Spatial Modeling of Environmental Pollution and Ecological Risk
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Form:Einzelkauf Download
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Sprache:Englisch
175,00 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
01.12.2023
Herausgeber
Pravat Kumar Shit + weitereVerlag
Elsevier Science & Techn.Seitenzahl
475 (Printausgabe)
Dateigröße
57983 KB
Sprache
Englisch
EAN
9780323952835
Spatial Modeling of Environmental Pollution and Ecological Risk provides valuable information and insights for researchers, students and professionals in geography, hydrology, sedimentology, soil science, agriculture, engineering and GIS as they face increasingly complex challenges around development strategies for a sustainable society. Written by the world's leading researchers in their field, each article will begin with a short introductory essay that includes an overview of the sections' papers.
Individual chapters focus on the core themes of research and knowledge and some topics that have received lesser attention. Each chapter will review the current understanding of knowledge regarding the present study and scope and consider where future efforts should be directed.
- Discusses issues at the forefront of present research in environmental science, bioscience, ecology, pedogeomorphology, landscape, geoscience, forestry, hydrology and GIS
- Explores state-of-art techniques based on methodological and modeling in modern Deep learning and Machine learning geospatial techniques through case studies
- Describes novel control strategies, remediation and eco-restoration, and conservation techniques for sustainable development
Individual chapters focus on the core themes of research and knowledge and some topics that have received lesser attention. Each chapter will review the current understanding of knowledge regarding the present study and scope and consider where future efforts should be directed.
- Discusses issues at the forefront of present research in environmental science, bioscience, ecology, pedogeomorphology, landscape, geoscience, forestry, hydrology and GIS
- Explores state-of-art techniques based on methodological and modeling in modern Deep learning and Machine learning geospatial techniques through case studies
- Describes novel control strategies, remediation and eco-restoration, and conservation techniques for sustainable development
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