Application of Artificial Neural Network and Fuzzy regression in remotely sensed monitoring of drought

Author

Assistant Professor, Collage of Agriculture, Azarbaijan Shahid Madani University

Abstract

Drought monitoring is a major issue for agricultural water management and environmental protection. In this study, artificial neural network and fuzzy regression models have been used to evaluate the performance of several remotely sensed indices retrieved from MODIS images including NDVI, VTCI, VHI, NVSWI,TCI andTVXfor monitoring drought in 7 meteorological station across Iran namely Kermanshah, Tabriz, Kerman,Mashhad, Urumia,Yazd and Zanjan. The VHI, NVSWI, TCI and TVX had the highest number of significant coefficient of correlation with amount of rainfall in study stations. Based on error measures, the Fuzzy regression approach had the least error in modeling correlation of VHI, TCI and NVSWI with rainfall amount. Using the ANN model, the TVX found to be the best index in monitoring drought with highest accuracy. The results revealed that in the case of symmetric membership functions, changing the value of confidence level parameter would affect the value of fuzzy spread coefficient. For example increasing the confidence level parameter in case of VHI from 0.7 to 0.8 led to 50% increase of spread. In case of non-symmetric fuzzy coefficient, the peak point is sensitive to skewness factors; such that its value was increased for 22.2% moving from minimum to maximum skewness factor in case of TVX index. The decrease in confidence level parameter of TVX, which represents the degree of fuzziness, confirmed the better performance of artificial neural network in correlating TVX index and rainfall.

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References

ثقفیان، ب. 1387. پیش‌نویس برنامه راهبردی: ارزیابی و پیش‌بینی خشکسالی منطقه‌ای کشور. مرکز تحقیقات کشاورزی و منابع طبیعی خراسان رضوی. وزارت جهاد کشاورزی، ستاد هماهنگی طرح‌های پژوهشی همزیستی با خشکی.
شکیبا، ع.، میرباقری، ب.، خیری، ا. 1389. خشکسالی و تأثیر آن بر منابع آب زیرزمینی در شرق استان کرمانشاه با استفاده از شاخص .SPI فصلنامه علمی- پژوهشی انجمن جغرافیای ایران، 8 (25): 105-124.
کارآموز، م.، عراقی‌نژاد، ش. 1384. هیدرولوژی پیشرفته. انتشارات دانشگاه صنعتی امیرکبیر، 465 صفحه.
کوره‌پزان، ا. 1384. اصول تئوری مجموعه‌های فازی و کاربردهای آن در مدل‌سازی مهندسی منابع آب. انتشارات جهاد دانشگاهی واحد صنعتی امیرکبیر، 272 صفحه.
 محمدی‌نژاد، ا. 1391. استفاده از شبکه‌های بازگشتی به منظور مدل‌سازی و پیش‌بینی خشکسالی مبتنی بر داده‌های سنجش از دور. پایان‌نامه کارشناسی ارشد، رشته مهندسی عمران، دانشگاه صنعتی شریف.
Bayarjarga, Y. L., Karnieli, A., Bayasgalan, M., Khudulmur, S., Gandush, C., Tucker, C. J. 2006. A comparative study of NOAA–AVHRR derived drought indices using change vector analysis. Remote Sens. Environ., 105(1): 9–22.
Du, L., Tiana, Q., Yu, T., Meng, Q., Jancso, T., Udvardy, P., Huang, Y. 2013. A comprehensive drought monitoring method integrating MODIS and TRMM data. Int. J. Appl. Earth Obs. Geoinf., 23(1): 245–253.
Fatehi-Marj, A., Meijerink, A. 2011. Agricultural drought forecasting using satellite images, climate indices and artificial neural network. Int. J. Remote Sens., 32(24): 9707-9719.
Fernandez-Manso, A., Quintano, C., Fernandez-Manso, O. 2011. Forecast of NDVI in coniferous areas using temporal ARIMA analysis and climatic data at a regional scale. Int. J. Remote Sens., 32(6): 1595-1617.
Jafari, R., Bakhshandehmehr, L. 2013. Quantitative mapping and assessment of environmentally sensitive areas to desertification in central Iran. Land Degrad. Dev., 27(2): 108-119. 
Kogan, F. N. 1997. Global drought watch from space. Bull. Amer. Meteorol. Soc., 78(4): 621–636.
Kizil, Ü., GenÇ, L., İnalplat, M., Şapoloyo, D., Mirik, M. 2012.  Lettuce yield prediction under water stress using artificial neural network (ANN) model and vegetation indices. Žemdirbystė- Agric., 99(4): 409- 418.
Nichol, J. E., Abbas, S. 2015. Integration of remote sensing datasets for local scale assessment and prediction of drought. Sci. Total Environ., 505: 503–507.
Özger, M., Mishra, A. K. 2012. Long lead time drought forecasting using wavelet and fuzzy logic combination model: a case study in Texas. J. Hydrometeorol., 13(1): 284-297.
Shamsipour, A. A., Alavipanah, S. K. 2010. The role of fuzzy - AHP models in the efficiency of remotely sensed based drought indices in Kashan district, 30th EARSeL Symposium: Remote Sensing for Science, Education and Culture, France, Paris, 31 May - 03 Jun 2010.
Stepchenko, A., Chizhov, J. 2015. NDVI short-term forecasting using recurrent neural nNetworks. Proceedings of the 10th International Scientific and Practical Conference., 3:180-185.
Wan, Z., Wang, P., Li, X. 2004. Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA. Int. J. Remote Sens., 25(1): 61–72.
Yen, K. K., Ghoshary, S., Roig, G. 1999. A linear model using triangular fuzzy number coefficients. Fuzzy Sets and Syst., 106:167-177.

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