Data Fusion and Machine Learning Algorithms for Drought Forecasting Using Satellite Data

Document Type : Research Article

Authors

1 M.Sc. Graduated, Remote Sensing Division, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

2 Assistant Professor, Remote Sensing Division, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract

Drought is one of the natural disasters in the world, which is associated with various global factors, most of which can be observed using remote sensing techniques. One of the factors affecting agricultural drought is the vegetation associated with other drought-related factors. These parameters have a complicated relationship with each other, so machine learning algorithms can be used to predict better and model this phenomenon. Factors considered in this study include vegetation as the most critical factor, Land Surface Temperature (LST), Evapo Transpiration (ET), snow cover, rainfall, soil moisture these are derived from the active and passive sensors of satellite sensors as the products of LST, snow cover and vegetation using images of products of the MODIS sensor, rainfall using the images of the TRMM satellite, and soil moisture using the images of the SMOS satellite during a period from June 2010 to the end of 2018 for the central region of Iran. After that, primary processing was performed on these images. The vegetation index (NDVI) is modelled and predicted using an Artificial Neural Network algorithm (ANN), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF) for monthly periods. By using these methods we have been able to present a model with desirable accuracy. The ANN approach has provided higher accuracy than the other three algorithms. Also, an average accuracy with RMSE=0.0385 and =0.8740 was achieved.

Keywords

Main Subjects

References

Ahmed, N.K., Atiya, A.F. El Gayar, N. and El-Shishiny, H., 2010, An empirical comparison of machine learning models for time series forecasting,  Econometric Reviews, 29 (5-6), 594-621.
Alizadeh, M.R. and Nikoo, M.R., 2018, A fusion-based methodology for meteorological drought estimation using remote sensing data.  Remote Sensing of Environment, 211, 229-247.
Bai, J., Cui, Q., Chen, D., Yu, H., Mao, X., Meng, L. and Cai, Y., 2018, Assessment of the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) as an Agricultural Drought Index in China.  Remote Sensing, 10(8), 1302.
Barua, S., Ng, A.W.M. and Perera, B.J.C., 2012, Artificial neural network–based drought forecasting using a nonlinear aggregated drought index. Journal of Hydrologic Engineering, 17(12), 1408-1413.
Belayneh, A., Adamowski, J., Khalil, B. and Ozga-Zielinski, B., 2014, Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models. Journal of Hydrology, 508, 418-429.
Belayneh, A. and Adamowski, J., 2013, Drought forecasting using new machine learning methods/Prognozowanie suszy z wykorzystaniem automatycznych samouczących się metod. Journal of Water and Land Development, 18(9), 3-12.
Benesty, J., Chen, J., Huang, Y. and Cohen, I., 2009, Pearson correlation coefficient. In Noise Reduction in Speech Processing, 1-4, Springer.
Breiman, L., 2001, Random forests.  Machine Learning, 45(1), 5-32.
Breiman, L., 2017, Classification and Regression Trees: Routledge.
Chang, C.-C. and Lin, C.J., 2001, LIBSVM: a library for support vector machines ACM Trans. Intell Syst Technol, 2(3).
Cimen, M., 2008, Estimation of daily suspended sediments using support vector machines. Hydrological Sciences Journal, 53(3), 656-666.
Cortes, C. and Vapnik, V., 1995, Support-vector networks.  Machine Learning, 20(3), 273-297.
Daubechies, I., 1992, Ten Lectures on Wavelets. Vol. 61: Siam.
Duan, Z. and Bastiaanssen, W.G.M., 2013, First results from Version 7 TRMM 3B43 precipitation product in combination with a new downscaling–calibration procedure.  Remote Sensing of Environment, 131, 1-13.
Heumann, B.W., 2011, Satellite remote sensing of mangrove forests: Recent advances and future opportunities.  Progress in Physical Geography, 35(1), 87-108.
Kerr, Y.H., Waldteufel, P., Wigneron, J.-P., Delwart, S. Cabot, F. Boutin, J. Escorihuela, M.-J., Font, J., Reul, N. and Gruhier, C., 2010, The SMOS mission: New tool for monitoring key elements ofthe global water cycle.  Proceedings of the IEEE, 98(5), 666-687.
Kim, T.-W. and Valdés, J.B., 2003, Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. Journal of Hydrologic Engineering, 8(6), 319-328.
Kogan, F.N., 1995, Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data. Bulletin of the American Meteorological Society, 76(5), 655-668.
Kogan, F.N., 2000, Contribution of remote sensing to drought early warning.  Early Warning Systems for Drought Preparedness and Drought Management, 75-87.
Modarres, R., 2006, Regional precipitation climates of Iran.  Journal of Hydrology (New Zealand), 13-27.
Mokhtari Dehkordi, R. and Akhoondzadeh, M., 2020, Combining Neural Network and Wavelet Transform to Predict Drought in Iran Using MODIS and TRMM Satellite Data. Journal of Geospatial Information Technology, 7(4), 175-191.
Nason, G.P. and von Sachs, R., 1999, Wavelets in time-series analysis.  Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 357(1760), 2511-2526.
Park, S., Im, J., Park, S. and Rhee, J., 2017, Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula. Agricultural and Forest Meteorology, 237, 257-269.
Ramoelo, A., Majozi, N., Mathieu, R., Jovanovic, N., Nickless, A. and Dzikiti, S., 2014, Validation of global evapotranspiration product (MOD16) using flux tower data in the African savanna, South Africa. Remote Sensing, 6(8), 7406-7423.
Sánchez, N., González-Zamora, Á., Piles, M. and Martínez-Fernández, J., 2016, A new Soil Moisture Agricultural Drought Index (SMADI) integrating MODIS and SMOS products: a case of study over the Iberian Peninsula.  Remote Sensing, 8(4), 287.
Szalai, S. and Szinell, C.S., 2000, Comparison of two drought indices for drought monitoring in Hungary—a case study. In Drought and Drought Mitigation in Europe, 161-166, Springer.
Wilhite, D.A. and Buchanan-Smith, M., 2005, Drought as hazard: understanding the natural and social context.  Drought and Water Crises: Science, Technology, and Management Issues, 3-29.
Zhang, A. and Jia, G., 2013, Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data.  Remote Sensing of Environment, 134, 12-23.
Zhang, H., Chen, L., Qu, Y., Zhao, G. and Guo, Z., 2014, Support vector regression based on grid-search method for short-term wind power forecasting. Journal of Applied Mathematics.
Journal of the Earth and Space Physics
Volume 46, Issue 4
February 2021
Pages 231-246

Files

Share

How to cite

Statistics