In this paper, two artificial intelligence (AI) models (i.e., neural networks (ANN) and multivariate adaptive regression splines (MARS)) were developed using cumulative percentiles from the grain-size distribution (GSD) curve as input information to predict soil-water characteristic (SWCC). The importance of each variable was testified different sensitivity analyses. results show a strong corre...

Subclinical mastitis is one of the most significant diseases that cause economic losses in dairy cattle farming. This investigation was conducted on 112 head Holstein Friesian cows order to reveal relationship between subclinical and electrical conductivity milk composition quality. In study, CMT (California Mastitis Test) CSCC (Classified Somatic Cell Count) used diagnosis were as a binary res...

The accurate estimation of dew point temperature ( T ) is important in climatological, agricultural, and agronomical studies. In this study, the feasibility two soft computing methods, random forest (RF) multivariate adaptive regression splines (MARS), evaluated for predicting long-term mean monthly . Various weather variables including air temperature, sunshine duration, relative humidity, inc...

Ground vibration induced by rock blasting is an unavoidable effect that may generate severe damages to structures and living communities. Peak particle velocity (PPV) the key predictor for ground vibration. This study aims develop a model predict PPV in opencast mines. Two machine-learning techniques, including multivariate adaptive regression splines (MARS) classification tree (CART), which ar...

Missing data with gaps is always an obstacle to effective planning and management of water resources. Complete reliable hydrological time series are necessary for the optimal design A study was conducted fill in missing streamflow 54 observation stations across Turkey. This process done aid various statistical estimation methods. Estimations were performed by using Linear regression (LR), Artif...