Classification of Dryland Salinity Risk using Artificial Neural Networks

First Page		Document Content
Date: 2013-01-15 18:53:36 Earth Neural networks Soil science Learning Hydrology Artificial neural network Salinity Groundwater model Backpropagation Computational neuroscience Science Water		Classification of Dryland Salinity Risk using Artificial Neural Networks Add to Reading List Source URL: www.mssanz.org.au Download Document from Source Website File Size: 294,96 KB Share Document on Facebook

	Invasive Earthworms in the Northeastern USA and the Horticulture Industry Josef Görres, Soil Scientist The University of Vermont, Plant & Soil Science Department Email: January 2014 General Information. DocID: 1vq5b - View Document
	SOIL AND CROP SCIENCE STUDENT 1415 South Soil Street, Fort Collins, CO3555~ QUALIFICATIONS  Understanding of research methods including sample collection, analysis and data compilati DocID: 1vona - View Document
	Guidelines and Procedures for PLSCInternshiprevision) 1. Eligibility: PLSCis open students majoring in turfgrass and soil science, horticulture, sustainable plant & soil systems, landscape DocID: 1vhLT - View Document
	Soils Overview Provided by the Soil Science Society of America Soils are complex mixtures of minerals, water, air, organic matter, and countless organisms that are the decaying remains of once-living things. It forms at DocID: 1va3u - View Document
	2018 Mailing List Rentals APPROXIMATE MEMBERSHIP and CERTIFICATION COUNTS (as of – subject to change) American Society of Agronomy Crop Science Society of America Soil Science Society of America DocID: 1v7XV - View Document