The Feasibility of Machine-learning Methods to Extract the Surface Evaporation Quantity using Satellite Imagery

Norouzi, E.; Behzadi, S.

doi:10.22061/jecei.2021.7563.406

Document Type : Original Research Paper

Authors

Surveying Engineering Department, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

https://doi.org/10.22061/jecei.2021.7563.406

Abstract

Background and Objectives: Climate phenomena such as quantity of surface evaporation are affected by many environmental factors and parameters, which makes modeling and data mining difficult. On the other hand, the estimation of surface evaporation for a target station can be difficult as a result of partial or complete lack of local meteorological data under many conditions. In this regard, satellite imagery can play a special role in modeling and data mining of climatic phenomena, because of their significant advantages, including availability and their potential analysis. Therefore, addressing the improvement and expansion of machine learning methods and modeling algorithms along with remote sensing data is inevitable.
Methods: In this research, we intend to study the ability of 11 machine-learning modeling algorithms to model data and surface evaporation phenomena using satellite imagery. We used two methods to prepare the database: PCA and its opposite method using standard deviation and correlation.
Results: The calculation of the Root Mean Squared Error (RMSE) indicated that, in general, the use of the PCA method has a better result in preparing and reducing the dimensions of large databases for all methods of machine learning. The SEGPR model was ranked first with the least error (93.49%) in the Principal Component Analysis (PCA) method, and the Artificial Neural Network (ANN) model performed well in both data preparation methods (93.42, 93.38), and the Classification-Tree-Coarse model had the highest error in both methods (92.66, 92.67).
Conclusion: Consequently, it can be said that by changing the methods of database preparation in order to train models, the modeling results can be changed effectively.

Keywords

20.1001.1.23223952.2021.9.2.10.8

Main Subjects

Geographic Information System

Open Access

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Publisher

Shahid Rajaee Teacher Training University

References

[1] F.E. Jones, Evaporation of water with emphasis on applications and measurements: CRC Press, 2018.

[2] G.A. Martin, A.C. Kidd, S. Tsim, P. Halford, A. Bibby, N. A. Maskell, et al., "Inter‐observer variation in image interpretation and the prognostic importance of non‐expansile lung in malignant pleural effusion," Respirology, 25(3): 298-304, 2020.

[3] A. Malik, A. Kumar, O. Kisi, "Monthly pan-evaporation estimation in Indian central Himalayas using different heuristic approaches and climate based models," Comput. Electron. Agric., 143: 302-313, 2017.

[4] S. Behzadi, A. Jalilzadeh, "Introducing a novel digital train model using artificial neural network algorithm," Civ. Eng. Dimen., 22(2): 47-51, 2020.

[5] A. Jalilzadeh, S. Behzadi, "Machine learning method for predicting the depth of shallow lakes using multi-band remote sensing images," Soft Comput. Civ. Eng., 3(2): 59-68, 2019.

[6] S. Behzadi, Z. Mousavi, E. Norouzi, "Mapping historical water-supply qanat based on fuzzy method. an application to the Isfahan Qanat (Isfahan, Iran)," Int. J. Numer. Methods in Civ. Eng., 3(4): 24-32, 2019.

[7] S. Behzadi, M. Kolbadinejad, "Introducing a novel method to solve shortest path problems based on structure of network using genetic algorithm," Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., 2019.

[8] A. Malik, A. Kumar, S. Kim, M. H. Kashani, V. Karimi, A. Sharafati, et al., "Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model," Eng. Appl. Comput. Fluid Mech., 14(1): 323-338, 2020.

[9] L. Wang, B. Hu, O. Kisi, M. Zounemat‐Kermani, and W. Gong, "Prediction of diffuse photosynthetically active radiation using different soft computing techniques," Q. J. R. Meteorolog. Soc., 143(706): 2235-2244, 2017.

[10] J. Sulaiman, S.H. Wahab, "Heavy rainfall forecasting model using artificial neural network for flood prone area," in IT Convergence and Security 2017, ed: Springer: 68-76, 2018.

[11] 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-based machine learning models," J. Hydrol., 566: 668-684, 2018.

[12] S. Ghimire, R.C. Deo, N.J. Downs, N. 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," Remote Sens. Environ., 212: 176-198, 2018.

[13] T. Xu, Z. Guo, S. Liu, X. He, Y. Meng, Z. Xu, et al., "Evaluating different machine learning methods for upscaling evapotranspiration from flux towers to the regional scale," J. Geophys. Res.: Atmos., 123: 8674-8690, 2018.

[14] Q. Zhou, A. Flores, N.F. Glenn, R. Walters, B. 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," PLoS One, 12: e0180239, 2017.

[15] D.P. Solomatine, K.N. Dulal, "Model trees as an alternative to neural networks in rainfall—runoff modelling," Hydrol. Sci. J., 48: 399-411, 2003.

[16] M. Kühnlein, T. Appelhans, B. Thies, T. Nauss, "Improving the accuracy of rainfall rates from optical satellite sensors with machine learning—A random forests-based approach applied to MSG SEVIRI," Remote Sens. Environ., 141: 129-143, 2014.

[17] C. Doña, N.-B. Chang, V. Caselles, J. M. Sánchez, L. Pérez-Planells, M.D. M. Bisquert, et al., "Monitoring hydrological patterns of temporary lakes using remote sensing and machine learning models: Case study of la Mancha Húmeda Biosphere Reserve in central Spain," Remote Sens., 8(8): 618, 2016.

[18] E. Norouzi, S. Behzadi, "Evaluating machine learning methods and satellite images to estimate combined climatic indices," Int. J. Numer. Methods Civ. Eng., 4(1): 30-38, 2019.

[19] F. Ling, G. M. Foody, H. Du, X. Ban, X. Li, Y. Zhang, et al., "Monitoring thermal pollution in rivers downstream of dams with Landsat ETM+ thermal infrared images," Remote Sens., 9(11): 1175, 2017.

[20] M. Kaidan, T. Maksymyuk, V. Andrushchak, M. Klymash, "Intelligent data flow aggregation in edge nodes of optical label switching networks," in Proc. 2019 3rd International Conference on Advanced Information and Communications Technologies (AICT): 145-148, 2019.

[21] R. Mitchell, J. Michalski, T. Carbonell, An artificial intelligence approach: Springer, 2013.

[22] I.T. Jolliffe, J. Cadima, "Principal component analysis: a review and recent developments," Philos. Trans. Roy. S. A: Math. Phy. Eng. Sci., 374: 20150202, 2016.

[23] D. Karaboga, E. Kaya, "Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey," Artif. Intell. Rev. 52(4): 2263-2293, 2019.

[24] J. Wu, Y. Su, Y. Cheng, X. Shao, C. Deng, C. Liu, "Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system," Appl. Soft Comput., 68: 13-23, 2018.

[25] S.J. Narayanan, I. Paramasivam, R.B. Bhatt, "On the estimation of optimal number of clusters for the induction of fuzzy decision trees," Int. J. Data Sci., 2(3): 221-245, 2017.

[26] K. Khamar, "Short text classification using kNN based on distance function," Int. J. Adv. Res. Compu. Commun. Eng., 2(4): 1916-1919, 2013.

[27] O. Kisi, O. Genc, S. Dinc, M. Zounemat-Kermani, "Daily pan evaporation modeling using chi-squared automatic interaction detector, neural networks, classification and regression tree," Comput. Electron. Agric., 122: 112-117, 2016.

[28] O. Genç, B. Gonen, M. Ardıçlıoğlu, "A comparative evaluation of shear stress modeling based on machine learning methods in small streams," J. Hydroinf., 17(5): 805-816, 2015.

[29] M.L. Van Horn, T. Jaki, K. Masyn, G. Howe, D.J. Feaster, A.E. Lamont, et al., "Evaluating differential effects using regression interactions and regression mixture models," Educ. Physiol. Meas., 75(4): 677-714, 2015.

[30] H. Jafarian, S. Behzadi, "Evaluation of PM2. 5 emissions in Tehran by means of remote sensing and regression models," Pollution, 6(3): 521-529, 2020.

[31] M. Pal, S. Deswal, "Modelling pile capacity using Gaussian process regression," Comput. Geotech., 37(7-8): 942-947, 2010.

[32] R.U. Maheswari, S. Rajalingam, T. Senthilkumar, "Condition monitoring of coal mine using ensemble boosted tree regression model," in Intelligent Communication Technologies and Virtual Mobile Networks: 19-29, 2019.

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Journal of Electrical and Computer Engineering Innovations (JECEI)

The Feasibility of Machine-learning Methods to Extract the Surface Evaporation Quantity using Satellite Imagery

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References

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Volume 9, Issue 2
July 2021
Pages 229-238

The Feasibility of Machine-learning Methods to Extract the Surface Evaporation Quantity using Satellite Imagery

References

References

Send comment about this article

Volume 9, Issue 2July 2021Pages 229-238

Volume 9, Issue 2
July 2021
Pages 229-238