Cover; Half Title Page ; Series Pages; Title Page ; Copyright Page; Contents; Foreword; Editors; Contributors; Introduction; Chapter 1: Network Science Perspectives on Engineering Adaptation to Climate Change and Weather Extremes; 1.1 Introduction; 1.2 Motivation; 1.3 Network Science in Climate Risk Management; 1.3.1 New Resilience Paradigm; 1.4 Network Technology Stack; 1.5 Case Studies: Telescoping Systems of Systems; 1.6 Conclusion; References; Chapter 2: Structured Estimation in High Dimensions: Applications in Climate; 2.1 Introduction

2.2 Sparse Structured Estimation and Structure Learning2.3 Sparse Group Lasso and Climate Applications; 2.3.1 SGL and Hierarchical Norms; 2.3.2 Experiments; 2.3.2.1 Prediction Accuracy; 2.3.2.2 Variable Selection for Brazil; 2.4 Multitask Sparse Structure Learning and Climate Applications; 2.4.1 Multitask Sparse Structure Learning; 2.4.1.1 Notation and Preliminaries; 2.4.1.2 Structure Estimation; 2.4.1.3 General MSSL Formulation; 2.4.1.4 Parameter Precision Structure; 2.4.1.5 Least Squares Regression; 2.4.1.6 Residual Precision Structure; 2.4.2 Experiments on GCMs Combination; 2.5 Conclusions

4.3.2.3 Kriging4.3.2.4 Analogue Techniques; 4.4 Bridging the Gaps; 4.4.1 Physics-Guided Data Mining; 4.4.2 Advancements in Spatiotemporal Data Mining; 4.4.2.1 Climate Networks for Covariate Selection; 4.4.2.2 Multitask Learning; 4.4.2.3 Mixture of Experts; 4.4.2.4 Downscaling Extremes; 4.4.2.5 Computational Cost; 4.4.3 Deep Learning for SD; 4.4.4 Case Study: Deep Belief Networks; 4.5 Conclusion; Acknowledgments; References; Chapter 5: Large-Scale Machine Learning for Species Distributions; 5.1 Introduction; 5.2 Theory and Concept; 5.3 Challenges of Learning Species Distributions