http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Computers in earth and environmental sciences
artificial intelligence and advanced technologies in hazards and risk management
1 online resource : illustrations (black and white, and color)
"Computers in Earth and Environmental Sciences: Artificial Intelligence and Advanced Technologies in Hazards and Risk Management addresses the need for a comprehensive book that focuses on multi-hazard assessments, natural and manmade hazards, and risk management using new methods and technologies that employ GIS, artificial intelligence, spatial modeling, machine learning tools and meta-heuristic techniques. The book is clearly organized into four parts that cover natural hazards, environmental hazards, advanced tools and technologies in risk management, and future challenges in computer applications to hazards and risk management. Researchers and professionals in Earth and Environmental Science who require the latest technologies and advances in hazards, remote sensing, geosciences, spatial modeling and machine learning will find this book to be an invaluable source of information on the latest tools and technologies available. Covers advanced tools and technologies in risk management of hazards in both the Earth and Environmental Sciences Details the benefits and applications of various technologies to assist researchers in choosing the most appropriate techniques for purpose Expansively covers specific future challenges in the use of computers in Earth and Environmental Science Includes case studies that detail the applications of the discussed technologies down to individual hazards"--Publisher
Intro -- Computers in Earth and Environmental Sciences: Artificial Intelligence and Advanced Technologies in Hazards and Risk Management -- Copyright -- Dedication -- Contents -- Contributors -- Acknowledgments -- Chapter 1: Predicting dissolved oxygen concentration in river using new advanced machines learning: Long-short term memor ... -- 1. Introduction -- 2. Materials and methods -- 2.1. Study area and data -- 2.2. Performance assessment of the models -- 3. Methodology -- 3.1. Gaussian process regression (GPR) -- 3.2. Genetic programming (GP) -- 3.3. Group method of data handling (GMDH) -- 3.4. Support vector regression (SVR) -- 3.5. Long short-term memory (LSTM) -- 3.6. Multiple linear regression (MLR) -- 4. Results -- 4.1. USGS 15015595 station -- 4.2. USGS 15024800 station -- 5. Discussion -- 6. Conclusions -- References -- Chapter 2: Fractal analysis of valley sections in geological formations of arid areas -- 1. Introduction -- 2. Materials and methods -- 2.1. Study area -- 2.2. Hypsometric integral -- 2.3. The ratio of valley floor width to valley height (Vf) -- 2.4. The ratio of valley depth to valley top width (Fr) -- 2.5. The stream length gradient index -- 2.6. Fractal geometry (monofractal and multifractal analyses) -- 3. Results -- 4. Discussion -- 5. Conclusion -- References -- Chapter 3: A data-driven approach for estimating contaminants in natural water -- 1. Introduction -- 1.1. Estimation of chloride in water -- 1.2. Data-driven tools applied for water quality -- 1.3. Research significance -- 2. Data and methods -- 2.1. Data -- 2.1.1. Study area -- 2.2. Decision tree -- 2.3. Methodology -- 3. Results and discussion -- 3.1. Development of DT models with recorded data from five districts -- 3.2. Development of DT models with recorded data from water logged districts.
3.3. Development of DT models with recorded data from water depleted districts -- 3.4. Comparison with previous works -- 3.5. Merits and demerits of the models in this study -- 4. Conclusions -- Acknowledgments -- Conflict of interest -- Data availability -- References -- Chapter 4: Application of analytical hierarchy process (AHP) in landslide susceptibility mapping for Qazvin province, N Iran -- 1. Introduction -- 2. Study area -- 3. Material and methods -- 3.1. Information used -- 3.2. Steps of AHP analysis -- 3.3. Principles of scoring the criterion and subcriterion -- 3.4. Estimating the final weight of the criteria -- 3.5. Evaluating the logical compatibility of the decisions -- 3.6. Prioritization of the effective parameters and preparation of decision-making tree -- 4. Results and discussion -- 4.1. Geological criteria -- 4.2. Environmental criterion -- 4.3. Geomorphological criteria -- 4.4. Hydrological criteria -- 4.5. Climatological criteria -- 4.6. Geotechnical criteria -- 4.7. Landslide hazard zoning in Qazvin province -- 5. Conclusions -- References -- Chapter 5: Assessment of machine learning algorithms in land use classification -- 1. Introduction -- 2. Material and method -- 2.1. Study area -- 2.2. Preprocessing -- 2.2.1. Atmospheric correction -- 2.3. Artificial neural network -- 2.4. Support vector machine -- 2.5. Accuracy assessment -- 3. Results -- 4. Conclusion -- References -- Chapter 6: Evaluation of land use change predictions using CA-Markov model and management scenarios -- 1. Introduction -- 2. Definitions and background -- 2.1. CA-Markov model -- 2.2. Land use change scenario -- 3. Material and method -- 3.1. Study area -- 3.2. Image preprocessing and classification -- 3.3. Land-use change model of 2018 with CA-Markov -- 3.4. Suitability map -- 3.5. Land-use change model of 2036 -- 4. Results and discussion.
5. Conclusion -- References -- Chapter 7: Topographical features and soil erosion processes -- 1. Introduction -- 2. Terminology and definition of hillslope geometry -- 3. Three-dimensional hillslopes characteristics in hydrological models -- 3.1. The effect of hillslopes geometry on surface and subsurface runoff -- 3.2. The effect of hillslopes geometry on time of concentration and time to equilibrium -- 3.3. The effect of hillslopes geometry on different soil erosion processes -- 4. Conclusion -- Acknowledgments -- References -- Chapter 8: Mapping the NDVI and monitoring of its changes using Google Earth Engine and Sentinel-2 images -- 1. Introduction -- 2. Doroudzan Dam Watershed -- 3. Methodology -- 3.1. NDVI -- 4. Results -- 5. Discussion -- 6. Conclusion -- References -- Chapter 9: Spatiotemporal urban sprawl and land resource assessment using Google Earth Engine platform in Lahore district ... -- 1. Introduction -- 1.1. Literature review -- 1.2. Rationale of the study -- 2. Study area -- 3. Methodology -- 4. Results and discussion -- 4.1. Land cover change assessment and mapping -- 4.2. Spatiotemporal land surface temperature -- 4.3. Landscape metrics for urban sprawl -- 5. Conclusion -- References -- Chapter 10: Using OWA-AHP method to predict landslide-prone areas -- 1. Introduction -- 2. Study area -- 3. Material and methods -- 3.1. Data used -- 3.2. Fuzzy logic -- 3.3. Analytical hierarchy process (AHP) -- 3.4. OWA-AHP method -- 3.5. Adaptive neuro-fuzzy inference system (ANFIS) -- 4. Results and discussion -- 4.1. Fuzzy logic -- 4.2. Computation of criterion weight -- 4.3. ANFIS method -- 4.4. Determining the optimal regression model -- 5. Conclusions -- Acknowledgments -- References -- Chapter 11: Multiscale drought hazard assessment in the Philippines -- 1. Introduction -- 2. Methodology -- 2.1. Standardized precipitation index.
2.2. Principal component analysis -- 2.3. Data and data processing -- 3. Results -- 3.1. Multiscale drought characteristics -- 3.2. Drought hazard -- 4. Discussion -- 5. Conclusion -- References -- Chapter 12: Selection of the best pixel-based algorithm for land cover mapping in Zagros forests of Iran using Sentinel-2 ... -- 1. Introduction -- 2. Material and methods -- 2.1. Study area -- 2.2. Data -- 2.3. Methodology -- 2.3.1. Supervised classification of Sentinel-2A image -- Maximum likelihood (ML) -- Minimum distance (MD) -- Mahalanobis distance (MaD) -- Support vector machine (SVM) -- Neural network (NN) -- 2.3.2. Accuracy assessment of pixel-based algorithms for land cover mapping -- 3. Results -- 3.1. The land cover maps by pixel-based algorithms -- 3.2. Accuracy assessment of pixel-based algorithms for land cover mapping -- 4. Discussions -- 5. Conclusions -- References -- Chapter 13: Identify the important driving forces on gully erosion, Chaharmahal and Bakhtiari province, Iran -- 1. Introduction -- 2. Materials and methods -- 3. Methodology -- 4. Results and discussion -- 5. Conclusion -- Acknowledgment -- References -- Chapter 14: Analysis of social resilience of villagers in the face of drought using LPCIEA indicator case study: Downstre ... -- 1. Introduction -- 2. Methodology -- 2.1. Study area -- 2.2. LPCIEA indicator -- 2.3. Data extraction methods -- 3. Results -- 3.1. Statistical data and image processing -- 3.2. LPCIEA index -- 4. Discussion -- 5. Conclusion -- Acknowledgments -- References -- Chapter 15: Spatial and seasonal modeling of the land surface temperature using random forest -- 1. Introduction -- 2. Methodology -- 2.1. LST mapping using Landsat satellite images -- 2.2. Preparing effective factors on LST -- 2.2.1. Land use mapping through the RF model -- 2.2.2. Environmental and topographical features -- 2.3. RF algorithm.
2.4. Evaluation of the random forest model -- 2.5. Importance of variables -- 3. Results and discussion -- 3.1. LST mapping using Landsat images -- 3.2. LST map modeling -- 3.3. Importance of variables -- 4. Conclusion -- References -- Chapter 16: Municipal landfill site selection and environmental impacts assessment using spatial multicriteria decision a ... -- 1. Introduction -- 2. Study area -- 3. Data and methods -- 3.1. Criteria definition and map preparation -- 3.2. Landfill suitability analysis -- 3.3. Environmental impact assessment -- 4. Results and discussion -- 5. Conclusion -- Acknowledgments -- Conflict of interests -- References -- Chapter 17: Predictive habitat suitability models for Teucrium polium L. using boosted regression trees -- 1. Introduction -- 2. Material and method -- 2.1. Study area and sampling -- 2.2. Teucrium polium species -- 2.3. Selection of thematic variables -- 2.3.1. Geomorphic factors -- 2.3.2. Elevation/altitude/DEM -- 2.3.3. Slope and aspect -- 2.3.4. TWI -- 2.3.5. Curvatures -- 2.3.6. Soil factors -- 2.3.7. Annual mean rainfall -- 2.4. Correlation consideration of among 15 variables -- 2.5. BRT model -- 2.6. Model's evaluation -- 3. Results -- 3.1. Pearson correlation analysis -- 3.2. BRT model's performance -- 3.3. Explanatory factor importance analysis -- 3.4. Perspective map of suitable habitat distribution -- 3.5. Validation of BRT model -- 4. Discussions -- 4.1. BRT model -- 4.2. Evaluation indices -- 4.3. Environmental variable -- 5. Conclusion -- Funding -- Conflict of interest -- References -- Chapter 18: Ecoengineering practices for soil degradation protection of vulnerable hill slopes -- 1. Introduction -- 2. Materials and method -- 2.1. Soil erosion and land degradation -- 2.2. Soil degradation and the quality of water -- 2.3. Need for the implementation of techniques of bioengineering.
Includes bibliographical references and index.
Amsterdam, Netherlands ; Cambridge, MA, United States : Elsevier, [2022]