Artificial Neural Networks for Predicting the Water Retention Curve of Sicilian Agricultural Soils

Modeling soil-water regime and solute transport in the vadose zone is strategic for estimating agricultural productivity and optimizing irrigation water management. Direct measurements of soil hydraulic properties, i.e., the water retention curve and the hydraulic conductivity function, are often expensive and time-consuming, and represent a major obstacle to the application of simulation models. As a result, there is a great interest in developing pedotransfer functions (PTFs) that predict the soil hydraulic properties from more easily measured and/or routinely surveyed soil data, such as particle size distribution, bulk density (ρb), and soil organic carbon content (OC). In this study, application of PTFs was carried out for 359 Sicilian soils by implementing five different artificial neural networks (ANNs) to estimate the parameter of the van Genuchten (vG) model for water retention curves. The raw data used to train the ANNs were soil texture, ρb, OC, and porosity. The ANNs were evaluated in their ability to predict both the vG parameters, on the basis of the normalized root-mean-square errors (NRMSE) and normalized mean absolute errors (NMAE), and the water retention data. The Akaike's information criterion (AIC) test was also used to assess the most efficient network. Results confirmed the high predictive performance of ANNs with four input parameters (clay, sand, and silt fractions, and OC) in simulating soil water retention data, with a prediction accuracy characterized by MAE = 0.026 and RMSE = 0.069. The AIC efficiency criterion indicated that the most efficient ANN model was trained with a relatively low number of input nodes

Calibration transfer in near infrared spectroscopic analysis using adaptive artificial neural network

Near Infrared Spectroscopy (NIRS) has been implemented in various areas due to its non-invasive and rapid measurement features. A NIRS calibration model can be transferred among different instruments using calibration transfer methods. According to review paper reported by J. Worksman, the most popular calibration transfer methods of spectra standardization methods require primary and secondary instruments to acquire transfer samples at the same samples. However, if the primary instrument is broken, then the existing model cannot be transferred using these methods. Artificial Neural Network (ANN) that has the capability of adapting new environmental conditions. Thus, this study aims to investigate the feasibility of an adaptive ANN (AANN) as an alternative in transferring models from primary to secondary instruments with transfer samples collected on secondary instruments only. First, ANN was developed and optimized using primary instrument’s spectrum. Then, the optimized ANN was adapted to secondary instruments using transfer samples collected on secondary instruments, in which the weights and biases of the ANN were updated. Finding show that the excellent results were obtained using proposed AANN and 20 transfer samples, with the best averaged root mean squared error of prediction (RMSEP) of 0.1017% and the best averaged correlation coefficient of 0.7898, followed by Direct Standardization – Artificial Neural Network (DS-ANN) and Direct Standardization – Adaptive Artificial Neural Network (DS-AANN) in corn oils prediction applications. The proposed AANN outperformed previous works Piecewise Direct Standardization – Partial Least Squared (PDS-PLS) with RMSEP of 0.1321% and 0.1150%, and correlation coefficient of 0.7780 and 0.7785, for m5/mp5 and m5/mp6 respectively. Hence, proposed AANN has the capability to transfer the existing calibration model to secondary instruments without the involvement of primary instrument

Guidance Index for Shallow Landslide Hazard Analysis

Rainfall-induced landslides are one of the most frequent hazards on slanted terrains. They lead to considerable economic losses and fatalities worldwide. Intense storms with high-intensity and long-duration rainfall have high potential to trigger rapidly moving soil masses due to changes in pore water pressure and seepage forces. Nevertheless, regardless of the intensity-duration of the rainfall, shallow landslides are influenced by antecedent soil moisture conditions. To the present day, no system exists that dynamically interrelates these two factors. This work establishes a relationship between antecedent soil moisture and rainfall expressed in the form of a Shallow Landslide Index (SLI) at 1km2 resolution for the United States. The proposed mathematical model is based on a logistic regression-learning algorithm that systematically adapts from previous landslide events listed in a comprehensive landslide inventory. Because landslides are considered to be the product of the interaction of static and dynamic factors, static factors are examined first. Also, because significant uncertainties are found when mapping factors in large spatial scales, buffer and threshold techniques are used to downscale areas and minimize uncertainties. Static parameters for 230 shallow rainfall-induced landslides in the continental United States are examined. ASTER GDEM is used as the basis for topographical characterization of slope and buffer analysis. Slope angle threshold assessment at the 50, 75, 95, 98, and 99 percentiles is tested locally. Further analysis of each threshold in relation to other parameters is investigated in a logistic regression model for the continental U.S. It is determined that lower than 95-percentile thresholds under-estimate slope angles and best regression fit can be achieved when utilizing the 99-threshold slope angle. This model predicts the highest number of cases correctly at 87.0% accuracy. A one-unit rise in the 99-threshold range increases landslide likelihood by 11.8%. The logistic regression model is carried over to ArcGIS where all static variables are processed based on their corresponding coefficients. A regional slope susceptibility map for the continental United States is developed and analyzed against the available landslide records and their spatial distributions. Consequently, a mathematical algorithm is proposed to determine landslide probability as a function of static and dynamic factors employing accumulated water volume. As rainfall thresholds alone do not provide information about the soil wetness profile with depth, the Shallow Landslide Index (SLI) is intended to be an indicator of antecedent root soil moisture and rainfall accumulation over a 1km2 pixel area. Experimentally, root-soil moisture retrieved from AMSR-E and rainfall retrieved from TRMM are used as proxies to develop such index. Static and dynamic conditions leading to each landslide event are examined over 60-days, 30-days, 10-days and 7-days. The input dataset is randomly divided into training and verification sets where validation results indicate that the best-fit model predicts the highest number of cases correctly at 93.2% accuracy. The resulting equation is then incorporated in a python subroutine that calculates the SLI for each of the 900,000-pixel points. For each pixel, the algorithm incrementally tries values from 0 to the value that makes the event probability equal to 1. Since AMSR-E and TRMM stopped working in October 2011 and April 2015 respectively, a solution that works for the future is presented. Root-soil moisture retrieved from SMAP and rainfall retrieved from GPM are used to develop models that calculate the SLI for the continental United States for 10-days, 7-days, and 3-days. The resulting models indicated a strong relationship (93.4%, 93.8%, and 93.7% respectively) between the predictors and the prediction value. Nevertheless, as of the writing of this work, the SMAP root soil moisture product has a mean latency of 7-days hence the SLI is functional for 10 or 7 days. It is expected that as SMAP’s latency is reduced, the SLI functionally can also be brought to a shorter period. The resulting SLI map can potentially be used as an indicator of the total amount of rainfall needed for a given duration of time to trigger a shallow landslide in a susceptible area

Testing of Materials and Elements in Civil Engineering

This book was proposed and organized as a means to present recent developments in the field of testing of materials and elements in civil engineering. For this reason, the articles highlighted in this editorial relate to different aspects of testing of different materials and elements in civil engineering, from building materials to building structures. The current trend in the development of testing of materials and elements in civil engineering is mainly concerned with the detection of flaws and defects in concrete elements and structures, and acoustic methods predominate in this field. As in medicine, the trend is towards designing test equipment that allows one to obtain a picture of the inside of the tested element and materials. Interesting results with significance for building practices were obtained

Corrosion and Degradation of Materials

Studies on the corrosion and degradation of materials play a decisive role in the novel design and development of corrosion-resistant materials, the selection of materials used in harsh environments in designed lifespans, the invention of corrosion control methods and procedures (e.g., coatings, inhibitors), and the safety assessment and prediction of materials (i.e., modelling). These studies cover a wide range of research fields, including the calculation of thermodynamics, the characterization of microstructures, the investigation of mechanical and corrosion properties, the creation of corrosion coatings or inhibitors, and the establishment of corrosion modelling. This Special Issue is devoted to these types of studies, which facilitate the understanding of the corrosion fundamentals of materials in service, the development of corrosion coatings or methods, improving their durability, and eventually decreasing corrosion loss

A Review of Resonant Converter Control Techniques and The Performances

paper first discusses each control technique and then gives experimental results and/or performance to highlights their merits. The resonant converter used as a case study is not specified to just single topology instead it used few topologies such as series-parallel resonant converter (SPRC), LCC resonant converter and parallel resonant converter (PRC). On the other hand, the control techniques presented in this paper are self-sustained phase shift modulation (SSPSM) control, self-oscillating power factor control, magnetic control and the H-∞ robust control technique

OBSERVER-BASED-CONTROLLER FOR INVERTED PENDULUM MODEL

This paper presents a state space control technique for inverted pendulum system. The system is a common classical control problem that has been widely used to test multiple control algorithms because of its nonlinear and unstable behavior. Full state feedback based on pole placement and optimal control is applied to the inverted pendulum system to achieve desired design specification which are 4 seconds settling time and 5% overshoot. The simulation and optimization of the full state feedback controller based on pole placement and optimal control techniques as well as the performance comparison between these techniques is described comprehensively. The comparison is made to choose the most suitable technique for the system that have the best trade-off between settling time and overshoot. Besides that, the observer design is analyzed to see the effect of pole location and noise present in the system

A Review of Resonant Converter Control Techniques and The Performances

paper first discusses each control technique and then gives experimental results and/or performance to highlights their merits. The resonant converter used as a case study is not specified to just single topology instead it used few topologies such as series-parallel resonant converter (SPRC), LCC resonant converter and parallel resonant converter (PRC). On the other hand, the control techniques presented in this paper are self-sustained phase shift modulation (SSPSM) control, self-oscillating power factor control, magnetic control and the H-∞ robust control technique

State-Feedback Controller Based on Pole Placement Technique for Inverted Pendulum System

This paper presents a state space control technique for inverted pendulum system using simulation and real experiment via MATLAB/SIMULINK software. The inverted pendulum is difficult system to control in the field of control engineering. It is also one of the most important classical control system problems because of its nonlinear characteristics and unstable system. It has three main problems that always appear in control application which are nonlinear system, unstable and non-minimumbehavior phase system. This project will apply state feedback controller based on pole placement technique which is capable in stabilizing the practical based inverted pendulum at vertical position. Desired design specifications which are 4 seconds settling time and 5 % overshoot is needed to apply in full state feedback controller based on pole placement technique. First of all, the mathematical model of an inverted pendulum system is derived to obtain the state space representation of the system. Then, the design phase of the State-Feedback Controller can be conducted after linearization technique is performed to the nonlinear equation with the aid of mathematical aided software such as Mathcad. After that, the design is simulated using MATLAB/Simulink software. The controller design of the inverted pendulum system is verified using simulation and experiment test. Finally the controller design is compared with PID controller for benchmarking purpose