scholarly journals A Simple Water Retention Model Based on Grain Size Distribution

2021 ◽  
Vol 11 (20) ◽  
pp. 9452
Author(s):  
Andrew Vidler ◽  
Olivier Buzzi ◽  
Stephen Fityus
Keyword(s):  
Plant Growth ◽  
Water Retention ◽  
Prediction Models ◽  
Mine Waste ◽  
Coarse Grained ◽  
Retention Model ◽  
Proposed Model ◽  
Life Options ◽  
Using Data ◽  
Drying Curves

The Hunter valley region in NSW Australia is an area with a heavy coal mining presence. As some mines come to their end of life, options are being investigated to improve the topsoil on post mining land for greater plant growth, which may allow economically beneficial farmland to be created. This research is part of an investigation into mixing a mine waste material, coal tailings, with topsoil in order to produce an improved soil for plant growth. Implementing such a solution requires estimation of the drying path of the water retention curves for the tailings and topsoil used. Instead of a lengthy laboratory measurement, a prediction of the drying curve is convenient in this context. No existing prediction models were found that were suitable for these mine materials, hence this paper proposes a simple and efficient model that can more accurately predict drying curves for these mine materials. The drying curves of two topsoils and two tailings from Australian coal mines were measured and compared with predictions using the proposed model, which performs favorably compared to several existing models in the literature. Additionally, the proposed model is assessed using data from a variety of fine- and coarse-grained materials in the literature. It is shown that the proposed model is overall more accurate than every other model assessed, indicating the model may be useful for various materials other than those considered in this study.

Mechanistic-Empirical Rut Prediction Model for In-Service Pavements

2000 ◽  
Vol 1730 (1) ◽  
pp. 99-109 ◽  
Author(s):  
Hyung Bae Kim ◽  
Neeraj Buch ◽  
Dong-Yeob Park
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Flexible Pavements ◽  
Proposed Model ◽  
Major Mode ◽  
Global Positioning ◽  
Pavement Engineering ◽  
Using Data ◽  
Consistency Properties

Rutting is a major mode of failure in flexible pavements. Development of accurate predictive rut performance models is an ongoing pursuit of the pavement engineering community. This has resulted in a plethora of rut prediction models ranging from purely mechanistic to empirical. Presented is the development of a mechanistic-empirical rut prediction model that uses data from 39 in-service flexible pavements from Michigan. The proposed model accounts for the rut contribution of the subgrade, subbase, base, and asphalt concrete layers. The model addresses inventory-type variables like pavement cross section, ambient temperature, and asphalt consistency properties. The applicability of the model was validated by using data from 24 Long-Term Pavement Performance–Global Positioning System (GPS) sites. For 19 of the 24 GPS sites, the predicted rut depth was within 5 mm of the measured rut depth.

scholarly journals UAV Image Multi-Labeling with Data-Efficient Transformers

2021 ◽  
Vol 11 (9) ◽  
pp. 3974
Author(s):  
Laila Bashmal ◽  
Yakoub Bazi ◽  
Mohamad Mahmoud Al Rahhal ◽  
Haikel Alhichri ◽  
Naif Al Ajlan
Keyword(s):  
Data Augmentation ◽  
Feature Representation ◽  
Aerial Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Proposed Model ◽  
Class Labels ◽  
Using Data ◽  
Uav Image

In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model.

scholarly journals Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2332
Author(s):  
Cecilia Martinez-Castillo ◽  
Gonzalo Astray ◽  
Juan Carlos Mejuto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forests ◽  
Prediction Models ◽  
Meteorological Station ◽  
Ann Model ◽  
Global Irradiation ◽  
Artificial Neural ◽  
Input Variables ◽  
Using Data

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

Multiparameter Real-World System Identification using Iterative Residual Tuning

2021 ◽  
pp. 1-12
Author(s):  
Adam Allevato ◽  
Mitch W Pryor ◽  
Andrea L. Thomaz
Keyword(s):  
System Identification ◽  
Robot Control ◽  
Tracking Error ◽  
Nonlinear System Identification ◽  
Visual Observation ◽  
Free System ◽  
Proposed Model ◽  
Derivative Free ◽  
Identification Technique ◽  
Using Data

Abstract In this work we consider the problem of nonlinear system identification, using data to learn multiple and often coupled parameters that allow a simulator to more accurately model a physical system or mechanism and close the so-called reality gap for more accurate robot control. Our approach uses iterative residual tuning (IRT), a recently-developed derivative-free system identification technique that utilizes neural networks and visual observation to estimate parameter differences between a proposed model and a target model. We develop several modifications to the basic IRT approach and apply it to the system identification of a 5-parameter model of a marble rolling in a robot-controlled labyrinth game mechanism. We validate our technique both in simulation—where we outperform two baselines—and on a real system, where we achieve marble tracking error of 4% after just 5 optimization iterations.

scholarly journals A simple model for the estimation of the number of fatalities due to floods in central Europe

2014 ◽  
Vol 14 (7) ◽  
pp. 1663-1676 ◽  
Author(s):  
M. Brazdova ◽  
J. Riha
Keyword(s):  
Central Europe ◽  
Pairwise Comparison ◽  
The Czech Republic ◽  
Factors Affecting ◽  
Hazard Exposure ◽  
Proposed Model ◽  
River Floods ◽  
Using Data ◽  
Human Losses ◽  
Significant Factors

Abstract. In this paper a model for the estimation of the number of potential fatalities is proposed based on data from 19 past floods in central Europe. First, the factors contributing to human losses during river floods are listed and assigned to the main risk factors: hazard – exposure – vulnerability. The order of significance of individual factors has been compiled by pairwise comparison based on experience with real flood events. A comparison with factors used in existing models for the estimation of fatalities during floods shows good agreement with the significant factors identified in this study. The most significant factors affecting the number of human losses in floods have been aggregated into three groups and subjected to correlation analysis. A close-fitting regression dependence is proposed for the estimation of loss of life and calibrated using data from selected real floods in central Europe. The application of the proposed model for the estimation of fatalities due to river floods is shown via a flood risk assessment for the locality of Krnov in the Czech Republic.

scholarly journals Predicting Corporate Financial Sustainability Using Novel Business Analytics

2018 ◽  
Vol 11 (1) ◽  
pp. 64 ◽  
Author(s):  
Kyoung-jae Kim ◽  
Kichun Lee ◽  
Hyunchul Ahn
Keyword(s):  
Financial Distress ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Support Vector ◽  
Model Parameters ◽  
Financial Sustainability ◽  
Business Analytics ◽  
Financial Distress Prediction ◽  
Proposed Model ◽  
Distress Prediction

Measuring and managing the financial sustainability of the borrowers is crucial to financial institutions for their risk management. As a result, building an effective corporate financial distress prediction model has been an important research topic for a long time. Recently, researchers are exerting themselves to improve the accuracy of financial distress prediction models by applying various business analytics approaches including statistical and artificial intelligence methods. Among them, support vector machines (SVMs) are becoming popular. SVMs require only small training samples and have little possibility of overfitting if model parameters are properly tuned. Nonetheless, SVMs generally show high prediction accuracy since it can deal with complex nonlinear patterns. Despite of these advantages, SVMs are often criticized because their architectural factors are determined by heuristics, such as the parameters of a kernel function and the subsets of appropriate features and instances. In this study, we propose globally optimized SVMs, denoted by GOSVM, a novel hybrid SVM model designed to optimize feature selection, instance selection, and kernel parameters altogether. This study introduces genetic algorithm (GA) in order to simultaneously optimize multiple heterogeneous design factors of SVMs. Our study applies the proposed model to the real-world case for predicting financial distress. Experiments show that the proposed model significantly improves the prediction accuracy of conventional SVMs.

scholarly journals An Objective Search for Unrecognized Bias in Validated COVID-19 Prediction Models

2021 ◽  
Author(s):  
Hossein Estiri ◽  
Zachary Strasser ◽  
Sina Rashidian ◽  
Jeffrey Klann ◽  
Kavishwar Wagholikar ◽  
...  
Keyword(s):  
Predictive Models ◽  
Medical Records ◽  
Prediction Models ◽  
Evaluation Studies ◽  
Protected Group ◽  
Increased Risk ◽  
Using Data ◽  
Bias Evaluation ◽  
Algorithmic Bias

The growing recognition of algorithmic bias has spurred discussions about fairness in artificial intelligence (AI) / machine learning (ML) algorithms. The increasing translation of predictive models into clinical practice brings an increased risk of direct harm from algorithmic bias; however, bias remains incompletely measured in many medical AI applications. Using data from over 56 thousand Mass General Brigham (MGB) patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we evaluate unrecognized bias in four AI models developed during the early months of the pandemic in Boston, Massachusetts that predict risks of hospital admission, ICU admission, mechanical ventilation, and death after a SARS-CoV-2 infection purely based on their pre-infection longitudinal medical records. We discuss that while a model can be biased against certain protected groups (i.e., perform worse) in certain tasks, it can be at the same time biased towards another protected group (i.e., perform better). As such, current bias evaluation studies may lack a full depiction of the variable effects of a model on its subpopulations. If the goal is to make a change in a positive way, the underlying roots of bias need to be fully explored in medical AI. Only a holistic evaluation, a diligent search for unrecognized bias, can provide enough information for an unbiased judgment of AI bias that can invigorate follow-up investigations on identifying the underlying roots of bias and ultimately make a change.

scholarly journals Environmentally Friendly Soil Water Conservation Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 5513-5520
Keyword(s):  
Plant Growth ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Water Use ◽  
Water Scarcity ◽  
Water Retention ◽  
Direct Method ◽  
Environmentally Friendly ◽  
Cultivated Plants

The conservation of water resources through their optimal use is a compulsory for countries with water shortages in the arid and semi-arid regions, and it should be in an environmentally friendly manner to avoid the serious consequences of the use of environmentally harmful substances, the implications of which are currently evident from climate change, pollution of water bodies, soils, etc. Since Egypt is one of those countries suffering from water scarcity and uses about 82.5 percent of its water consumption in agriculture, according to data of the Ministry of Irrigation in 2010, so this research is focusing on the use of new methods to increase the efficiency of irrigation water, to achieve high productivity of agricultural crops with less water use that will certainly help to alleviate or solve the water scarcity issue. The study used a physical based model, to simulate the methods used to increase sand soil properties to ensure larger water retention index. Within this work, soil have been sampled from different areas, to simulate the behavior of arid lands, under different water retention techniques. Soil was exposed to different techniques, as it was mixed with soil additives in different quantities and different types. Physical barriers of cohesive soil and polyethylene sheets were used in addition to studying the effect of mulch on water storage capacity in noncohesive soil. Water retention have been measured using the direct method of determination soil water content by oven drying and the volumetric water content (𝞱v ) with time graphs have been plotted in groups, as well as the cultivated plants have been monitored as to measure the influence on plants growing and irrigation efficiency. And the experiment showed that the use of rice straw (RS) and wheat straw (WS) in the powder condition have a significant effect in increasing in the soil water content and even to the plant growth, the WS obtained 𝞱v values approaching the loam soil at times and slightly less in the case of RS, when the percentage of RC and WS was 30% to the sandy soil volume/volume (v/v). Also the use of mulch of RS showed a noticeable increase in 𝞱v and significant improvement of plant growth to that without mulch. These proven technologies can be used in sandy land targeted for reclamation to reduce water use in agriculture.

scholarly journals Characterizing the oligogenic architecture of plant growth phenotypes informs genomic selection approaches in a common wheat population

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Noah DeWitt ◽  
Mohammed Guedira ◽  
Edwin Lauer ◽  
J. Paul Murphy ◽  
David Marshall ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Plant Growth ◽  
Genomic Selection ◽  
Plant Height ◽  
Prediction Models ◽  
Growth Traits ◽  
Heading Date ◽  
Additive Genetic Variation ◽  
Moderate Effect ◽  
Over Time

Abstract Background Genetic variation in growth over the course of the season is a major source of grain yield variation in wheat, and for this reason variants controlling heading date and plant height are among the best-characterized in wheat genetics. While the major variants for these traits have been cloned, the importance of these variants in contributing to genetic variation for plant growth over time is not fully understood. Here we develop a biparental population segregating for major variants for both plant height and flowering time to characterize the genetic architecture of the traits and identify additional novel QTL. Results We find that additive genetic variation for both traits is almost entirely associated with major and moderate-effect QTL, including four novel heading date QTL and four novel plant height QTL. FT2 and Vrn-A3 are proposed as candidate genes underlying QTL on chromosomes 3A and 7A, while Rht8 is mapped to chromosome 2D. These mapped QTL also underlie genetic variation in a longitudinal analysis of plant growth over time. The oligogenic architecture of these traits is further demonstrated by the superior trait prediction accuracy of QTL-based prediction models compared to polygenic genomic selection models. Conclusions In a population constructed from two modern wheat cultivars adapted to the southeast U.S., almost all additive genetic variation in plant growth traits is associated with known major variants or novel moderate-effect QTL. Major transgressive segregation was observed in this population despite the similar plant height and heading date characters of the parental lines. This segregation is being driven primarily by a small number of mapped QTL, instead of by many small-effect, undetected QTL. As most breeding populations in the southeast U.S. segregate for known QTL for these traits, genetic variation in plant height and heading date in these populations likely emerges from similar combinations of major and moderate effect QTL. We can make more accurate and cost-effective prediction models by targeted genotyping of key SNPs.

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