Modelling the influence of crop density and weather conditions on field drying characteristics of switchgrass and maize stover using random forest

Khanchi, Amit; Birrell, Stuart J.; Mitchell, Robert B.

doi:10.1016/j.biosystemseng.2018.02.002

Cited by 11 publications

(2 citation statements)

References 28 publications

(42 reference statements)

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“…According to the obtained results, the MMANN and MGGP outperformed the other 2018). The RF model is similar to the classification and regression tree (CART) but differs because it contains many trees (Khanchi et al 2018), and the model prediction accuracy was not governed by a sole statistical index (Meng et al 2019). For the Iranian region of Golestan, Shabani et al (2020) examined the Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), RF, and the SVM.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of CatBoost Method for Predicting Weekly Pan Evaporation in Subtropical and Sub-Humid Regions

Vishwakarma,

Kumar,

Yadav

et al. 2024

Pure Appl. Geophys.

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Pan evaporation modelling and forecasting is needed to provide timely, continuously, and valuable water information to support water management. In this study we predict weekly pan evaporation using six machine learning approaches.The proposed models were:the multiple linear regression (MLR), the multiple nonlinear least-squares regression (MNLSR), the multivariate adaptive regression splines (MARS), the random forest regression (RF), the CatBoostand the M5 Model Tree (M5Tree).The algorithms were developed using data collected at Crop Research Center (CRC), Pantnagar-Uttarakhand, India. Stepwise regression was used as input variable selection algorithm for selecting the best relevant input from several meteorological variables. For models development we split the data into two subsets: the first three years reserved for model calibration i.e., validation, and the fourth-year data was used validation purpose.The rootmean square error (RMSE), Nash-Sutcliffe efficiency (NSE), Willmott index of agreement (WI), Pearson correlation coefficient (PCC), coefficient of determination (R 2 ), and mean absolute error (MAE) were used to assess the effectiveness of weekly pan evaporation estimation models. CatBoost model based on four predictors was the most accurate at Pantnagar station, with NSE, WI, MAE, RMSE, PCC and R 2 ranging from 0.9838 to 0.9877, 0.9960 to 0.9968, 0.1991 to 0.2078, 0.3643 to 0.3650 to 0.9955, 0.9925 and 0.9851 to 0.9911, respectively. Random Forest model has scored the second rank in terms of accuracy and effectiveness for mapping weekly pan evaporation followed by MARS, MNLSR, MLR, and M5Tree.

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Section: Introductionmentioning

confidence: 99%

Evaluation of CatBoost Method for Predicting Weekly Pan Evaporation in Subtropical and Sub-Humid Regions

Vishwakarma,

Kumar,

Yadav

et al. 2024

Pure Appl. Geophys.

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“…Recent developments in the branch of food drying involve advancements in the development of mathematical models [1,2], spanning empirical, semi-empirical, and theoretical approaches [3,4]. Researchers have increasingly employed computational methods, including artificial neural networks [5,6], convolutional networks [7][8][9], random forests [10,11], support vector machines [12,13], and more, to analyze the impacts of diverse drying conditions and methods on food quality and safety. The integration of these computational techniques provides a sophisticated understanding of how different variables influence the drying process.…”

Section: Introductionmentioning

confidence: 99%