با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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Multiple AI model integration strategy : Application to saturated hydraulic conductivity prediction from easily available soil properties
استراتژی یکپارچه سازی مدل هوش مصنوعی چندگانه: کاربرد در پیش بینی هدایت هیدرولیکی اشباع شده از خصوصیات خاک که به راحتی در دسترس است-2020 A multiple model integration scheme driven by artificial neural network (ANN) (MM-ANN) was developed and
tested to improve the prediction accuracy of soil hydraulic conductivity (Ks) in Tabriz plain, an arid region of
Iran. The soil parameters such as silt, clay, organic matter (OM), bulk density (BD), pH and electrical conductivity
(EC) were used as model inputs to predict soil Ks. Standalone models including multivariate adaptive
regression splines (MARS), M5 model tree (M5Tree), support vector machine (SVM) and extreme learning
machine (ELM) were also implemented for comparative evaluation with MM-ANN model predictions. Based on
several performance indicators such as Nash Sutcliffe Efficiency (NSE), results showed that the calibrated MMANN
model involving the predictions of MARS, M5Tree, SVM and ELM models by considering all the soil
parameters used in this study as inputs provided superior soil Ks estimates. The proposed hybrid model (MMANN)
emerged as a reliable intelligence model for the assessment of soil hydraulic conductivity with an
NSE=0.939 & 0.917 during training and testing, respectively. Accurate prediction of field-scale soil hydraulic
conductivity is crucial from the view point of agricultural sustainability and management prospects. Keywords: Saturated hydraulic conductivity | Extreme learning machine | Multiple model strategy | Multivariate adaptive regression splines | M5Tree | Support | vector machine | Prediction |
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