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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.
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.
1. Drinking Water Production with a Dual Floating Medium—Sand Filter System.- 2. Determination of Reduced Sulfur Compounds in the Aquatic Environment by High-Performance Liquid Chromatography and Capillary Electrophoresis.- 3. Metal Speciation in Overflow and Leachate from a Thermal Power Plant Ash Pond: Impact on Receiving Waters.- 4. A Possibility of Application of Clinoptilolite for Water Pollution Control.- 5. Effect of Land Management in Winter Crop Season on Methane Emission from the Following Rice Growth Period.- 6. Studies on N2O Emissions from Agricultural Land of Rice-Wheat Rotation System in the Tai-Lake Region of China.- 7. Atmospheric Deposition Measurements in Northern Poland.- 8. Control of Volatile Organics Emission to the Atmosphere during the Solvent Sublation Process.- 9. A Method of Reducing the SO2 Emission from Power Boilers.- 10. Atmosphere Protection through Energy Loss Minimization.- 11. Problems of the Implementation of Environmental Management System According to ISO14001 in Poland.- 12. Innovative Technology for Municipal Waste Utilization for Rzeszów City.- 13. Biofilm Reactors: A New Form of Wastewater Treatment.- 14. Retention Mechanisms in Nanofiltration.- 15. Nanofiltration for Removal of Organic Substances from Waste Water: Application in the Textile Industry.- 16. Metal-Ion Selectivity of Phosphoric Acid Resin in Aqueous Nitric Acid Media.- 17. Catalytic Oxidation of 1,2-Dichloropropane on Copper-Zinc Catalyst.- 18. Thermocatalytic Treatment of Sulphur Organic Compounds.- 19. Simultaneous Electrooxidation of Cyanides and Recovery of Copper on Carbon Fibre.- 20. Neutralization of Hazardous Wastes Combined with Clinker Manufacturing.- 21. An Attempt to Estimate the PCDF/PCDD Emissions from Waste Incinerated in Cement Kilns.- 22. TheUse of EDTA to Increase the Leachability of Heavy Metals from Municipal Solid Waste Incinerator Fly Ash.- 23. Ecologic and Economic Aspects of Utilization of Fly Ashes for Road Construction.- 24. Solidification/Stabilisation of Hazardous Waste Containing Arsenic: Effect of Waste Form Size on the Leachability.- 25. A New Method for Treatment of Chromium Containing Wastes.- 26. Agricultural Use of Sludge in China.- 27. A Model Study of Soil Acidification in a Small Catchment Near Guiyang, Southwestern China.- 28. The Relative Importance of Aluminum Solid-Phase Component in Agricultural Soils Treated with Oxalic and Sulfuric Acids.- 29. The Role of Organic Matter and Aluminum in Zinc and Copper Transport through Forest Podsol Soil Profiles.- 30. Aluminum Mobilization by Sulfuric and Nitric Acids from Some Polish Soils.- 31. Soil and Soil Water Chemistry at Some Polish Sites with Acid Podzol Soils.- 32. The Role of Citric, Lactic and Oxalic Acids in Aluminum Mobilization from Some Polish and Chinese Agricultural Soils.- 33. Water-Soluble Rare Earth Elements in Some Top-Soils of China.- 34. Ion Exchanger Composites as Humus Substitute for Restoration of Degraded Soils.- 35. Effect of Concentration and Duration of Acid Treatment on Water Adsorption and Titration Behaviour of Smectite, Illite and Kaolin.- About the Editors.- Author Index.
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