Prediction of environmental indicators in land levelling using artificial intelligence techniques

Isham Alzoubi

School of Surveying Geospatial Engineering, Syria

Published Date: 2022-06-18

Isham Alzoubi
School of Surveying Geospatial Engineering, Syria

Received: June 10, 2022; Accepted: June 14, 2022; Published: June 18, 2022

Visit for more related articles at Journal of Reproductive Health and Contraception

Abstract

The aim of this work was to determine best linear model Adaptive Neuro-Fuzzy Inference System (ANFIS) and Sensitivity
Analysis in order to predict the energy consumption for land leveling. In this research effects of various soil properties such as
Embankment Volume, Soil Compressibility Factor, Specific Gravity, Moisture Content, Slope, Sand Percent, and Soil Swelling
Index in energy consumption were investigated. The study was consisted of 90 samples were collected from 3 different regions.
The grid size was set 20 m in 20 m (20*20) from a farmland in Karaj province of Iran. The values of RMSE and R2 derived by ICAANN
model were, to Labor Energy (0.0146 and 0.9987), Fuel energy (0.0322 and 0.9975), Total Machinery Cost (0.0248 and
0.9963), Total Machinery Energy (0.0161 and 0.9987) respectively, while these parameters for multivariate regression model
were, to Labor Energy (0.1394 and 0.9008), Fuel energy (0.1514 and 0.8913), Total Machinery Cost (TMC) (0.1492 and 0.9128),
Total Machinery Energy (0.1378 and 0.9103).Respectively, while these parameters for ANN model were, to Labor Energy
(0.0159 and 0.9990), Fuel energy (0.0206 and 0.9983), Total Machinery Cost (0.0287 and 0.9966), Total Machinery Energy
(0.0157 and 0.9990) respectively, while these parameters for Sensitivity analysis model were, to Labor Energy (0.1899 and
0.8631), Fuel energy (0.8562 and 0.0206), Total Machinery Cost (0.1946 and 0.8581), Total Machinery Energy (0.1892 and
0.8437) respectively, respectively, while these parameters for ANFIS model were, to Labor Energy (0.0159 and 0.9990), Fuel
energy (0.0206 and 0.9983), Total Machinery Cost (0.0287 and 0.9966), Total Machinery Energy (0.0157 and 0.9990)
respectively, Results showed that ICA_ANN with seven neurons in hidden layer had better. According to the results of
Sensitivity Analysis, only three parameters; Density, Soil Compressibility Factor and, Embankment Volume Index had significant
effect on fuel consumption. According to the results of regression, only three parameters; Slope, Cut-Fill Volume (V) and, Soil
Swelling Index (SSI) had significant effect on energy consumption. Using adaptive neuro-fuzzy inference system for

Select your language of interest to view the total content in your interested language

Viewing options

Flyer image

Share This Article

paper.io

agar io

wowcappadocia.com
cappadocia-hotels.com
caruscappadocia.com
brothersballoon.com
balloon-rides.net

wormax io