as the best machine learning methods for temperature and relative humidity, while the best accuracy to estimate the HT index is 92.8792% for Matern 5/2 GPR. Therefore, it can be said that by changing the methods of database preparation, the modeling results can be changed effectively in order to train models.
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Type of Study: Research | Subject: General

1. [1] Q. Weng, Advances in environmental remote sensing: sensors, algorithms, and applications, CRC Press, 2011. [DOI:10.1201/b10599]
2. [2] S. Behzadi, Z. Mousavi, E. Norouzi, Mapping Historical Water-Supply Qanat Based On Fuzzy Method. An Application to the Isfahan Qanat (Isfahan, Iran), International Journal of Numerical Methods in Civil Engineering, 3(4) (2019) 24-32. [DOI:10.29252/nmce.3.4.24]
3. [3] Ö.J.I.S. Kişi, Modeling monthly evaporation using two different neural computing techniques, 27(5) (2009) 417-430. [DOI:10.1007/s00271-009-0158-z]
4. [4] Ö. Kişi, Evolutionary neural networks for monthly pan evaporation modeling, Journal of Hydrology, 498 (2013) 36-45. [DOI:10.1016/j.jhydrol.2013.06.011]
5. [5] O. Kisi, Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree, Journal of Hydrology, 528 (2015) 312-320. [DOI:10.1016/j.jhydrol.2015.06.052]
6. [6] S. Kim, J. Shiri, O.J.W.R.M. Kisi, Pan Evaporation Modeling Using Neural Computing Approach for Different Climatic Zones, 26(11) (2012) 3231-3249. [DOI:10.1007/s11269-012-0069-2]
7. [7] A. Guven, O. Kisi, Monthly pan evaporation modeling using linear genetic programming, Journal of Hydrology, 503 (2013) 178-185. [DOI:10.1016/j.jhydrol.2013.08.043]
8. [8] Ö. Kişi, M.J.J.o.h. Tombul, Modeling monthly pan evaporations using fuzzy genetic approach, 477 (2013) 203-212. [DOI:10.1016/j.jhydrol.2012.11.030]
9. [9] M.K. Goyal, B. Bharti, J. Quilty, J. Adamowski, A. Pandey, Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS, Expert Systems with Applications, 41(11) (2014) 5267-5276. [DOI:10.1016/j.eswa.2014.02.047]
10. [10] L. Wang, B. Hu, O. Kisi, M. Zounemat‐Kermani, W.J.Q.J.o.t.R.M.S. Gong, Prediction of diffuse photosynthetically active radiation using different soft computing techniques, 143(706) (2017) 2235-2244. [DOI:10.1002/qj.3081]
11. [11] A. Malik, A. Kumar, O.J.C. Kisi, E.i. Agriculture, Monthly pan-evaporation estimation in Indian central Himalayas using different heuristic approaches and climate based models, 143 (2017) 302-313. [DOI:10.1016/j.compag.2017.11.008]
12. [12] D.P. Solomatine, K.N.J.H.S.J. Dulal, Model trees as an alternative to neural networks in rainfall-runoff modelling, 48(3) (2003) 399-411. [DOI:10.1623/hysj.48.3.399.45291]
13. [13] J. Sulaiman, S.H. Wahab, Heavy Rainfall Forecasting Model Using Artificial Neural Network for Flood Prone Area, in: IT Convergence and Security 2017, Springer, 2018, pp. 68-76. [DOI:10.1007/978-981-10-6451-7_9]
14. [14] X. Lu, Y. Ju, L. Wu, J. Fan, F. Zhang, Z. Li, Daily pan evaporation modeling from local and cross-station data using three tree-basedmachine learning models, Journal of Hydrology, 566 (2018) 668-684. [DOI:10.1016/j.jhydrol.2018.09.055]
15. [15] S.A. Naghibi, H.R. Pourghasemi, B.J.E.m. Dixon, assessment, GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran, 188(1) (2016) 44. [DOI:10.1007/s10661-015-5049-6]
16. [16] M. Kühnlein, T. Appelhans, B. Thies, T.J.R.S.o.E. Nauss, Improving the accuracy of rainfall rates from optical satellite sensors with machine learning-A random forests-based approach applied to MSG SEVIRI, 141 (2014) 129-143. [DOI:10.1016/j.rse.2013.10.026]
17. [17] T. Lillesand, R.W. Kiefer, J. Chipman, Remote sensing and image interpretation, John Wiley & Sons, 2014.
18. [18] S. Ghimire, R.C. Deo, N.J. Downs, N.J.R.S.o.E. Raj, Self-adaptive differential evolutionary extreme learning machines for long-term solar radiation prediction with remotely-sensed MODIS satellite and Reanalysis atmospheric products in solar-rich cities, 212 (2018) 176-198. [DOI:10.1016/j.rse.2018.05.003]
19. [19] T. Xu, Z. Guo, S. Liu, X. He, Y. Meng, Z. Xu, Y. Xia, J. Xiao, Y. Zhang, Y.J.J.o.G.R.A. Ma, Evaluating different machine learning methods for upscaling evapotranspiration from flux towers to the regional scale, 123(16) (2018) 8674-8690. [DOI:10.1029/2018JD028447]
20. [20] C. Doña, N.-B. Chang, V. Caselles, J.M. Sánchez, L. Pérez-Planells, M.d.M. Bisquert, V. García-Santos, S. Imen, A.J.R.S. Camacho, Monitoring hydrological patterns of temporary lakes using remote sensing and machine learning models: Case study of la Mancha Húmeda Biosphere Reserve in central Spain, 8(8) (2016) 618. [DOI:10.3390/rs8080618]
21. [21] Q. Zhou, A. Flores, N.F. Glenn, R. Walters, B.J.P.o. Han, A machine learning approach to estimation of downward solar radiation from satellite-derived data products: An application over a semi-arid ecosystem in the US, 12(8) (2017) e0180239. [DOI:10.1371/journal.pone.0180239]
22. [22] K. Kuwata, R. Shibasaki, Estimating crop yields with deep learning and remotely sensed data, in: Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International, IEEE, 2015, pp. 858-861. [DOI:10.1109/IGARSS.2015.7325900]
23. [23] J. Rogan, J. Franklin, D. Stow, J. Miller, C. Woodcock, D. Roberts, Mapping land-cover modifications over large areas: A comparison of machine learning algorithms, Remote Sensing of Environment, 112(5) (2008) 2272-2283. [DOI:10.1016/j.rse.2007.10.004]
24. [24] G.B. Anderson, M.L. Bell, R.D. Peng, Methods to calculate the heat index as an exposure metric in environmental health research, Environmental health perspectives, 121(10) (2013) 1111-1119. [DOI:10.1289/ehp.1206273]
25. [25] M.S. Jin, Developing an index to measure urban heat island effect using satellite land skin temperature and land cover observations, Journal of Climate, 25(18) (2012) 6193-6201. [DOI:10.1175/JCLI-D-11-00509.1]
26. [26] L.P. Rothfusz, N.S.R. Headquarters, The heat index equation (or, more than you ever wanted to know about heat index), Fort Worth, Texas: National Oceanic and Atmospheric Administration, National Weather Service, Office of Meteorology, 9023 (1990).
27. [27] L. Bastistella, P. Rousset, A. Aviz, A. Caldeira-Pires, G. Humbert, M. Nogueira, Statistical Modelling of Temperature and Moisture Uptake of Biochars Exposed to Selected Relative Humidity of Air, Bioengineering, 5(1) (2018). [DOI:10.3390/bioengineering5010013]
28. [28] T.U.S.G.S.E.E.A.o. http://earthexplorer.usgs.gov.
29. [29] F. Ling, G.M. Foody, H. Du, X. Ban, X. Li, Y. Zhang, Y.J.R.S. Du, Monitoring thermal pollution in rivers downstream of dams with Landsat ETM+ thermal infrared images, 9(11) (2017) 1175. [DOI:10.3390/rs9111175]
30. [30] M. Goodson, Preparing Your Dataset for Machine Learning: 8 Basic Techniques That Make Your Data Better, (2017).
31. [31] R.S. Michalski, J.G. Carbonell, T.M. Mitchell, Machine learning: An artificial intelligence approach, Springer Science & Business Media, 2013.
32. [32] I.T. Jolliffe, J.J.P.T.R.S.A. Cadima, Principal component analysis: a review and recent developments, 374(2065) (2016) 20150202. [DOI:10.1098/rsta.2015.0202]
33. [33] A. Jalilzadeh, S. Behzadi, Machine Learning Method for predicting the depth of shallow lakes Using Multi-Band Remote Sensing Images, Soft Computing in Civil Engineering, 3(2) (2019) 59-68.
34. [34] J.-S.J.I.t.o.s. Jang, man,, cybernetics, ANFIS: adaptive-network-based fuzzy inference system, 23(3) (1993) 665-685. [DOI:10.1109/21.256541]
35. [35] A. Abraham, Adaptation of fuzzy inference system using neural learning, in: Fuzzy systems engineering, Springer, 2005, pp. 53-83. [DOI:10.1007/11339366_3]
36. [36] K.J.I.J.o.A.R.i.C. Khamar, C. Engineering, Short text classification using kNN based on distance function, 2(4) (2013) 1916-1919.
37. [37] R.A. Maronna, R.D. Martin, V.J. Yohai, M. Salibián-Barrera, Robust statistics: theory and methods (with R), Wiley, 2018. [DOI:10.1002/9781119214656]
38. [38] C.J.J.D.m. Burges, k. discovery, A tutorial on support vector machines for pattern recognition, 2(2) (1998) 121-167.
39. [39] M. Pal, S.J.C. Deswal, Geotechnics, Modelling pile capacity using Gaussian process regression, 37(7-8) (2010) 942-947. [DOI:10.1016/j.compgeo.2010.07.012]
40. [40] P. Van der Linden, J. Mitchell, editors, ENSEMBLES: Climate change and its impacts-Summary of research and results from the ENSEMBLES project, (2009).

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