5. Satellite precipitation products (SPPs)

Ali, Deo, et al. [43] evaluated the performance of three models (ANFIS, M5Tree, and MPMR) to forecast SPI3, SPI6, and SPI12 calculated from a 35-year rainfall data set (1981–2015) from three (3) stations in Pakistan. SPI data were partitioned into 70% (training) and 30% (testing) periods. M5Tree is a kind of decision tree with linear regression functions on the leaves [72], whereas MPMR stands for minimax probability machine regression [73] and was also applied to benchmark the ensemble-ANFIS model. Regarding SPI3 forecast, ANFIS (R = 0.889 to 0.946) outperformed MPMR (R = 0.843 to 0.935) and M5Tree (R = 0.831 to 0.916). Similarly, SPI6 ANFIS (R = 0.968 to 0.974) outperformed M5Tree (R = 0.950 to 0.967) and MPMR (R = 0.952 to 0.970). For SPI12, ANFIS (R = 0.987 to 0.993) overcome M5Tree (R = 0.950 to 0.967) and MPMR (R = 0.984 to 0.986). The other statistics (e.g., RMSE, WI) corroborated the superior performance of ANFIS. Just as important, the ensemble-ANFIS model achieved the highest accuracy at the three

stations when predicting moderate, severe, and extreme droughts.

, 10+3] and [2<sup>3</sup>

the accuracy of RF was less affected by the longer lead time.

decomposition significantly improves the quality of prediction.

1000) for g and (1, 0.5 and 1) for γ (LS-SVR).

within the range of [10<sup>3</sup>

Drought - Detection and Solutions

1 neuron, respectively.

rainfall prediction.

16

Khosravi et al. [74] used rainfall data from the Tropical Rainfall Measuring Mission (TRMM) during 2000–2014 in the eastern district of Isfahan to generate 12-month SPI. The first 85% of the data was used to train a single-hidden layer feedforward ANN, an SVR with RBF kernel, an LS-SVR with RBF kernel, and an ANFIS method. Optimum values of SVR and LS-SVR were obtained by a grid search

For SPI12, SVR achieved the highest accuracy (RMSE = 0.21), followed by LS-SVR (RMSE = 0.38), ANN (RMSE = 1.24), and ANFIS (RMSE = 1.36). The best ANN model consisted of three layers (input, hidden, and output) with 30, 8, and

Chen et al. [75] evaluated RF and ARIMA to forecast SPI3 (short-term drought) with a 1-month lead time and SPI12 (long-term drought). Both models were developed based on data from 1966 to 1995 (four stations in China), and predictions (1 month or 6 months ahead) were made from 1996 to 2004. Overall, RF performed consistently better than ARIMA. Results also suggested that RF is more robust in predicting dry events. Finally, ARIMA lost the capacity to predict SPI12, whereas

Agana and Homaifar [76] developed a hybrid model using a denoised empirical mode decomposition [77] and DBN. The proposed method was applied to predict a standardized streamflow index (SSI) across the Colorado River basin (ten stations). The new model was compared with MLP and SVR in predicting SSI12 (1, 6, and 12 months lead time). DBN, SVR, and their hybrid versions displayed rather similar prediction errors. However, DBN and EMD-DBN outperformed all other models for two-step predictions at almost all stations. As in wavelets, the empirical mode

Finally, we want to mention two examples where ML was directly applied to

Sumi et al. [78] compared ANNs, multivariate adaptive regression splines or MARS [79], k-nearest neighbor [80], and SVR with RBF kernel to predict daily and monthly rainfall in Fukuoka, Japan. A preprocessed training set (1975–2004) was

El Shafie et al. [77] evaluated a radial basis function neural network (RBFNN) to forecast rainfall in Alexandria City, Egypt. The model was trained using rainfall data from 1960 to 2001 (four stations) and tested with data from 2002 to 2009 to predict yearly and monthly (January and December) precipitation. Regarding yearly model efficiency, R2 = 0.94 for RBFNN, whereas the control (a multiple linear regression MR model) only reached R2 = 0.21. Regarding monthly precipitation, RBFNN was very successful (R2 = 0.899 for January and R2 = 0.997 for December) as compared to the control (R2 = 0.997 and 0.34, respectively).

, 2+3] for C and γ (SVR) or (10, 100 and

There is no satellite that can reliably quantify rainfall under all circumstances. However, ground observations, although reliable and with long-term records, do not provide a consistent spatial representation of precipitation, particularly on certain world regions. Therefore, satellite data become necessary, as they provide more homogeneous data quality compared to ground observations [81, 82]. To our knowledge, merged satellite-gauge products are becoming indispensable.

Precipitation data sets may be classified into one of four categories: gauge data sets (e.g., CRU TS [83], APHRODITE [84]), satellite-exclusive (e.g., CHOMPS [85]), merged satellite-gauge products (e.g., GPCP [86], TRMM3B42), and reanalysis (e.g., NCEP1/NCEP2 [87], ERA-Interim [88]). Reanalysis implies integrating irregular observations with models encompassing physical and dynamic processes in order to generate an estimate of the state of the system across a uniform grid and with temporal continuity [89].

Many studies show that satellite precipitation algorithms show different biases, detection probabilities, and missing rainfall ratios in summer and winter. Sources of error include the satellite sensor itself, the retrieval error [90], and spatial and temporal sampling [91, 92].

Algorithms that estimate rainfall from satellite observations are based on either thermal infrared (TIR) bands (inferring cloud-top temperature), passive microwave sensors (PMW), or active microwave sensors (AMW). The TIR-based approach takes into account cold cloud duration or CCD, that is, the time that a cloud has a temperature below the threshold at a given pixel [93]. The PMW-based approach takes advantage of the fact that microwaves can penetrate clouds to explore their internal properties through the interaction of raindrops [94]. AMW is what usually known as precipitation radar [95].

There is a plethora of validation studies of satellite-based rainfall estimates (SREs). Normally, these SREs are compared against ground rainfall estimates [91, 96].

Sun et al. [97] reviewed 30 currently available global precipitation (gauge-based, satellite-related, or reanalysis) data sets. The degree of variability of the precipitation estimates varies by region. Large differences in annual and seasonal estimates were found in tropical oceans, complex mountain areas, Northern Africa, and some high-latitude regions. Systematic errors are the main sources of errors over large parts of Africa, northern South America, and Greenland. Random errors are the dominant kinds of error in large regions of global land, especially at high latitudes. Regarding satellite assessments, PERSIANN-CCS has larger systematic errors than CMORPH, TRMM 3B42, and PERSIANN-CDR. The spatial distribution of systematic errors is similar for all reanalysis products [97].

Table 2 presents a comparison of several representative satellite rainfall products. More information regarding these and other products can be found in [97, 98].

Abbreviations: (IR) infrared satellite imagery, (MW) microwave estimates, (GG) ground gauges, (AMSU) Advanced Microwave Sounding Unit, (AMSU-B) Advanced Microwave Sounding Unit-B, (SSM/I) Special Sensor Microwave/Imager; (AMSR-E) Advanced Microwave Scanning Radiometer for the Earth Observing


accessed and controlled through an Internet-accessible application programming interface (API) and an associated web-based interactive development environment (IDE). It also possess a library with more than 800 functions, ranging from simple mathematical functions to powerful geostatistical, machine learning, and image

Satellite Data and Supervised Learning to Prevent Impact of Drought on Crop…

In many situations, intense computing resources are required for image processing operations [105], but more friendly solutions can be suggested to those

As mentioned before, many satellite precipitation products are freely available (Table 1). Most of them are in network Common Data Form (netCDF) format [95]. R users can access this format using the "ncdf4" [96] or "raster" [97] packages. These data were already processed and can be used to forecast and perform complementary analyses [98]. We have already mentioned the SPEI [59] or the SPI [60]

Regarding the ML methods discussed here, almost all of them are available in packages deposited at the CRAN or CRAN-like repositories, for example, "Random Forest" package [43], "rminer" [99] that implements ANN, SVR and boosting [99], etc. A full list of packages implementing ML algorithms is available at https://cran.r-

Finally, also available at the repositories are plenty of packages that are really

Climate change is shifting global rainfall patterns and will increase the intensity and duration of drought around the world; this produces the need to take contingency actions to prevent the impact of famine. ML models, an evolving research area, are a valuable complement to methods previously proposed for forecasting drought. Results obtained so far for predicting meteorological indices are very satisfactory, especially with hybrid models such as WT-ANN or WT-SVR.

Most of the work that we reported here is based on the standardized precipitation index or SPI, which is a reliable measure of drought used in more than 60 countries. The leading month or the number of months over which SPI is calculated

Unfortunately, many of the examples were based on ground gauge data. The brevity (and noise) of the records obstructs the use of many satellite products. However, as time progresses and data retrieval improves, satellite products will be

The exponential growth of public and free satellite imagery sources and of opensource software, as well as cheaper access to cloud-based technology, will provide powerful forecasting tools to a greater number of researchers, allowing them to

The authors declare that there is no conflict of interest regarding the publication

processing operations [105].

7. Conclusion

who do have not the necessary skills.

DOI: http://dx.doi.org/10.5772/intechopen.85471

packages used to generate, for example, the SPI index.

helpful for visualizing and interpreting the results [107, 108].

project.org/web/views/MachineLearning.html.

significantly influences the prediction values.

forecast drought before it occurs.

Conflict of interest

of this article.

19

long and accurate enough to generate reliable results.

#### Table 2.

Representative satellite rainfall products.

System, (MHS) Microwave Humidity Sounder, (GPCP) Global Precipitation Climatology Centre, (GOES) Geostationary Operational Environmental Satellite, (Metsat) meteorological satellite, (NOAA) NASA-provided TIROS series of weather forecasting satellite run by the National Oceanic and Atmospheric Administration, (DMSP) Defense Meteorological Satellite Program, (GRIDSAT-B1) geostationary IR channel brightness temperature.
