Land Evaluation: A General Perspective

Karthikeyan, K.; Singh, Sudhir Kumar; Yousuf, Abrar; Bhople, B. S.; Pushpanjali, .; Naitam, RK

doi:10.1201/9780429430633-11

Cited by 2 publications

(2 citation statements)

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“…where y t represents the t th observation (t ∈ [[1, n]], n ∈ N * ); w i is the weight vector associated with the i th neuron in the hidden layer (i ∈ [ [1,11]]); b i and b are respectively the bias of the i th neuron in the hidden layer and the bias applied to output neurone of 3-MLP model. The optimal parameters are θ = (w (1) , b (1) , w (2) , b (2) ) with The total number of parameters, n θ = 67.…”

Section: Specification Of Model and Generation Of A Data Populationmentioning

confidence: 99%

“…One of the nonlinear models that has received great attention last few years is the model based on artificial neural networks (ANNs). They are used in the fields of prediction and classification, fields in which regression models and other related statistical techniques have traditionally been used [1][2][3][4]. Multilayer perceptron neural networks (MLPs) are one of the architectures of ANNs acting as a type of regression model, not necessarily parametric, which enables complex functional forms to be modeled [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Robustness of Imputation Methods with Backpropagation Algorithm in Nonlinear Multiple Regression

Hounmenou¹,

Behingan²,

Chrysostome³

et al. 2021

Preprint

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Missing observations constitute one of the most important issues in data analysis in applied research studies. The magnitude and their structure impact parameters estimation in the modeling with important consequences for decision-making. This study aims to evaluate the efficiency of imputation methods combined with the backpropagation algorithm in a nonlinear regression context. The evaluation is conducted through a simulation study including sample sizes (50, 100, 200, 300 and 400) with different missing data rates (10, 20, 30 40 and 50%) and three missingness mechanisms (MCAR, MAR and MNAR). Four imputation methods (Last Observation Carried Forward, Random Forest, Amelia and MICE) were used to impute datasets before making prediction with backpropagation. 3-MLP model was used by varying the activation functions (Logistic-Linear, Logistic-Exponential, TanH-Linear and TanH-Exponentiel), the number of nodes in the hidden layer (3 - 15) and the learning rate (20 - 70%). Analysis of the performance criteria (R2, r and RMSE) of the network revealed good performances when it is trained with TanH-Linear functions, 11 nodes in the hidden layer and a learning rate of 50%. MICE and Random Forest were the most appropriate for data imputation. These methods can support up to 50% of missing rate with an optimal sample size of 200.

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