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

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 droughtrelated 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.

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

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 droughtrelated 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.