## 2 Review of Literature
Other researchers showed that, according to the intrinsic characteristics of crops and plant growth conditions, this method is suitable for forecasting [13; 14; 20]. Studies that used RF method, showed that this method has an acceptable performance for predicting the yield of agricultural products in different conditions with different indicator [10; 16; 17; 22]. [PERSON] and [PERSON] [17] specifically demonstrated the capability of this method in using remote sensing indicators. Due to the unstable nature of climate phenomena and factors affecting plant growth, using the KNN method along with other methods gives more reliable performance to the results [22]. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Row**} & \multirow{2}{*}{**Authors**} & \multirow{2}{*}{**Data of**} & \multirow{2}{*}{**Location of Study**} & \multirow{2}{*}{**Copy**} & \multirow{2}{*}{**Tools**} & \multirow{2}{*}{**RF**} & \multirow{2}{*}{**MRR**} & \multicolumn{2}{c}{**E-RFNN**} & \multicolumn{2}{c}{**DL**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**LET**} & \multicolumn{2}{c}{**DT**} & \multicolumn{2}{c}{**NN**} & \multicolumn{2}{c}{**NNN**} & \multicolumn{2}{c}{**CPR**} & \multicolumn{2}{c}{**BST**} & \multicolumn{2}{c}{**BCT**} & \multicolumn{2}{c}{**LR**} \\ \cline{1-1} \cline{9-15}  & & & & & & & & & & & & & & & & & & & & & \\ \hline
1 & [PERSON] et al. [10] & 2016 & India & Rico & WEEK-A/CEB & & & & & & & & & & & & & & & & & \\
2 & Eigm and Abbasi [1] & 2018 & Pakistan & Wheat & Phone-Waka & & & & & & & & & & & & & & & & & \\
3 & [PERSON] et al. [14] & 2019 & India & September & SGR-Python & & & & & & & & & & & & & & & & \\
4 & [PERSON] et al. [5] & 2015 & China & Italy & Mr & MATLAB & & & & & & & & & & & & & & & & \\
5 & [PERSON] et al. [20] & 2018 & Vietnam & Golden & MATLAB & & & & & & & & & & & & & & & & \\
6 & [PERSON] and [PERSON] [17] & 2016 & United States & Com & R software & & & & & & & & & & & & & & & \\
7 & Naumannburg and & & & & & & & & & & & & & & & & & & & & \\
7 & [PERSON] [19] & 2017 & India & Rico & R software & & & & & & & & & & & & & & & & \\
8 & [PERSON] [9] & 2020 & Iran & India & & R software & & & & & & & & & & & & & & \\
9 & [PERSON] [3] & 2015 & United States & Com & R software & & & & & & & & & & & & & & & \\
10 & [PERSON] [20] & 2010 & India & & CAITA & & & & & & & & & & & & & & & \\
11 & [PERSON] et al. [21] & 2020 & China & Wheat & MATLAB-HPCA- & & & & & & & & & & & & & & & & \\
12 & [PERSON] [21] & 2020 & Iran & Italy & GEE & & & & & & & & & & & & & & & \\
13 & Peperant research & 2020 & Iran & Chicago & SGR-Python & & & & & & & & & & & & & & \\ \hline \multicolumn{11}{l}{The * symbol in each new indicates which methods (columns) in each research has been used and names of method ab-breviations explained as follow: BFNN: Back-Prepropagation Neural Networks, RF: Random Forest, ML: Multiple Lunar Regression, Deep learning, 37 th-Support Vector Machine Machine, EIT: Extremely Randomized Trees, DT: Decision tree, NN: neural network, KNN: K-nearest neighbor, GPR: Gaussian Process Regression, RF: Boost Trees, RF: Digging Tree, LG: Logistic Regression,  
In this study, for the first time the prediction of rainfed chickpea farming for 11 counties with the highest chickpea production in Kermanshah Province of Iran was performed by the use of SVM, RF, and KNN machine learning methods, and a combination of remote sensing and local synoptic station data. The above comparison shows that most of the past studies have been focused on crops that are either irrigated or cultivated in rainy areas. In Iran, however, rainfed agriculture in areas with limited access to water accounts for a fairly large portion of the country's total agricultural output and therefore plays a critical role in the food security of this country [3; 6]. Considering the climate of Kermanshah and its evolving water scarcity issues, there seems to be some room for the expansion of rainfed farming in this province.