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The boy ready nations and disaster risk reduction : 19 countries in perspective / / edited by Michael H. Glantz
| The boy ready nations and disaster risk reduction : 19 countries in perspective / / edited by Michael H. Glantz |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (391 pages) |
| Disciplina | 363.3472 |
| Collana | Disaster Studies and Management |
| Soggetto topico |
Emergency management
Catàstrofes naturals Canvi climàtic Gestió d'emergències |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783030865030
9783030865023 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface -- Acknowledgments -- Notes on the Text -- Contents -- Introduction -- 1 Introduction to El Niño -- 2 El Niño and Its Effects -- 3 What El Niño Can Do: Mapping Global and Regional Hotspots -- 4 Forecasting El Niño and What It Means to Be "El Niño Ready" -- 5 Deconstructing Readiness -- 6 Limits to Readiness -- 7 Being Ready: The Who, the by When, and the to What Degree -- 8 Strategic and Tactical Responses -- 9 Using El Niño History and Science -- 10 Climate Change and ENSO -- 11 "Mostly El Niño": Uncertainty and Degrees of Attribution -- References -- East Asia -- China -- 1 Climate, Water, and Weather Setting -- 2 Strength and Reliability of El Niño Teleconnections in China -- 3 Forecasting El Niño -- 4 El Niño Analogue Years Noted -- 5 Early Warning -- 6 CMA's El Niño Information and Forecasts -- 7 First Observations of 2015-2016 El Niño -- 8 Impacts Across China -- 9 Regional Impacts of El Niño -- 10 Government and NMHS -- 11 Inter-Agencies Involved with El Niño Forecasts and Impacts -- 12 Media Coverage -- 13 Hurdles and Obstacles -- 14 El Niño-Related Surprises -- 15 Some Lessons Learned from El Niño Events -- References -- South Asia -- The Maldives -- 1 Political and Economic Setting -- 2 Climate of Maldives -- 3 Strength of El Niño Teleconnections in Maldives -- 4 Challenges in Communicating El Niño to and Its Impacts on Society -- 5 Forecasting El Niño for Maldives -- 6 Teleconnections During the 2014-16 El Niño and Their Impacts -- 6.1 2014-2016 El Niño Event -- 6.2 Indian Ocean Dipole (IOD) -- 6.3 Indian Ocean Warming -- 6.4 Madden-Julian Oscillation (MJO) -- 7 Climate Over Maldives, 2014-2016 -- 7.1 Rainfall -- 7.2 Temperature -- 8 What Were the Impacts on the Maldives? -- 8.1 Coral Bleaching -- 8.2 Flooding -- 8.3 Dengue -- 8.4 Fisheries -- 9 Hurdles and Obstacles -- 10 Lessons -- References -- Pakistan.
1 Political and Economic Setting -- 2 Strength of El Niño Teleconnections in Pakistan -- 3 Forecasting El Niño in Pakistan -- 4 El Niño Information and Forecasts -- 5 2015-2016 El Niño -- 5.1 Detection -- 5.2 Governance and Policy Response -- 6 Lessons Identified -- References -- Sri Lanka -- 1 Introduction -- 2 Political and Economic Setting and Vulnerability -- 3 Climate of Sri Lanka -- 4 Teleconnections with the Sri Lankan Climate -- 4.1 Impact of El Niño on Sri Lankan Climate -- 4.2 Influences of Indian Ocean Dipole -- 4.3 Indian Ocean Warming (IOW) -- 4.4 Madden-Julian Oscillation (MJO) -- 5 Forecasting El Niño -- 6 El Niño Information and Forecasting -- 7 Events During the 2014-2016 El Niño -- 8 Climate During the 2014-2016 El Niño -- 9 El Niño's Sectoral Impacts -- 9.1 Coral Bleaching -- 9.2 Drought in 2013-2014 -- 9.3 Flooding, October-December 2015 -- 9.4 Landslides and Floods, May 2016 -- 9.5 Hydropower -- 9.6 Rice Cultivation -- 9.7 Coconut Cultivation -- 9.8 Tea Cultivation -- 9.9 Dengue -- 10 Government and NMHS -- 11 Interagency Activities Dealing with El Niño Forecasts and Impacts -- 12 Media Coverage -- 13 Lessons Learned from El Niño Events -- References -- Southeast Asia -- The Philippines -- 1 The Philippines Context -- 2 Society -- 3 Economy -- 4 Geopolitical Structure -- 5 Research Method -- 6 An Overview of the 2015-2016 El Niño Event -- 7 Forecasting El Niño -- 8 The Media -- 9 "El Niño Readiness" Discourse: A Thematic Analysis -- 10 El Niño Impacts -- 11 Additional Noteworthy Impacts -- 12 Preparedness -- 13 Barriers to Preparedness -- 14 El Niño Discourse's Dominant Voices -- 15 Other Voices -- 16 Conflicting Voices -- 17 Concluding Comments -- References -- Myanmar -- 1 Introduction -- 2 The Global El Niño Situation in Early 2016 -- 3 A Brief History of El Niño in Myanmar -- 4 The 2016 El Niño Outlook Forum (ENOF). 5 Way Forward -- References -- Timor-Leste -- 1 Introduction -- 2 Drought, Agriculture, and Food Insecurity in Timor-Leste -- 3 The El Niño-Triggered Food Crisis of 2015-16 -- 3.1 El Niño Impacts in Timor-Leste -- 3.2 Timing of Impacts and Mitigative Actions Taken -- 3.3 Challenges Faced During the Drought Response -- 4 Modernizing Timor-Leste's NDMG to Support a Food Security EWS -- 4.1 Food Security-Focused Climate Products and Services -- 4.2 NDMG and User Interface -- 4.3 NDMG Modernization: Part of Broader Development and Investment Planning -- 5 Conclusions -- References -- Vietnam -- 1 Political and Economic Setting -- 2 ENSO-Related Risk Management Systems in Vietnam -- 3 El Niño Teleconnections in Vietnam -- 4 Analogue Year Data -- 5 Forecasting and Early Warning El Niño -- 6 The 2015-16 El Niño -- 7 Impacts of the 2015-16 El Niño on Vietnam -- 7.1 Agriculture and Food Security -- 7.2 Electricity Production -- 7.3 Health and Water Security -- 7.4 Sudden-Onset Extreme Events -- 7.5 Biodiversity and Ecology -- 8 Hurdles and Obstacles -- 9 Some Lessons Learned from El Niño Events in Vietnam -- 10 Vietnam Country Case Study Executive Summary -- References -- South Pacific -- Fiji -- 1 Fiji Context: Political and Economic Setting -- 2 Strength of El Niño Teleconnections in Fiji -- 3 Response to the 2015-16 El Niño Forecast -- 4 First Forecast of the 2015-16 El Niño -- 5 Analogue Years Noted Before the 2015-16 El Niño Event -- 6 Forecasting the 2015-16 El Niño -- 7 The 2015-16 El Niño Early Warning -- 8 The 2015-16 El Niño Impacts in Fiji -- 9 Tropical Cyclone Winston: Early Warning and Response -- 10 Media Coverage of the 2015-2016 El Niño -- 11 Regional Impacts of El Niño 2015-16 -- 12 National Hydrological and Meteorological Services (NHMSs) -- 13 Hurdles and Obstacles -- 14 Lessons Learned from the 2015-16 El Niño -- References. Pacific Islands Region -- 1 Introduction -- 2 El Niño's Potential Impacts in the Pacific Region -- 3 Preparedness for El Niño -- 4 Conclusions -- References -- Sub-Saharan Africa -- Ethiopia -- 1 Introduction -- 2 Climate-Related Hazards and Disaster -- 3 Different Governments, Different Levels of Preparedness -- 4 El Niño and the Ethiopian Economy -- 5 Ethiopian Preparedness and the 2015-16 El Niño -- 6 Impacts of and Responses to the 2015-16 El Niño -- 7 Resource Mobilization -- 8 Hurdles and Obstacles -- 9 Lessons Learned -- 10 Conclusions -- References -- Kenya-Regional -- 1 Introduction and Setting -- 2 The East African Climate -- 3 Conclusions and Recommendations -- References -- Kenya-Local -- 1 Introduction -- 2 Enabling Effective Preparedness and Response to Early Warning -- 3 Data Collection Methods -- 4 Study Area and Broader Context -- 5 Findings and Discussion -- 5.1 2015-16 El Niño Predictions in Kenya -- 5.2 Micro-Level Understanding and Perception of the El Niño Phenomenon -- 5.3 Micro-Level Impacts of the El Niño Rains, 2015 -- 5.4 Effectiveness of El Niño Early Warning Dissemination at the Local Level -- 5.5 Community Response and Preparedness Actions -- 6 Conclusions and Recommendations -- References -- South Africa -- 1 Introduction -- 2 Socio-Political and Environmental Context of South Africa -- 2.1 Introduction to South Africa -- 2.2 Socioeconomic Context -- 2.3 Weather and Climate Patterns in South Africa -- 3 El Niño in South Africa: Science, Detection, and Impacts -- 3.1 El Niño Detection and Warning Issuance in South Africa -- 3.2 Past and Current El Niños -- 3.3 Impacts of the 2015-16 El Niño in South Africa -- 4 To Be Forewarned is to Be Forearmed: How South Africa Responded to the 2015-16 El Niño-Related Droughts -- 4.1 Governmental Structure, Legislation, and Processes -- 5 Response to the 2015-16 El Niño. 5.1 Response to Early Warnings -- 6 Response to Drought Impacts -- 7 Drought (Mis-)Management -- 8 El Niño in the Media -- 9 Lessons Learned for El Niño Readiness in South Africa: Insights from El Niño -- 9.1 Evolution of Drought Management Practices -- 9.2 Lessons Learned and Recommendations -- References -- Latin America -- Central America -- 1 Introduction -- 2 The Central American Context -- 3 Contending with Hydrometeorological Hazards in Central America -- 4 From Major Disasters to Prevention -- 5 El Niño in Central America -- 6 El Niño 2015-16: Early Forecasts and News Reports -- 7 El Niño Reporting on Central American Internet News Sites -- 8 The Credibility of the Region's NMHSs -- 9 Concluding Comments -- References -- Cuba -- 1 Political and Economic Setting -- 2 El Niño Teleconnections: Regional Impacts of El Niño -- 3 Impacts in Cuba -- 4 Forecasting El Niño -- 5 Suggested Analogue El Niño Years Noted During the 2015-16 Event -- 6 Early Warning -- 7 El Niño Information and Forecasts -- 8 First Indicators of the 2015-16 El Niño -- 9 Government and the NMHS -- 10 Inter-agencies Involved in El Niño Forecasting and Response -- 11 Media Coverage -- 12 Hurdles and Obstacles -- 13 El Niño-Related Surprises in Cuba -- 14 Lessons Learned from El Niño Events -- References -- Ecuador -- 1 Setting -- 2 The 2015-16 El Niño Event -- References -- The Panama Canal -- 1 Setting -- 2 The Strength of El Niño Teleconnections in Panama -- 3 Forecasting El Niño -- 3.1 Previous El Niño Years -- 4 Early Warning -- 5 The Source of Panama's El Niño Information and Forecasts -- 5.1 Formation of the 2015-16 El Niño -- 6 2015-16 El Niño Impacts on Panama -- 7 Regional Impacts of El Niño -- 8 Government and the NMHS -- 9 Inter-Agencies Involved with El Niño Forecasts and Impacts -- 9.1 Media Coverage -- 10 Hurdles and Obstacles. 10.1 Lessons Learned from Past and Current El Niño Events. |
| Record Nr. | UNINA-9910551830003321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Geomorphic Risk Reduction Using Geospatial Methods and Tools
| Geomorphic Risk Reduction Using Geospatial Methods and Tools |
| Autore | Sarkar Raju |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , 2024 |
| Descrizione fisica | 1 online resource (328 pages) |
| Disciplina | 363.3472 |
| Altri autori (Persone) |
SahaSunil
AdhikariBasanta Raj ShawRajib |
| Collana | Disaster Risk Reduction Series |
| ISBN | 981-9977-07-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- About This Book -- Contents -- Editors and Contributors -- Part I Geomorphic Hazards and Machine Learning Techniques -- 1 Landslide Susceptibility Assessment Based on Machine Learning Techniques -- 1.1 Introduction -- 1.1.1 Landslides -- 1.1.2 Development of Landslide Susceptibility Assessment -- 1.1.3 Machine Learning for Dealing with Regression and Classification Problems -- 1.1.4 Supervised and Unsupervised Learning -- 1.2 Landslide Inventory and Disaster-Related Geo-Environmental Variable Dataset -- 1.2.1 Landslide Inventory -- 1.2.2 Geo-Environmental Database -- 1.3 Machine Learning Methods for Landslide Susceptibility Assessment -- 1.3.1 Linear Regression -- 1.3.2 Logistic Regression -- 1.3.3 Naïve Bayes -- 1.3.4 K-Nearest Neighbors -- 1.3.5 K-Means Clustering -- 1.3.6 Random Forest -- 1.3.7 Boosting Algorithms -- 1.3.8 Support Vector Machine -- 1.3.9 Artificial Neural Network -- 1.4 Summarization -- 1.4.1 Scale -- 1.4.2 Performance -- 1.4.3 Modeling -- 1.4.4 Interpretability -- References -- 2 Gully Erosion Susceptibility Using Advanced Machine Learning Method in Pathro River Basin, India -- 2.1 Introduction -- 2.2 Materials and Methods -- 2.2.1 Study Area -- 2.2.2 Preparing Gully Erosion Influencing Factors -- 2.2.3 Multicollinearity Test Among Independent Factors -- 2.2.4 Gully Erosion Susceptibility Mapping Based on BRT Model -- 2.2.5 Performance Assessment Through Receiver Operating Characteristics (ROC) Curve -- 2.3 Results -- 2.3.1 Considering Multicollinearity Effective Factors -- 2.3.2 Gully Erosion Susceptibility Model (GESM) Based on BRT -- 2.3.3 Validation of BRT Model -- 2.4 Discussion and Conclusion -- References -- 3 Artificial Neural Network Ensemble with General Linear Model for Modeling the Landslide Susceptibility in Mirik Region of West Bengal, India -- 3.1 Introduction -- 3.2 Study Area.
3.3 Materials and Methods -- 3.3.1 Landslide Inventory Map (LIM) -- 3.3.2 Preparing Landslide Conditioning Factors -- 3.3.3 Multicollinearity Test -- 3.3.4 Methods of Landslide Susceptibility Mapping -- 3.3.5 Validation Methods -- 3.4 Results -- 3.4.1 Multicollinearity Analysis -- 3.4.2 Landslide Susceptibility Maps -- 3.4.3 Analysis of Factor Importance by Frequency Ratio (FR) -- 3.4.4 Validation and Comparison of Models -- 3.5 Discussion -- 3.6 Conclusion -- References -- 4 An Advanced Hybrid Machine Learning Technique for Assessing the Susceptibility to Landslides in the Upper Meenachil River Basin of Kerala, India -- 4.1 Introduction -- 4.2 Study Area -- 4.3 Material and Method -- 4.3.1 Methodology -- 4.3.2 Landslide Inventory Map (LIM) -- 4.3.3 Preparing Landslide Conditioning Factors (LCF) -- 4.3.4 Multi-Collinearity Test -- 4.3.5 Methods of Landslide Susceptibility Mapping -- 4.3.6 Validation Methods -- 4.4 Results -- 4.4.1 Multi-Collinearity Analysis -- 4.4.2 Landslide Susceptibility Maps (LSMs) -- 4.4.3 Analysis of Factor Importance by Information Gain Ratio (IGR) -- 4.4.4 Validation and Comparison of Models -- 4.5 Discussion -- 4.6 Conclusion -- References -- 5 Novel Ensemble of M5P and Deep Learning Neural Network for Predicting Landslide Susceptibility: A Cross-Validation Approach -- 5.1 Introduction -- 5.2 Study Area -- 5.3 Material and Method -- 5.3.1 Methodology -- 5.3.2 Landslide Inventory Map (LIM) and Cross-Validation Method -- 5.3.3 Preparing Landslide Conditioning Factors -- 5.3.4 Multi-collinearity Test -- 5.3.5 Methods of Landslide Susceptibility Mapping -- 5.3.6 Validation Methods -- 5.4 Results -- 5.4.1 Multi-collinearity Analysis -- 5.4.2 Landslide Susceptibility Maps -- 5.4.3 Analysis of Factor Importance by Random Forest (RF) -- 5.4.4 Validation and Comparison of Models -- 5.5 Discussion -- 5.6 Conclusion -- References. 6 Assessment of Landslide Vulnerability Using Statistical and Machine Learning Methods in Bageshwar District of Uttarakhand, India -- 6.1 Introduction -- 6.2 Study Area -- 6.3 Material and Methods -- 6.3.1 Landslide Inventory -- 6.3.2 Landslide Vulnerability Determining Factors: -- 6.3.3 The Logistic Regression (LR): -- 6.3.4 Artificial Neuron Networks (ANN) -- 6.3.5 Validation Method-ROC Curve -- 6.4 Results -- 6.4.1 Landslide Susceptibility (Lsi) -- 6.4.2 Socio-Economic Vulnerability (S−evi) -- 6.4.3 Relative Landslide Vulnerability (RLvi) -- 6.4.4 Validation and Comparison: -- 6.5 Discussion -- 6.6 Conclusion -- References -- 7 An Ensemble of J48 Decision Tree with AdaBoost and Bagging for Flood Susceptibility Mapping in the Sundarbans of West Bengal, India -- 7.1 Introduction -- 7.2 Study Area -- 7.3 Material and Methods -- 7.3.1 Study Design -- 7.3.2 Data Source -- 7.3.3 Preparation of Flood Inventory Map (FIM) -- 7.3.4 Determination of Flood Conditioning Factors (FCFs) -- 7.3.5 Multi-collinearity Analysis -- 7.3.6 Models Applied for Flood Susceptibility Mapping -- 7.4 Results -- 7.4.1 Analysis of Multi-collinearity Problem Among FCFs -- 7.4.2 Assessment of Flood Susceptibility -- 7.4.3 Factors Importance Analysis -- 7.4.4 Validation and Comparison -- 7.5 Discussion -- 7.6 Conclusion -- References -- 8 Spatial Flash Flood Modeling in the Beas River Basin of Himachal Pradesh, India, Using GIS-Based Machine Learning Algorithms -- 8.1 Introduction -- 8.2 Study Area -- 8.3 Material and Method -- 8.3.1 Methodology -- 8.3.2 Flash Flood Inventory Map (FFIM) -- 8.3.3 Preparing Flash Flood Conditioning Factors -- 8.3.4 Multi-Collinearity Test -- 8.3.5 Methods of Flash Flood Susceptibility Mapping -- 8.3.6 Validation Methods -- 8.4 Results -- 8.4.1 Multi-collinearity Analysis -- 8.4.2 Flash Flood Susceptibility Maps. 8.4.3 Analysis of Factor Importance by Random Forest (RF) -- 8.4.4 Validation and Comparison of Models -- 8.5 Discussion -- 8.6 Conclusion -- References -- Part II Geomorphic Hazards and Multi-temporal Satellite Images -- 9 Quantitative Assessment of Interferometric Synthetic Aperture Radar (INSAR) for Landslide Monitoring and Mitigation -- 9.1 Introduction -- 9.1.1 Description of the El Forn Landslide -- 9.1.2 Physics-Based Model -- 9.2 Materials and Methods -- 9.2.1 Experimental Design -- 9.3 Results -- 9.3.1 Correlating InSAR with In-Situ Data for Seasonal and Extreme Ground Motion -- 9.3.2 Ordinary Kriging: Assessing the Necessary Number of InSAR Observations -- 9.4 Discussion -- References -- 10 Geospatial Study of River Shifting and Erosion-Deposition Phenomenon Along a Selected Stretch of River Damodar, West Bengal, India -- 10.1 Introduction -- 10.2 Study Area -- 10.3 Methodology -- 10.3.1 Data Used and Methodology -- 10.3.2 Morphometric Analysis -- 10.3.3 Sinuosity Index -- 10.3.4 Braiding Index -- 10.3.5 Alpha Index -- 10.4 Statistical Analysis -- 10.4.1 Correlation Coefficient -- 10.4.2 Student's t-test -- 10.4.3 River Reaches and Cross-Sections -- 10.5 Results and Discussion -- 10.5.1 Temporal Changes in Morphometric Parameters -- 10.5.2 Statistical Interpretation of Various Morphometric Parameters -- 10.5.3 Erosion-Deposition and River Shifting Phenomena -- 10.5.4 Spatio-Temporal Variation of Erosion-Deposition Scenarios Along the Selected River Stretch -- 10.5.5 Reach-Wise Erosion and Deposition Phenomena and Shifting of River Course from 2001 to 2018 -- 10.5.6 Statistical Interpretation of Erosion and Deposition Study -- 10.6 Conclusions -- References -- 11 Assessing the Shifting of the River Ganga Along Malda District of West Bengal, India Using Temporal Satellite Images -- 11.1 Introduction -- 11.2 Study Area. 11.3 Materials and Methods -- 11.3.1 Method of Image Classification and Accuracy Assessment -- 11.4 Results and Discussion -- 11.4.1 Shifting of Channel -- 11.4.2 Impact of Channel Shifting on Land Use and Land Cover Along River Bank 1990-2020 -- 11.5 Conclusion -- References -- 12 An Evaluation of Hydrological Modeling Using CN Method and Satellite Images in Ungauged Barsa River Basin of Pasakha, Bhutan -- 12.1 Introduction -- 12.2 Study Area -- 12.2.1 Study Method -- 12.2.2 Soil Grouping: Infiltration Testing -- 12.2.3 Hydrologic Soil Group (HSG) -- 12.2.4 Curve Number -- 12.3 Runoff Simulation in HEC-HMS 3.4 -- 12.4 Conclusion -- References -- 13 Measuring Landslide Susceptibility in Jakholi Region of Garhwal Himalaya Using Landsat Images and Ensembles of Statistical and Machine Learning Algorithms -- 13.1 Introduction -- 13.2 Study Area -- 13.3 Materials and Methods -- 13.3.1 Preparing Landslide Inventory Map -- 13.3.2 Preparing Landslide Conditioning Factors -- 13.3.3 Multicollinearity Test -- 13.3.4 Certainty Factor (CF) Model -- 13.3.5 Support Vector Machine (SVM) -- 13.3.6 Random Forest -- 13.3.7 ANN -- 13.3.8 Model Validation Techniques -- 13.4 Results -- 13.4.1 Analyzing the Multicollinearity -- 13.4.2 Relationship Between Landslides and Conditioning Factors -- 13.4.3 Landslide Susceptibility Condition -- 13.4.4 Model Validation and Comparison -- 13.5 Discussion -- 13.6 Conclusion -- References -- 14 Landslide Susceptibility Mapping Using Satellite Images and GIS-Based Statistical Approaches in Part of Kullu District, Himachal Pradesh, India -- 14.1 Introduction -- 14.2 Research Area -- 14.3 Methodology -- 14.4 Data Used and Database Preparation -- 14.4.1 Landslide Inventory -- 14.4.2 Prepared Thematic Layers -- 14.5 Adopted Probabilistic Approaches -- 14.5.1 Frequency Ratio (FR) -- 14.5.2 Shannon Entropy (SE). 14.5.3 Information Value (IV). |
| Record Nr. | UNINA-9910855372203321 |
Sarkar Raju
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| Singapore : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Preparing for disaster : building household and community capacity / / by Douglas Paton, PH.D., C.Psychol., School of Psychology, University of Tasmania, Launceston, Tasmania, Australia and John McClure, PH.D., School of Psychology, Victoria University, Wellington, New Zealand
| Preparing for disaster : building household and community capacity / / by Douglas Paton, PH.D., C.Psychol., School of Psychology, University of Tasmania, Launceston, Tasmania, Australia and John McClure, PH.D., School of Psychology, Victoria University, Wellington, New Zealand |
| Autore | Paton Douglas |
| Pubbl/distr/stampa | Springfield, Illinois : , : Charles C Thomas, Publisher, , 2013 |
| Descrizione fisica | 1 online resource (259 p.) |
| Disciplina | 363.3472 |
| Altri autori (Persone) | McClureJohn |
| Soggetto topico |
Emergency management
Preparedness |
| ISBN | 0-398-08897-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
""PREPARING FOR DISASTER""; ""CONTENTS""; ""Chapter 1 CO-EXISTING WITH A HAZARDOUS ENVIRONMENT""; ""Chapter 2 PEOPLE, HAZARDS, AND HAZARD MITIGATION""; ""Chapter 3 HAZARD READINESS AND PREPAREDNESS""; ""Chapter 4 PEOPLE�S BELIEFS AND HAZARD PREPAREDNESS""; ""Chapter 5 PREDICTING HAZARD PREPAREDNESS: SOCIAL COGNITIVE INFLUENCES""; ""Chapter 6 SOCIAL INFLUENCES ON HAZARD BELIEFS""; ""Chapter 7 HAZARD PREPAREDNESS: COMMUNITY ENGAGEMENT AND EMPOWERMENT""; ""Chapter 8 CROSS-CULTURAL PERSPECTIVES ON HAZARD PREPAREDNESS""; ""Chapter 9 BUSINESS PREPAREDNESS""
""FUTURE ISSUES IN HAZARD PREPAREDNESS: ENGAGING PEOPLE, SCIENCE, PRACTICE""""REFERENCES""; ""INDEX"" |
| Record Nr. | UNINA-9910693976303321 |
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Paton Douglas
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| Springfield, Illinois : , : Charles C Thomas, Publisher, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Reducing Disaster: Early Warning Systems For Climate Change / / edited by Ashbindu Singh, Zinta Zommers
| Reducing Disaster: Early Warning Systems For Climate Change / / edited by Ashbindu Singh, Zinta Zommers |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | Dordrecht : , : Springer Netherlands : , : Imprint : Springer, , 2014 |
| Descrizione fisica | 1 online resource (394 p.) |
| Disciplina | 363.3472 |
| Soggetto topico |
Climate change
Energy policy Energy and state Energy systems Climate Change Energy Policy, Economics and Management Energy Systems |
| ISBN | 94-017-8598-8 |
| Classificazione |
RB 10121
ZG 9290 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Chapter 1: The Impact of Climate Change on Natural Disasters -- Chapter 2: Challenges in Early Warning of the Persistent and Widespread Winter Fog over the Indo-Gangetic Plains: A Satellite Perspective -- Chapter 3: Assessing human vulnerability to climate change from an evolutionary perspective -- Chapter 4: Early Warning Systems Defined -- Chapter 5: The State of Early Warning Systems -- Chapter 6: Climate Change and Early Warning Systems for Wildland Fire -- Chapter 7: Climate Change Implications and Use of Early Warning Systems for Global Dust Storms -- Chapter 8: Applications of Medium Range Probabilistic Flood Forecast for Societal Benefits - Lessons Learned from Bangladesh -- Chapter 9: Flood forecasting and early warning: an example from the UK Environment Agency -- Chapter 10: The Evolution of Kenya’s Drought Management System -- Chapter 11: Understanding the warning process through the lens of practice: emancipation as a condition of action. Some lessons from France -- Chapter 12: The Effect of Early Flood Warnings on Mitigation and Recovery during the 2010 Pakistan Floods -- Chapter 13: Disasters are gendered: what’s new? -- Chapter 14: The Ethics of Early Warning Systems for Climate Change -- Chapter 15: Decadal Warning Systems -- Chapter 16: The role of scientific modelling and insurance in providing innovative solutions for managing the risk of natural disasters -- Chapter 17: “Follow the spiders”: Ecosystems as Early Warnings -- Chapter 18: Natural hazards and Climate Change in Kenya: Minimizing the impacts on vulnerable communities through Early Warning Systems. . |
| Record Nr. | UNINA-9910299620703321 |
| Dordrecht : , : Springer Netherlands : , : Imprint : Springer, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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