Calibration of Soil Water Content Data from EnviroSCAN System Using Artificial Neural Network

2016 ◽  
Vol 12 (5) ◽  
pp. 1-11
Author(s):  
Hussein Al-Ghobari ◽  
Mohamed Marazky ◽  
Abdulwahed Aboukarima ◽  
Mamdouh Minyawi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Artificial Neural

scholarly journals Comparison of Pattern Recognition, Artificial Neural Network and Pedotransfer Functions for Estimation of Soil Water Parameters

2010 ◽  
Vol 2 (3) ◽  
pp. 114-120 ◽  
Author(s):  
Amir LAKZIAN ◽  
Mohammad BANNAYAN AVAL ◽  
Nasrin GORBANZADEH
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Water Content ◽  
Soil Water ◽  
Solid Phase ◽  
Soil Water Potential ◽  
Pedotransfer Functions ◽  
Mean Square ◽  
Artificial Neural

This paper presents the comparison of three different approaches to estimate soil water content at defined values of soil water potential based on selected parameters of soil solid phase. Forty different sampling locations in northeast of Iran were selected and undisturbed samples were taken to measure the water content at field capacity (FC), -33 kPa, and permanent wilting point (PWP), -1500 kPa. At each location solid particle of each sample including the percentage of sand, silt and clay were measured. Organic carbon percentage and soil texture were also determined for each soil sample at each location. Three different techniques including pattern recognition approach (k nearest neighbour, k-NN), Artificial Neural Network (ANN) and pedotransfer functions (PTF) were used to predict the soil water at each sampling location. Mean square deviation (MSD) and its components, index of agreement (d), root mean square difference (RMSD) and normalized RMSD (RMSDr) were used to evaluate the performance of all the three approaches. Our results showed that k-NN and PTF performed better than ANN in prediction of water content at both FC and PWP matric potential. Various statistics criteria for simulation performance also indicated that between kNN and PTF, the former, predicted water content at PWP more accurate than PTF, however both approach showed a similar accuracy to predict water content at FC.

scholarly journals Prediksi Pengoperasian Traktor di Lahan Kering Menggunakan Artificial Neural Network

2021 ◽  
Vol 12 (3) ◽  
pp. 127-133
Author(s):  
Taufik Nugraha Agassi ◽  
Yose Sebastian ◽  
Zainal Arifin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Content ◽  
Supervised Learning ◽  
Soil Water ◽  
Soil Water Content ◽  
Back Propagation ◽  
Learning Method ◽  
Ann Model ◽  
Dry Land

Soil water content is an important parameter in making a decision to use a tractor or not. The process of measuring soil water content and levels of field capacity in conventional which takes a long time and cannot be used in real-time to measure it is a major problem in the field. Determinants of soil water content such as ambient temperature, humidity, and rainfall can be obtained easily and quickly either by using a tool or retrieving data from the nearest BMKG station. The objective of this research is to obtain the most optimal prediction model in making decisions about tractor operation in dry land. This research uses an Artificial Neural Network (ANN) in modeling predictions of tractor operation. Prediction of tractor operation is a prediction of tractor use on a certain day using input data obtained before the day of tractor use. ANN modeling uses the back-propagation supervised learning method. The best ANN model used four hidden neurons with a learning coefficient of 0.2, a momentum of 0.8 and 20,000 iterations. This model has been able to provide optimal predictions with an accuracy value of 77%. The ANN model has been successful in predicting tractor operation on dry land using the back-propagation supervised learning method.

scholarly journals Prediction of size distribution of iron ore granules and permeability of its bed

2011 ◽  
Vol 47 (2) ◽  
pp. 113-123 ◽  
Author(s):  
X. Lv ◽  
C. Bai ◽  
X. Huang ◽  
G. Qiu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Content ◽  
Size Distribution ◽  
Mass Fraction ◽  
Raw Materials ◽  
Good Prediction ◽  
Moisture Capacity ◽  
Granulation Process ◽  
Artificial Neural

The granulation process, which is determined by many factors like properties of the mixture and the operating parameters, is of very importance for getting a good permeability of the burden in the sintering strand. The prediction of the size distribution of the granules and the permeability of its bed by the artificial neural network was studied in this paper. It was found by the experiments that the order of significance in the granulation process is water content added into the mixture, the mass fraction of the particles of 0.7-3 mm, and the moisture capacity. The water content added in the mixture and the mass fractions of the particles of 0.7-3 mm have the positive relation to the permeability of granulation, While, the moisture capacity has the negative relation to the permeability of granulation. Both the moisture capacity and the water content added were used as the inputs in the model of artificial neural network, which can give a good prediction on the permeability and mass fraction of the granules of 3-8 mm, as well as the tendency of the samples under instable raw materials conditions. These two models can be used for optimization the granulation.

scholarly journals Prediction of soil water characteristic curve using artificial neural network: a new approach

2018 ◽  
Vol 162 ◽  
pp. 01014
Author(s):  
Abdul-Kareem Esmat Zainal ◽  
Shaimaa Hasan Fadhil
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Properties ◽  
Soil Water ◽  
Unsaturated Soil ◽  
Characteristic Curve ◽  
New Approach ◽  
Soil Water Characteristic Curve ◽  
Important Relationship ◽  
Artificial Neural

Soil-Water Characteristic Curve (SWCC) is an important relationship between matric suction and volumetric water content of soils especially when dealing with unsaturated soil problems, these problems may include seepage, bearing capacity, volume change, etc. where the matric or total suction may have a considerable effect on unsaturated soil properties. Obtaining an accurate SWCC for a soil could be cumbersome and sometimes it is time consuming and needs effort for some soils, either through laboratory tests or through field tests. Accurate prediction of this curve can give more precise expectations in design or analysis that include some unsaturated soil properties, which can save more effort and time. This work will concentrate on proposing a new approach for determining the SWCC using Artificial Neural Network (ANN) depending on some soil properties (air-entry point and residual degree of saturation) through computer software MatLab as a tool for ANN. The new approach is to plot the SWCC curve points instead of obtaining the parameters used in Brooks and Corey (BC) Model (1964), van Genuchten (VG) Model (1980), or Fredlund and Xing (FX) Model (1994). Results showed close agreement in determination of the SWCC by verification of the ANN results with an additional curve sample.

Predicting the effective thermal conductivity of unfrozen soils with various water contents based on artificial neural network

2021 ◽  
Author(s):  
Chuan-Yong Zhu ◽  
Zhi-Yang He ◽  
Mu Du ◽  
Liang Gong ◽  
Xinyu Wang
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Water Content ◽  
Effective Thermal Conductivity ◽  
Pearson Correlation ◽  
Complex Structure ◽  
Ann Model ◽  
Input Parameters ◽  
Artificial Neural

Abstract The effective thermal conductivity of soils is a crucial parameter for many applications such as geothermal engineering, environmental science, and agriculture and engineering. However, it is pretty challenging to accurately determine it due to soils’ complex structure and components. In the present study, the influences of different parameters, including silt content (m si), sand content (m sa), clay content (m cl), quartz content (m qu), porosity, and water content on the effective thermal conductivity of soils, were firstly analyzed by the Pearson correlation coefficient. Then different artificial neural network (ANN) models were developed based on the 465 groups of thermal conductivity of unfrozen soils collected from the literature to predict the effective thermal conductivity of soils. Results reveal that the parameters of m si, m sa, m cl, and m qu have a relatively slight influence on the effective thermal conductivity of soils compared to the water content and porosity. Although the ANN model with six parameters has the highest accuracy, the ANN model with two input parameters (porosity and water content) could predict the effective thermal conductivity well with acceptable accuracy and R 2=0.940. Finally, a correlation of the effective thermal conductivity for different soils was proposed based on the large number of results predicted by the two input parameters ANN-based model. This correlation has proved to have a higher accuracy without assumptions and uncertain parameters when compared to several commonly used existing models.

STREAMFLOW SIMULATION: COMPARISON BETWEEN SOIL WATER ASSESSMENT TOOL AND ARTIFICIAL NEURAL NETWORK MODELS

2021 ◽  
Vol 5 (2) ◽  
pp. 173-182
Author(s):  
Shehu Usman Haruna ◽  
Aliyu Kasim Abba ◽  
Rabi'u Aminu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Water ◽  
Assessment Tool ◽  
Swat Model ◽  
Coefficient Of Determination ◽  
Artificial Neural ◽  
Soil Water Assessment Tool ◽  
Water Assessment ◽  
Streamflow Simulation

The present study compared the performance of two different models for streamflow simulation namely: Soil Water Assessment Tool (SWAT) and the Artificial Neural Network (ANN). During the calibration periods, the Nash-Sutcliff (NS) and Coefficient of Determination (R2) for SWAT was 0.74 and 0.81 respectively, whereas for ANN, it was 0.99 and 0.85 respectively. The ANN performs better during the validation period as the result revealed with NS and R2 having 0.98 and 0.89 respectively, while for the SWAT model it was 0.71 and 0.74 respectively. Based on the recommended comparison of graphical and statistical evaluation performances of both models, the ANN model performed better in estimating peak flow events than the SWAT model in the Upper Betwa Basin. Furthermore, the rigorous time required and expertise for calibration of the SWAT is much less as compared with the ANN. Moreover, the results obtained from both models demonstrate the performances of the

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