An expert system to predict intricate saline - sodic subsoil patterns in upland South Australia

Soil Research ◽  
2009 ◽  
Vol 47 (6) ◽  
pp. 602 ◽  
Author(s):  
M. Thomas ◽  
R. W. Fitzpatrick ◽  
G. S. Heinson
Keyword(s):  
Expert System ◽  
Computer Model ◽  
A Priori ◽  
South Australia ◽  
Soil Survey ◽  
A Priori Knowledge ◽  
Environmental Covariates ◽  
Landscape Models ◽  
Soil Landscape ◽  
Priori Knowledge

Digital soil mapping (DSM) offers apparent benefits over more labour-intensive and costly traditional soil survey. Large cartographic scale (e.g. 1 : 10 000 scale) soil maps are rare in Australia, especially in agricultural areas where they are needed to support detailed land evaluation and targeted land management decisions. We describe a DSM expert system using environmental correlation that applies a priori knowledge from a key area (128 ha) soil–landscape with a regionally repeating toposequence to predict the distribution of saline–sodic subsoil patterns in the surrounding upland farming region (2275 ha) in South Australia. Our predictive framework comprises interrelated and iterative steps, including: (i) consolidating a priori knowledge of the key area soil–landscape; (ii) refining existing mentally held and graphic soil–landscape models; (iii) selecting suitable environmental covariates compatible with geographic information systems (GIS) by interrogation via 3D visualisation using a GIS; (iv) transforming the existing soil–landscape models to a computer model; (v) applying the computer model to the environmental variables using the expert system; (vi) performing the predictive mapping; and (vii) validation. The environmental covariates selected include: digital terrain attributes of slope gradient, topographic wetness index and plan curvature, and airborne gamma-radiometric K%. We apply selected soil profile physiochemical data from a prior soil survey to validate mapping. Results showed that we correctly predicted the saline–sodic subsoils in 10 of 11 reference profiles in the region.

Seeing, Knowing, and Doing

2020 ◽  
Author(s):  
Robert Audi
Keyword(s):  
Practical Reasoning ◽  
Practical Knowledge ◽  
A Priori ◽  
Human Action ◽  
A Priori Knowledge ◽  
Rational Action ◽  
Reasons For Action ◽  
Discriminative Response ◽  
Rich Information ◽  
Priori Knowledge

This book provides an overall theory of perception and an account of knowledge and justification concerning the physical, the abstract, and the normative. It has the rigor appropriate for professionals but explains its main points using concrete examples. It accounts for two important aspects of perception on which philosophers have said too little: its relevance to a priori knowledge—traditionally conceived as independent of perception—and its role in human action. Overall, the book provides a full-scale account of perception, presents a theory of the a priori, and explains how perception guides action. It also clarifies the relation between action and practical reasoning; the notion of rational action; and the relation between propositional and practical knowledge. Part One develops a theory of perception as experiential, representational, and causally connected with its objects: as a discriminative response to those objects, embodying phenomenally distinctive elements; and as yielding rich information that underlies human knowledge. Part Two presents a theory of self-evidence and the a priori. The theory is perceptualist in explicating the apprehension of a priori truths by articulating its parallels to perception. The theory unifies empirical and a priori knowledge by clarifying their reliable connections with their objects—connections many have thought impossible for a priori knowledge as about the abstract. Part Three explores how perception guides action; the relation between knowing how and knowing that; the nature of reasons for action; the role of inference in determining action; and the overall conditions for rational action.

How Reality is Reasonable

2018 ◽  
Author(s):  
Donald C. Williams
Keyword(s):  
A Priori ◽  
A Priori Knowledge ◽  
Dimensional Manifold ◽  
The World ◽  
Being There ◽  
Ways Of Being ◽  
Fundamental Entity ◽  
Spatiotemporal Relations ◽  
Priori Knowledge

This chapter begins with a systematic presentation of the doctrine of actualism. According to actualism, all that exists is actual, determinate, and of one way of being. There are no possible objects, nor is there any indeterminacy in the world. In addition, there are no ways of being. It is proposed that actual entities stand in three fundamental relations: mereological, spatiotemporal, and resemblance relations. These relations govern the fundamental entities. Each fundamental entity stands in parthood relations, spatiotemporal relations, and resemblance relations to other entities. The resulting picture is one that represents the world as a four-dimensional manifold of actual ‘qualitied contents’—upon which all else supervenes. It is then explained how actualism accounts for classes, quantity, number, causation, laws, a priori knowledge, necessity, and induction.

How Do We Know that We’re Not Brains in Vats?

2018 ◽  
Author(s):  
Keith DeRose
Keyword(s):  
Epistemic Justification ◽  
A Priori ◽  
A Priori Knowledge ◽  
Conservative Approach ◽  
Contingent Fact ◽  
Brains In Vats ◽  
Priori Knowledge

In this chapter the contextualist Moorean account of how we know by ordinary standards that we are not brains in vats (BIVs) utilized in Chapter 1 is developed and defended, and the picture of knowledge and justification that emerges is explained. The account (a) is based on a double-safety picture of knowledge; (b) has it that our knowledge that we’re not BIVs is in an important way a priori; and (c) is knowledge that is easily obtained, without any need for fancy philosophical arguments to the effect that we’re not BIVs; and the account is one that (d) utilizes a conservative approach to epistemic justification. Special attention is devoted to defending the claim that we have a priori knowledge of the deeply contingent fact that we’re not BIVs, and to distinguishing this a prioritist account of this knowledge from the kind of “dogmatist” account prominently championed by James Pryor.

On A Priori Knowledge in Particle Filter for In-Vivo Analysis of Implanted Knee

2013 ◽  
Author(s):  
Shohei Tada ◽  
Syoji Kobashi ◽  
Kei Kuramoto ◽  
Fumiaki Imamura ◽  
Takatoshi Morooka ◽  
...  
Keyword(s):  
Particle Filter ◽  
A Priori ◽  
A Priori Knowledge ◽  
In Vivo Analysis ◽  
Priori Knowledge

Incorporating a priori knowledge into neural networks

1995 ◽  
Vol 31 (22) ◽  
pp. 1930-1931 ◽  
Author(s):  
D. Anguita ◽  
S. Rovetta ◽  
S. Ridella ◽  
R. Zunino
Keyword(s):  
Neural Networks ◽  
A Priori ◽  
A Priori Knowledge ◽  
Priori Knowledge

Computation of satellite clock–ephemeris corrections using a priori knowledge for satellite-based augmentation system

GPS Solutions ◽  
2016 ◽  
Vol 21 (2) ◽  
pp. 663-673 ◽  
Author(s):  
Jie Chen ◽  
Zhigang Huang ◽  
Rui Li
Keyword(s):  
A Priori ◽  
A Priori Knowledge ◽  
Satellite Clock ◽  
Priori Knowledge

A priori knowledge based particle filter for estimating 3-D pose position of implanted knee

2010 ◽  
Author(s):  
Yusuke Nakajima ◽  
Syoji Kobashi ◽  
Yohei Tsumori ◽  
Nao Shibanuma ◽  
Fumiaki Imamura ◽  
...  
Keyword(s):  
Particle Filter ◽  
A Priori ◽  
A Priori Knowledge ◽  
Knowledge Based ◽  
Priori Knowledge

Spectral-domain covariance estimation with a priori knowledge

2006 ◽  
Vol 42 (3) ◽  
pp. 1010-1020 ◽  
Author(s):  
P.R. Gurram ◽  
N.A. Goodman
Keyword(s):  
A Priori ◽  
Covariance Estimation ◽  
Spectral Domain ◽  
A Priori Knowledge ◽  
Priori Knowledge

Automated nanoscale AFM measurements using a-priori-knowledge

2009 ◽  
Author(s):  
C. Recknagel ◽  
H. Rothe
Keyword(s):  
A Priori ◽  
A Priori Knowledge ◽  
Priori Knowledge
Sign in / Sign up

Export Citation Format

Share Document