Identifying the Features Affecting the Cost of Drip Irrigation Systems Using Feature Selection Methods

This research aimed to select essential features for modeling the cost of pressurized irrigation systems using the data of 515 drip irrigation projects in four parts, including the cost of pumping station and central control system (TCP), cost of on-farm equipment (TCF), cost of installation and operation on-farm and pumping station (TCI), and total cost (TCT). In the first stage, a database including 39 features influencing the cost of the mentioned sectors was prepared and the price of all projects (2006 to 2019) was updated for the base year of 2021. Then, feature selection was done with different algorithms in MATLAB environment and in two parts including (1) all features (39 features before and after the design stage) and (2) 18 features before the design phase (BD). The results showed that the amounts of RMSE and R2 for all the features were equal to 0.007 and 0.92, respectively, and for the BD section, they were equal to 0.003 and 0.89, respectively. Among the different algorithms for feature selection, support vector machine (SVM) and optimization algorithms (Wrapper) were identified as the best learner and feature selection method, respectively. The results of the evaluation criteria showed that the two LCA and FOA algorithms achieved the best estimation, and their error criterion in all the features were 0.0020 and 0.0018, respectively, while their correlations were 0.94 and 0.94. In the BD features, these criteria were 0.0006 and 0.95 for both algorithms, respectively. Finally, in the all features section, 10 out of 39 features and for BD section, 8 out of 18 were selected as the most effective features. The results of choosing the most effective features that affect the cost of different parts of the drip irrigation system can make the cost modeling of the systems simpler and faster and, while being useful for research works, it facilitates estimation and management of costs before implementation of each project.