首页|Developing hybrid data-intelligent method using Boruta-random forest optimizer for simulation of nitrate distribution pattern
Developing hybrid data-intelligent method using Boruta-random forest optimizer for simulation of nitrate distribution pattern
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NSTL
Elsevier
? 2022 Elsevier B.V.One of the critical factors in the optimal design of drip-fertigation systems is determining the distribution of nitrate in the soil. Handling such a complex non-linear process is challenging. The main goal of this study is to develop an accurate hybrid Boruta Random Forest (BRF)-Whale Optimization Algorithm (WOA) integrated with an Artificial Neural Network (ANN) to estimate the nitrate concentration (NO3?) in the distribution system. In addition to applying ANN and support vector regression (SVR) methods, various training algorithms and kernel functions are used as standalone validation models to evaluate the robustness of the WOA-ANN model for nitrate pattern estimation. The algorithm uses 11 variables extracted from the experimental study, which are optimally arranged in five input combinations employing the BRF Feature Selection (FS) and regression analyses. The statistical and diagnostic analyses showed that the BRF-FS is the best approach to optimize the WOA-ANN model. The proposed approach provided the best metrics (i.e. R=0.962, RMSE=0.029 mg/L, MAE=0.024, and U95%=0.056) and improved the ANN's accuracy by 30%. It also outperformed the ANN (R=0.913 and RMSE=0.042 mg/L) and SVR (R=0.901 and RMSE=0.045 mg/L) when applied to estimate the NO3? values. An external validation analysis showed the robustness of all applied machine learning models. Moreover, the significant scoring assessment also showed that when using the BRF-FS approach, the initial nitrate concentration in soil (N0) and nitrate concentration in irrigation water (F?NO3?) had the most influence on the estimation of nitrate pattern, respectively.
Artificial neural networkBoruta Random forestDrip irrigationExternal validationNitrate patternWhale optimization algorithm
Jamei M.、Maroufpoor S.、Aminpour Y.、Karbasi M.、Malik A.、Karimi B.
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Faculty of Engineering Shohadaye Hoveizeh Campus of Technology Shahid Chamran University of Ahvaz
Department of Irrigation and Reclamation Engineering University of Tehran
Department of Hydraulic Hydro-Environmental Engineering Water Research Institute Ministry of Energy
Water Engineering Department Faculty of Agriculture University of Zanjan
Punjab Agricultural University Regional Research Station
Department of Water Science and Engineering Faculty of Agriculture University of Kurdistan
NSTL
Elsevier
