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Aggiungi al carrelloPaperback. Condizione: new. Paperback. AI methods are harnessed for environmental protection and resource conservation, offering innovative solutions to some of the world s most pressing ecological challenges. From monitoring biodiversity and predicting climate change impacts to optimizing energy consumption and managing waste, AI enables more efficient, data-driven decision-making. Machine learning algorithms analyze large datasets from satellite imagery, sensors, and environmental models to detect deforestation, pollution levels, and the health of ecosystems. AI-powered systems can improve resource management by forecasting demand and consumption patterns, enhancing the sustainability of water, energy, and agricultural systems. By automating processes, identifying trends, and proposing actionable insights, AI may empower governments, organizations, and individuals to implement more effective conservation strategies. AI Methods for Environmental Protection and Resource Conservation explores the role of intelligent technology in environmental science. It examines how artificial intelligence assists in conservation, deforestation monitoring, weather forecasting, CO2 removal, and sustainable transportation. This book covers topics such as big data, energy engineering, and sustainable development, and is a useful resource for engineers, business owners, academicians, researchers, and environmental scientists. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Da: AussieBookSeller, Truganina, VIC, Australia
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. Understanding and mitigating the impacts of flood hazards and wetland changes requires precise, data-driven approaches, making geostatistics a critical tool in environmental analysis. By applying spatial statistical techniques to hydrological, topographical, and climatic datasets, researchers can generate detailed, high-resolution maps that identify flood-prone areas and monitor wetland dynamics. This information enhances predictive modeling of flood events and supports sustainable land-use planning and ecosystem conservation. As climate change increases the frequency and intensity of extreme weather, geostatistical methods may help better assess vulnerability, inform policy, and guide adaptive strategies for at-risk communities and habitats. Geostatistical Insights on Mapping Flood Hazards and Wetland Dynamics examines the use of geostatistics in flood risk mitigation techniques. It explores how various intelligent technologies assist in geographical mapping for areas affected by shifting climates and hazardous weather patterns. This book covers topics such as geomorphology, risk mitigation, and climatology, and is a useful resource for engineers, climatologists, geologists, conservationists, academicians, researchers, and environmental scientists. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. AI methods are harnessed for environmental protection and resource conservation, offering innovative solutions to some of the world s most pressing ecological challenges. From monitoring biodiversity and predicting climate change impacts to optimizing energy consumption and managing waste, AI enables more efficient, data-driven decision-making. Machine learning algorithms analyze large datasets from satellite imagery, sensors, and environmental models to detect deforestation, pollution levels, and the health of ecosystems. AI-powered systems can improve resource management by forecasting demand and consumption patterns, enhancing the sustainability of water, energy, and agricultural systems. By automating processes, identifying trends, and proposing actionable insights, AI may empower governments, organizations, and individuals to implement more effective conservation strategies. AI Methods for Environmental Protection and Resource Conservation explores the role of intelligent technology in environmental science. It examines how artificial intelligence assists in conservation, deforestation monitoring, weather forecasting, CO2 removal, and sustainable transportation. This book covers topics such as big data, energy engineering, and sustainable development, and is a useful resource for engineers, business owners, academicians, researchers, and environmental scientists. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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EUR 267,53
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Da: Rarewaves.com USA, London, LONDO, Regno Unito
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Da: AussieBookSeller, Truganina, VIC, Australia
EUR 281,99
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. Understanding and mitigating the impacts of flood hazards and wetland changes requires precise, data-driven approaches, making geostatistics a critical tool in environmental analysis. By applying spatial statistical techniques to hydrological, topographical, and climatic datasets, researchers can generate detailed, high-resolution maps that identify flood-prone areas and monitor wetland dynamics. This information enhances predictive modeling of flood events and supports sustainable land-use planning and ecosystem conservation. As climate change increases the frequency and intensity of extreme weather, geostatistical methods may help better assess vulnerability, inform policy, and guide adaptive strategies for at-risk communities and habitats. Geostatistical Insights on Mapping Flood Hazards and Wetland Dynamics examines the use of geostatistics in flood risk mitigation techniques. It explores how various intelligent technologies assist in geographical mapping for areas affected by shifting climates and hazardous weather patterns. This book covers topics such as geomorphology, risk mitigation, and climatology, and is a useful resource for engineers, climatologists, geologists, conservationists, academicians, researchers, and environmental scientists. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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