LEADER 01739cam a22003377i 4500 001 991003165279707536 005 20250430130143.0 008 950902s1587 it b 000 ita 035 $ab14291241-39ule_inst 035 $aCICOGNARA-4417$9ExL 040 $aBibl. Interfacoltà T. Pellegrino$bita 041 0 $aita 100 1 $aVergilio, Polidoro,$csac.$d$0311038 245 10$aDi Polidoro Virgilio da Urbino De gli inventori delle cose, libri otto.$cTradotti per M. Francesco Baldelli, con due tavole, una de' capitoli, e l'altra delle cose più notabili. 246 30$aDe gli inventori delle cose, libri otto. 250 $aNuouamente stampati. 260 $aIn Fiorenza :$bper Filippo, e Iacopo Giunti, e fratelli,$c1587.$e(In Firenza :$fappresso i Giunti,$g1587) 300 $a[72] 426 p.; $c4° (23 cm). 500 $aMarca tipogr. dei Giunti sul front. 500 $aCors. ; rom. 500 $aIniziali e fregi xil. 500 $aRiproduzione in microfiche dell'originale conservato presso la Biblioteca Apostolica Vaticana 594 $aEx libris Jos De Luca sulla guardia ant.$5BAV$3Stamp.De.Luca.IV.5856 787 18$iLeopoldo Cicognara Program :$tBiblioteca Cicognara$h[microform] : literary sources in the history of art and kindred subjects 787 18$tCatalogo ragionato dei libri d'arte e d'antichità / Leopoldo Cicognara 907 $a.b14291241$b01-04-22$c28-07-16 912 $a991003165279707536 945 $aLE002 SB Raccolta Cicognara, mcrf 1765(A)$g0$lle002$pE0.00$rn$so $t11$u0$v0$w0$x0$y.i15762592$z28-07-16 996 $aDi Polidoro Virgilio da Urbino De gli inventori delle cose, libri otto$91391019 997 $aUNISALENTO 998 $ale002$b28-07-16$cm$dg $e-$fita$git $h0$i1 LEADER 05567nam 22006615 450 001 9911039322403321 005 20251101120356.0 010 $a9789819515882$b(electronic bk.) 010 $z9789819515875 024 7 $a10.1007/978-981-95-1588-2 035 $a(MiAaPQ)EBC32384867 035 $a(Au-PeEL)EBL32384867 035 $a(CKB)41997170500041 035 $a(DE-He213)978-981-95-1588-2 035 $a(EXLCZ)9941997170500041 100 $a20251101d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Intelligence and Machine Learning for Climate Disaster Management /$fedited by Deepak Kumar, Nick P. Bassill 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (0 pages) 225 1 $aDisaster Resilience and Green Growth,$x2662-4893 311 08$aPrint version: Kumar, Deepak Artificial Intelligence and Machine Learning for Climate Disaster Management Singapore : Springer,c2025 9789819515875 327 $aChapter 1. Artificial Intelligence and Machine Learning for Climate Disaster Management -- Chapter 2. The Climate Science, designing, and implementation of Climate Policy -- Chapter 3. Strategic Integration of Green Innovation and Information Systems for Sustainable Development -- Chapter 4. Artificial Intelligence and Machine Learning to Assess the Effects of COVID-19 on Land Surface Temperature (LST) and Air Quality Index -- Chapter 5. Trend analysis of long-term temperature data for prediction of heat waves through statistical analysis using extreme value theory for climate disaster management -- Chapter 6. Risk Assessment and Vulnerability Mapping of Soil Erosion for Climate Resilient Disaster Management with Geospatial Techniques -- Chapter 7. A Geospatial approach for DEM-Based Surface Hydrological Modelling of Sub-Watersheds towards Climate Disaster Management -- Chapter 8. Urban Heat Island Mitigation through Green Infrastructure for Sustainable Development -- Chapter 9. Identification of soil erosion susceptible areas with analytical hierarchy process (AHP) modelling for climate disaster management -- Chapter 10. Role of Artificial Intelligence and Machine Learning for Climate Disaster Management and Earth Resource Management -- Chapter 11. From Data to Planning: Innovations through LiDAR technology and data science in Sustainable Urban Resource Management -- Chapter 12. Beyond the Office Walls: Exploring Nature Connectedness for Climate Stress and Disaster Management -- Chapter 13. Challenges and Future Trends in Climate Disaster Management. 330 $aThis edited volume highlights the importance of Artificial Intelligence (AI) and Machine Learning (ML) being utilised in climate disaster management to enhance preparedness, response, recovery, and mitigation efforts. The integration of AI and ML technologies with climate disaster management greatly improves decision-making, resource allocation, and overall effectiveness in addressing the impacts of climate-related disasters. It serves as a comprehensive guide to harnessing the power of geospatial information and services for climate-resilient disaster management. This book is a timely exploration of how cutting-edge technology can shape our response to climate-related disasters. The book embarks on a journey through the intricate web of geospatial data, uncovering its indispensable role in disaster preparedness, response, and recovery. Several ways in which AI and ML are being applied comprise Early Warning Systems, Disaster Response Planning, Damage Assessment, Risk Assessment and Vulnerability Mapping, Climate Modeling and Simulation, Resilience and Adaptation Strategies, Satellite Imagery for Disaster Response, Vulnerability Mapping and Resilience Planning, Data Integration in Emergency Response, Climate Adaptation Policies, among many others. Real-world case studies and practical insights illustrate how geospatial technology empowers early warning systems, enhances risk assessment, and streamlines resource allocation. It delves into the nuances of climate adaptation planning, shedding light on innovative strategies for building resilient communities and infrastructure. This book will serve as an invaluable resource for policymakers, disaster managers, researchers, and anyone concerned with safeguarding communities against climate-related disasters.>. 410 0$aDisaster Resilience and Green Growth,$x2662-4893 606 $aBioclimatology 606 $aHuman ecology$xStudy and teaching 606 $aEcology 606 $aEarth sciences 606 $aNatural disasters 606 $aClimate Change Ecology 606 $aEnvironmental Studies 606 $aEnvironmental Sciences 606 $aEarth Sciences 606 $aNatural Hazards 615 0$aBioclimatology. 615 0$aHuman ecology$xStudy and teaching. 615 0$aEcology. 615 0$aEarth sciences. 615 0$aNatural disasters. 615 14$aClimate Change Ecology. 615 24$aEnvironmental Studies. 615 24$aEnvironmental Sciences. 615 24$aEarth Sciences. 615 24$aNatural Hazards. 676 $a577.22 700 $aKumar$b Deepak$01354437 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9911039322403321 996 $aArtificial Intelligence and Machine Learning for Climate Disaster Management$94454542 997 $aUNINA