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Chemistry for the Protection of the Environment 3: Proceedings of the 11th International Conference held in Cairo, Assuan, and Luxor, Egypt, September 10-17, 1997

Proceedings

Series: Environmental Science Research (Closed) Volume: 55

By: Lucjan Pawłowski (Editor), Marjorie A Gonzales (Editor), Marzenna R Dudzińska (Editor), William J Lacy (Editor)

139 black & white illustrations, biography

Kluwer Academic Publishers

Paperback | Jul 2013 | #213379 | ISBN-13: 9781475796667
Availability: Usually dispatched within 1-2 weeks Details
NHBS Price: £149.00 $190/€178 approx
Hardback | Dec 1999 | #100876 | ISBN: 0306460262
Availability: Usually dispatched within 1-2 weeks Details
NHBS Price: £163.00 $208/€195 approx

About this book

The 25 years up to 1999 have seen a tremendous growth in the application of statistical and modelling techniques to ecological problems. This expansion has been accelerated by the increasing availability of software, books and computing power. However, the suitability of some of these approaches to data analysis, in a relatively knowledge-poor discipline such as ecology, can be questioned on grounds of appropriateness and robustness. One reason for these concerns is that many ecological problems are at best poorly defined and most lack algorithmic solutions. Machine learning methods offer the potential for a different approach to these difficult problems. One definition of machine learning is that it is concerned with inducing knowledge from data, where the data could be patterns in a game of chess or patterns in the species composition of natural communities. Unfortunately ecologists have little experience of these relatively recent and novel approaches to understanding data. This is a problem that is made more complex because there is no simple taxonomy of machine learning methods and there are relatively few examples in the mainstream ecological literature to encourage exploration. This text is aimed at introducing machine learning methods to a readership of professional ecologists. All but one of the chapters have been written by ecologists and biologists who highlight the application of a particular method to a particular class of problem. Examples include the identification of species, optimal mate choice, predicting species distributions and modelling landscape features. A group of experienced machine learning workers, who have become interested in environmental problems, have written a chapter that demonstrates how machine learning methods can be used to discover equations that describe the dynamic behaviour of ecological systems. The final chapter reviews "real learning", offering the potential for greater dialogue between the biological and machine learning communities.


Contents

- Water Resources--Quality of Surface and Drinking Water: Drinking Water Production with a Dual Floating Medium-Sand Filter System; B.A Bolto, et al.
- Determination of Reduced Sulphur Compounds in the Aquatic Environment by HPLC and CE; E. Kaniowska, et al.
- Air Pollutions--Reduction and Monitoring: Effect of Land Management in Winter Crop Season on Methane Emission from the Following Riced Growth Period; Z.C. Cai, H. Xu
- Studies on N2O Emissions from Agricultural Land of Rice-Wheat Rotation System in the Tai-Lake Region of China; X. Hua, X. Guangxi
- New Technologies in Wastewater Treatment: Problems of the Implementation of Environmental Management System According to ISO 14001 in Poland; R. Pochyluk, J. Szymanski
- Innovative Technology for Municipal Waste Utilization for Rzeszow City; B. Jamroz, J.A. Tomaszek
- Solid Waste Utilization: Neutralization of Hazardous Wastes Combined with Clinker Manufacturing; L. Pawlowski, et al.
- An Attempt to Estimate the PCDF/PCDD Emissions from Waste Incinerated in Cement Kiln; M.R. Dudzinska, et al.
- Polution Pathways and Soil Chemistry: A Model Study of Soil Acidification in a Small Catchment Near Guiyang, Southwestern China; L. Bohan, et al.
- 26 Additional Articles
- Index


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