scholarly journals Use of Topographic Models for Mapping Soil Properties and Processes

Soil Systems ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 32 ◽  
Author(s):  
Xia Li ◽  
Gregory W. McCarty ◽  
Ling Du ◽  
Sangchul Lee
Keyword(s):  
Soil Properties ◽  
Large Scale ◽  
Model Building ◽  
Principal Component Regression ◽  
Principal Component ◽  
Soil Parameters ◽  
Agricultural Field ◽  
Wide Range ◽  
Gravity Driven ◽  
Soc Density

Landscape topography is an important driver of landscape distributions of soil properties and processes due to its impacts on gravity-driven overland and intrasoil lateral transport of water and nutrients. Rapid advancements in aerial, space, and geographic technologies have led to large scale availability of digital elevation models (DEMs), which have proven beneficial in a wide range of applications by providing detailed topographic information. In this report, we presented a summary of recent topography-based soil studies and reviewed five main groups of topographic models in geospatial analyses widely used for soil sciences. We then compared performances of two types of topography-based models—topographic principal component regression (TPCR) and TPCR-kriging (TPCR-Kr)—to ordinary kriging (OKr) models in mapping spatial patterns of soil organic carbon (SOC) density and redistribution (SR) rate. The TPCR and OKr models were calibrated at an agricultural field site that has been intensively sampled, and the TPCR and TPCR-Kr models were evaluated at another field of interest with two sampling transects. High-resolution topographic variables generated from light detection and ranging (LiDAR)-derived DEMs were used as inputs for the TPCR model building. Both TPCR and OKr models provided satisfactory results on SOC density and SR rate estimations during model calibration. The TPCR models successfully extrapolated soil parameters outside of the area in which the model was developed but tended to underestimate the range of observations. The TPCR-Kr models increased the accuracies of estimations due to the inclusion of residual kriging calculated from observations of transects for local correction. The results suggest that even with low sample intensives, the TPCR-Kr models can reduce estimation variances and provide higher accuracy than the TPCR models. The case study demonstrated the feasibility of using a combination of linear regression and spatial correlation analysis to localize a topographic model and to improve the accuracy of soil property predictions in different regions.

scholarly journals Deep Learning-Based Sentimental Analysis for Large-Scale Imbalanced Twitter Data

2019 ◽  
Vol 11 (9) ◽  
pp. 190 ◽  
Author(s):  
Jamal ◽  
Xianqiao ◽  
Aldabbas
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Principal Component ◽  
Specific Topic ◽  
Weighting Method ◽  
Psychological Conditions ◽  
Twitter Data ◽  
Wide Range

Emotions detection in social media is very effective to measure the mood of people about a specific topic, news, or product. It has a wide range of applications, including identifying psychological conditions such as anxiety or depression in users. However, it is a challenging task to distinguish useful emotions’ features from a large corpus of text because emotions are subjective, with limited fuzzy boundaries that may be expressed in different terminologies and perceptions. To tackle this issue, this paper presents a hybrid approach of deep learning based on TensorFlow with Keras for emotions detection on a large scale of imbalanced tweets’ data. First, preprocessing steps are used to get useful features from raw tweets without noisy data. Second, the entropy weighting method is used to compute the importance of each feature. Third, class balancer is applied to balance each class. Fourth, Principal Component Analysis (PCA) is applied to transform high correlated features into normalized forms. Finally, the TensorFlow based deep learning with Keras algorithm is proposed to predict high-quality features for emotions classification. The proposed methodology is analyzed on a dataset of 1,600,000 tweets collected from the website ‘kaggle’. Comparison is made of the proposed approach with other state of the art techniques on different training ratios. It is proved that the proposed approach outperformed among other techniques.

GIS AND STATISTICAL EVALUATION OF SOIL QUALITY OF SIDI YAHYA, GHARBPLAIN (MOROCCO)

2021 ◽  
Vol 9 (6) ◽  
pp. 881-893
Author(s):  
Mbark Lahmar ◽  
Najib El Khodrani ◽  
Serine Omrania ◽  
Houria Dakak ◽  
Ahmed Douaik ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Properties ◽  
Soil Quality ◽  
Interpolation Method ◽  
Sustainable Use ◽  
Principal Component ◽  
Soil Parameters ◽  
Distance Weighted ◽  
Spatial Interpolation Method

The study of soil quality in irrigated areas is necessary to evaluate the sustainability of the agricultural production system. Indeed, the assessment of this quality is based on the physicochemical and biological characterization of soil parameters, as well as the knowledge of their spatial distribution and their evolution over time. This work aims to make a diagnosis of the current situation of soil quality of SidiYahya in the Gharb plain, Morocco. For this, sampling was carried out from 33 sites distributed over the studied plain during 2019. In this study, different soil properties including specifically texture, pH, electrical conductivity (EC), organic matter (OM), phosphorus (P2O5), and potassium (K2O) were measured while exchangeable sodium percentage (ESP) was calculated using the standard formula. Based on the observed soil properties a map was prepared by using a geographic information system (GIS), which was based specifically on the inverse distance weighted (IDW) spatial interpolation method. Data were processed using different statistical tools like descriptive statistics, correlation, and principal component analysis (PCA). Results of the study revealed that 70% of the soils have a heavy clayey texture with a predominance of vertisols (55%). Further, the study area soil is mainly alkaline (70%), poor in organic matter (61%) and phosphorus (52%), while very rich in potassium (70%), and non-saline (88%) contents. Soil pH was reported to be the least variable whereas sand, phosphorus, and salinity were the highest variable. IDW allowed mapping the soil properties by moving from punctual information to whole extent information. Furthermore, correlations were found between various soil properties by using PCA, 3 principal components (PCs) were able to extract 76% of the information from the 9 initial soil properties. Collected soil samples were grouped into 3 groups, based on their scores on the 3 PCs. Based on these two kinds of information, delineation of management zones can be established for a site-specific supply of agricultural inputs leading to better management of soil and water resources for securing their sustainable use.

Seasonal forecast of agricultural impacts of droughts in Mexico through a principal component regression based approach

2020 ◽  
Author(s):  
Roberto Real-Rangel ◽  
Adrián Pedrozo-Acuña ◽  
Agustín Breña-Naranjo
Keyword(s):  
Soil Moisture ◽  
Regional Scale ◽  
Principal Component Regression ◽  
Principal Component ◽  
Drought Monitoring ◽  
Drought Forecasting ◽  
Moisture Index ◽  
The North ◽  
Wide Range ◽  
Monitoring And Forecasting

<p>Drought monitoring and forecasting allows to adopt mitigating actions in early stages of an event to reduce the vulnerability of a wide range of environmetal, economical and social sectors. In Mexico, various drought monitoring systems on national and regional scale perform a follow up of these events, such as the Drought Monitor in Mexico, and the North American Drought Monitor, but seasonal drought forecasting is still a pending task. This study aims at fill this gap applying a methodology that uses data derived from a globally available atmospheric reanalysis product and a principal component regression based model oriented to predict drought impacts in rainfed crops associated to deficits in the soil moisture, estimated by means of the standardized soil moisture index (SSI). Using the state of Guanajuato (Center-North of Mexico) as a study case, the model generated yielded RSME values of 0.74 using regional and global hydrological, climatic and atmospheric variables as predictors with a lead-time of 4 months.</p>

Wavelength Selection Characterization for NIR Spectra

1997 ◽  
Vol 51 (5) ◽  
pp. 689-699 ◽  
Author(s):  
Jason M. Brenchley ◽  
Uwe Hörchner ◽  
John H. Kalivas
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Least Squares ◽  
Large Scale ◽  
Near Infrared ◽  
Optimization Algorithms ◽  
Principal Component Regression ◽  
Principal Component ◽  
Computer Time ◽  
Operational Parameters

For quantitative analysis of samples based on near-infrared (NIR) spectra, it is common practice to use full spectra in conjunction with partial least-squares (PLS) or principal component regression. Alternatively, least-squares (LS) can be used provided that proper wavelengths have been selected. Recently, optimization algorithms such as simulated annealing and the genetic algorithm have been applied to the selection of individual wavelengths. These algorithms are touted as global optimizers capable of locating the best set of parameters for a given large-scale optimization problem. Optimization methods such as simulated annealing and the genetic algorithm can become time intensive. Excessive computer time may be due not to computations but to the need to determine proper operational parameters to ensure acceptable optimization results. In order to reduce the time to select wavelengths, a different approach consists of selecting wavelengths directly on the basis of spectral criteria. This paper shows that results are not acceptable when one is separately using the criteria of large wavelength correlations to the prediction property, wavelengths associated with large values in loading vectors from PLS or derived from the singular value decomposition (SVD) of the spectra, and wavelengths associated with large PLS regression coefficients. However, it is demonstrated that acceptable results can be produced by using wavelength regions simultaneously associated with large correlations and loading values provided that the level of noise for identified wavelengths is also acceptable. Thus, this paper shows that, rather than using time-consuming optimization algorithms that generally select individual wavelengths, one can achieve improved results based on wavelength windows directly selected. In other words, the described approach is founded on the exclusion of spectral regions rather than the search for distinct wavelengths. As part of the NIR spectral characterization, it is shown that certain loading vectors from the SVD of spectra are equivalent to correlograms for prediction properties. The same is shown to be true for PLS loading vectors. This type of analysis is useful for determining dominant properties of spectra, i.e., primary properties responsible for spectral variations.

scholarly journals Dataset for Creating Pedotransfer Functions to Estimate Organic Liquid Retention of Soils

2014 ◽  
Vol 7 (1-2) ◽  
pp. 11-22
Author(s):  
Hilda Hernádi ◽  
András Makó
Keyword(s):  
Soil Properties ◽  
Water Retention ◽  
Organic Liquid ◽  
Principal Component ◽  
Fluid Retention ◽  
Nonaqueous Phase Liquids ◽  
Pedotransfer Functions ◽  
Estimation Methods ◽  
Scaling Method ◽  
Wide Range

Abstract Soil properties characterising pressure-saturation relationships (P-S), such as the fluid retention values or the fitting parameter of retention curves are basic input parameters for simulating the behaviour and transport of nonaqueous phase liquids (NAPLs) in subsurface. Recent investigations have shown the limited applicability of the commonly used estimation methods for predicting NAPL retention values in environmental practice. Alternatively, building pedotransfer functions (PTFs) based on the easily measurable properties of soils might give more accurate and reliable results for estimating hydraulic propertie s of soils and enable the utilisation of the wide range of data incorporated in Hungarian and international datasets. In spite of the availability of several well-established PTFs to predict the water retention of soils only a limited amount of research has been done concerning the NAPL retention of soils. Thus, in our study, data from our recent NAPL and water retention mea surements were collected into a dataset containing the basic soil properties as well. Relationships between basic soil propert ies and fluid retention of soils with water or an organic liquid (Dunasol 180/220) were investigated with principal component analysis. NAPL retention of soil samples were determined with PTFs, based on basic soil properties and their d erived values, and using a scaling method. Result of the statistical analysis (SPSS 13.1) revealed that using PTFs could be a promising alte rnative and could give more accurate results compared to the scaling method both for determining the NAPL saturation or the volumetric NAPL retention values of soils.

scholarly journals A modular computational framework for medical digital twins

2021 ◽  
Vol 118 (20) ◽  
pp. e2024287118
Author(s):  
J. Masison ◽  
J. Beezley ◽  
Y. Mei ◽  
HAL Ribeiro ◽  
A. C. Knapp ◽  
...  
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Model Building ◽  
Heterogeneous Data ◽  
Distributed Model ◽  
Modular Software ◽  
Digital Twins ◽  
Building Process ◽  
Wide Range ◽  
Models Of Disease

This paper presents a modular software design for the construction of computational modeling technology that will help implement precision medicine. In analogy to a common industrial strategy used for preventive maintenance of engineered products, medical digital twins are computational models of disease processes calibrated to individual patients using multiple heterogeneous data streams. They have the potential to help improve diagnosis, prognosis, and personalized treatment for a wide range of medical conditions. Their large-scale development relies on both mechanistic and data-driven techniques and requires the integration and ongoing update of multiple component models developed across many different laboratories. Distributed model building and integration requires an open-source modular software platform for the integration and simulation of models that is scalable and supports a decentralized, community-based model building process. This paper presents such a platform, including a case study in an animal model of a respiratory fungal infection.

scholarly journals Relationship between the 0.01 M CaCl2- and AL-soluble soil phosphorus contents

2018 ◽  
Vol 67 (1) ◽  
pp. 23-33
Author(s):  
Emese Szabó ◽  
László Huzsvai ◽  
Rita Kremper ◽  
Jakab Loch
Keyword(s):  
Regression Analysis ◽  
Soil Properties ◽  
Soil Phosphorus ◽  
Linear Regression Analysis ◽  
Extraction Methods ◽  
Soil Parameters ◽  
Regression Equations ◽  
Weak Correlation ◽  
Ammonium Lactate ◽  
Wide Range

The traditional Hungarian method for determining soil phosphorus (P) status is ammonium-lactate acetic acid (AL) extraction. AL is an acidic solution (buffered at pH 3.75), which is also able to dissolve P reserves, so there is a need for extraction methods that also characterize the mobile P pool. 0.01 M CaCl2-P is considered to directly describe available P forms, because the dilute salt solution has more or less the same ionic strength as the average salt concentration in many soil solutions. The amount of AL-P may be two orders of magnitude greater than that of CaCl2-P. Previous studies suggested that the relationship between AL-P and CaCl2-P was influenced by soil parameters. Regression analysis between AL-P and CaCl2-P showed medium or strong correlations when using soils with homogeneous soil properties, while there was a weak correlation between them for soils with heterogeneous properties. The objective of this study was to increase the accuracy of the conversion between AL-P and CaCl2-P, by constructing universal equations that also take soil properties into consideration. The AL-P and CaCl2-P contents were measured in arable soils (n=622) originating from the Hungarian Soil Information and Monitoring System (SIMS). These soils covered a wide range of soil properties. A weak correlation was found between AL-P and CaCl2-P in SIMS soils. The amounts and ratio of AL-P and CaCl2-P depended on soil properties such as CaCO3 content and texture. The ratio of AL-P to CaCl2-P changed from 37 in noncalcareous soils to 141 on highly calcareous soils. CaCl2-P decreased as a function of KA (plasticity index according to Arany), which is related to the clay content, while the highest AL-P content was found on loam soils, probably due to the fact that a high proportion of them were calcareous. The relationships between AL-P, CaCl2-P and soil properties in the SIMS dataset were evaluated using multiple linear regression analysis. In order to select the best model the Akaike Information Criterion (AIC) was used to compare different models. The soil factors included in the models were pHKCl, humus and CaCO3 content to describe AL-P, and KA, CaCO3 content and pHKCl to describe CaCl2-P. AL-P was directly proportional to pHKCl, humus and CaCO3 content, while CaCl2-P was inversely proportional to KA, CaCO3 content and pHKCl. The explanatory power of the models increased when soil properties were included. The percentage of the explained variance in the AL-P and CaCl2-P regression models was 56 and 51%, so the accuracy of the conversion between the two extraction methods was still not satisfactory and it does not seem to be possible to prepare a universally applicable equation. Further research is needed to obtain different regression equations for soils with different soil properties, and CaCl2-P should also be calibrated in long-term P fertilization trials.

scholarly journals Utilizing Probabilistic Downscaling Methods to Develop Streamflow Forecasts from Climate Forecasts

2017 ◽  
Vol 18 (11) ◽  
pp. 2959-2972 ◽  
Author(s):  
Amirhossein Mazrooei ◽  
A. Sankarasubramanian
Keyword(s):  
Large Scale ◽  
Multinomial Logistic Regression ◽  
Traditional Approach ◽  
Winter Season ◽  
Principal Component Regression ◽  
Principal Component ◽  
Climate Forecasts ◽  
Probabilistic Forecasts ◽  
Basin Scale ◽  
Streamflow Forecasts

Abstract Statistical information from ensembles of climate forecasts can be utilized in improving the streamflow predictions by using different downscaling methods. This study investigates the use of multinomial logistic regression (MLR) in downscaling large-scale ensemble climate forecasts into basin-scale probabilistic streamflow forecasts of categorical events over major river basins across the U.S. Sun Belt. The performance of MLR is then compared with the categorical forecasts estimated from the traditional approach, principal component regression (PCR). Results from both cross validation and split sampling reveal that in general, the probabilistic categorical forecasts from the MLR model have more accuracy and exhibit higher rank probability skill score (RPSS) compared to the PCR probabilistic forecasts. MLR forecasts are also more skillful than PCR forecasts during the winter season as well as for basins that exhibit high interannual variability in streamflows. The role of ensemble size of precipitation forecasts in developing MLR-based streamflow forecasts was also investigated. Because of its simplicity, MLR offers an alternate, reliable approach to developing categorical streamflow forecasts.

scholarly journals The Impact of Poplar Short Rotation Coppice on Topsoil Physical Properties and Related Water Conditions

2021 ◽  
Author(s):  
Victoria Virano Riquelme ◽  
Gabriela Fontenla-Razzetto ◽  
Filipa Tavares Wahren ◽  
Karl-Heinz Feger ◽  
Bálint Heil ◽  
...  
Keyword(s):  
Soil Properties ◽  
Hydraulic Properties ◽  
Water Retention ◽  
Sandy Loam ◽  
Short Rotation Coppice ◽  
Water Infiltration ◽  
Agricultural Field ◽  
Short Rotation ◽  
Wide Range ◽  
The Impact

AbstractIn Europe, the establishment of short rotation coppice (SRC) systems for biomass production has been expanding in the last decades. Several studies have considered the impacts of SRC on soil properties; many have focused on studying its effect on biochemical properties while only a few have addressed physical and hydraulic properties. This study reports the assessment of soil physical and hydraulic properties on two SRC sites on sandy soils planted with 3-year-old poplar trees and an adjacent conventional agricultural field in Western Slovakia. All sites contain a comparable sandy loam soil texture and both SRC fields differed only in the groundwater accessibility. Water infiltration experiments were conducted in the field with subsequent sampling of the upper topsoil (0–5 cm depth). The samples were further processed in the laboratory to obtain the water retention and hydraulic conductivity functions of the soil covering a wide range of soil pore saturation. These hydraulic functions were fitted by using the bimodal version of Kosugi-Mualem’s hydraulic model to estimate the pore-size distribution (PSD) of the soils. The comparison between the SRC field neighboring the agricultural field and the latter showed similar hydraulic soil properties such as the topsoil water retention. However, macropore content, bulk density (BD) and infiltration capacity differed under SRC particularly in the tree row. Analogously, the two SRC fields showed similar topsoil water contents. Other soil properties differed presenting an increased macropore content and higher BD in the SRC field with distant groundwater connection. Our findings suggest that the SRC management may influence the topsoil properties.

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