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The book demonstrates the geospatial technology approach to data mining techniques, data analysis, modeling, risk assessment, visualization, and management strategies in different aspects of natural and social hazards. This book has 25 chapters associated with risk assessment, mapping and management strategies of environmental hazards. It covers major topics such as Landslide Susceptibility, Arsenic Contaminated Groundwater, Earthquake Risk Management, Open Cast Mining, Soil loss, Flood Susceptibility, Forest Fire Risk, Malaria prevalence, Flood inundation, Socio-Economic Vulnerability, River Bank Erosion, and Socio-Economic Vulnerability. The content of this book will be of interest to researchers, professionals, and policymakers, whose work involves environmental hazards and related solutions.

Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- Editors and Contributors -- 1 Introduction to Spatial Modeling of Flood Risk and Hazard: Societal Implication -- Abstract -- 1.1 Concept of Floods -- 1.2 Impact of Flood-Global Scenario -- 1.3 Floods in India-A Case Study -- 1.4 Need for Floods Prediction Map -- 1.5 Role of Geospatial Technology for Floods Prediction -- 1.6 Key Aims of the Book -- 1.7 Individual Chapters -- References -- 2 Flood Susceptibility Mapping Using Morphometric Parameters and GIS -- Abstract -- 2.1 Introduction -- 2.2 Study Area -- 2.3 Data and Methods -- 2.4 Results and Discussion -- 2.4.1 Morphometric Parameters -- 2.4.1.1 Linear Parameters -- 2.4.1.2 Areal Parameters -- 2.4.1.3 Relief Parameters -- 2.4.2 Prioritization of the Sub-basin for Flood Susceptibility -- 2.4.3 Validation -- 2.5 Conclusion -- References -- 3 Palaeohydrologic Estimates of Flood Discharge of Lower Ramganga River Catchment of Ganga Basin, India, Using Slackwater Deposits -- Abstract -- 3.1 Introduction -- 3.2 Study Reach -- 3.3 Database and Methodology -- 3.3.1 Flood Frequency Analysis by Log-Pearson Type III Distribution -- 3.3.2 Palaeoflood Hydrological Investigations -- 3.3.3 Grain Size Measurement -- 3.4 Result and Discussion -- 3.4.1 Hydrological Characteristics -- 3.4.2 Estimation of Probable Flood Discharge and Flood Recurrence Interval -- 3.4.3 Estimation of Palaeohydraulic Flood Discharge Using Slack Water Deposits -- 3.4.4 Stratigraphy and Grain Size Analysis of Slackwater Deposits -- 3.4.5 Accuracy Assessment -- 3.5 Conclusion -- References -- 4 Flood Risk Zone Identification Using Multi-criteria Decision Approach -- Abstract -- 4.1 Introduction -- 4.2 Study Area -- 4.3 Materials and Method -- 4.3.1 Collection of Secondary Data -- 4.3.2 Analysis of Flood Frequency -- 4.3.3 Satellite Data Acquisition and Preprocessing.

Infectious disease outbreaks pose a significant threat to human health worldwide. The outbreak of pandemic coronavirus disease 2019 (COVID-19) has caused a global health emergency. Thus, identification of regions with high risk for COVID-19 outbreak and analyzing the behaviour of the infection is a major priority of the governmental organizations and epidemiologists worldwide. The aims of the present study were to analyze the risk factors of coronavirus outbreak for identifying the areas having high risk of infection and to evaluate the behaviour of infection in Fars Province, Iran. A geographic information system (GIS)-based machine learning algorithm (MLA), support vector machine (SVM), was used for the assessment of the outbreak risk of COVID-19 in Fars Province, Iran whereas the daily observations of infected cases were tested in the—polynomial and the autoregressive integrated moving average (ARIMA) models to examine the patterns of virus infestation in the province and in Iran. The results of the disease outbreak in Iran were compared with the data for Iran and the world. Sixteen effective factors were selected for spatial modelling of outbreak risk. The validation outcome reveals that SVM achieved an AUC value of 0.786 (March 20), 0.799 (March 29), and 86.6 (April 10) that displays a good prediction of outbreak risk change detection. The results of the third-degree polynomial and ARIMA models in the province revealed an increasing trend with an evidence of turning, demonstrating extensive quarantines has been effective. The general trends of virus infestation in Iran and Fars Province were similar, although a more volatile growth of the infected cases is expected in the province. The results of this study might assist better programming COVID-19 disease prevention and control and gaining sorts of predictive capability would have wide-ranging benefits.