Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence

Front cover
Half title
Title
Copyright
Contents
Contributors
Chapter 1 Climate uncertainties and biodiversity: An overview
1.1 Introduction
1.1.1 Variations in the timing of periodic life cycle phases
1.1.2 Why there was sudden change in climate?
1.1.3 Effect of climate change on flora
1.2 Effect of climate change on fauna including Homo sapiens
1.3 Effect of climate change on health of humans
1.3.1 Heat- and cold-related impacts
1.3.2 Storms and floods
1.3.3 Ozone depletion: Impacts of ultraviolet radiation
1.3.4 Environment
1.4 Whether we are adjusting to change in climate
1.5 Applications of artificial intelligence and machine learning
1.6 Conclusion
References
Chapter 2 Historical perspectives on climate change and its influence on nature
2.1 Introduction
2.2 Ancient cultures and climate change
2.3 Global warming and climate change
2.3.1 The greenhouse effect and greenhouse gases
2.3.2 The keeling curve
2.3.3 Anthropogenic climate change
2.4 Public perception and knowledge of climate change
2.4.1 Mass media and climate change
2.5 Intergovernmental panel on climate change
2.6 Emergence of climate change legislation
2.6.1 The Montreal protocol
2.6.2 The Kyoto Protocol
2.6.3 The Paris climate accord
2.7 Climate change and environmental activism
2.7.1 The Al gore effect
2.7.2 The Greta Thunberg effect
2.8 The 2021 United Nations climate change conference
2.9 Climate change mitigation and adaptation challenges
2.10 Conclusions
References
Chapter 3 Impact of climate change on water quality and its assessment
3.1 Introduction
3.2 Climate change: A global concern
3.3 Climate change and impact on water
3.3.1 Climate and hydrological process
3.3.2 Components of hydrological cycle
3.4 Impact of climate change on groundwater
3.4.1 Global and regional perspectives
3.5 Observed and projected change under the influence of climate change
3.5.1 Water temperature and flow rate
3.5.2 Impacts on water quality parameters
3.5.3 Water temperature and aquatic life
3.5.4 Compound toxicity and water temperature
3.5.5 Dissolve oxygen and water temperature
3.5.6 Conductivity and water temperature
3.5.7 Oxidation reduction potential and water temperature
3.5.8 pH and water temperature
3.6 Summary
References
Chapter 4 Climate change impacts on water resources: An overview
4.1 Introduction
4.1.1 Background and significance
4.2 Observed climate change impacts
4.2.1 Precipitation
4.2.2 Cryospheric water resources
4.2.3 Surface water resources
4.2.4 Groundwater resources
4.3 Modeling approaches
4.3.1 Applications in water resources: A commentary
4.4 Sustainable water resources management using AI/ML under changing climate
4.5 Hybrid models
4.6 Conclusions and outlook
References
Chapter 5 Impact of plastics in the socio-economic disaster of pollution and climate change: The roadblocks of sustainability in India*
5.1 Introduction
5.2 Plastic and the environment: A brief overview
5.3 The role of plastic in climate change
5.4 The social impacts of plastics, its pollution, and climate change: A necessary evil
5.4.1 Agriculture
5.4.2 Public health
5.5 The plastic industry and the economy
5.6 Implications of a plastic ban
5.7 Law and the plastic fiasco: India in the global context
5.8 Conclusion
Conflicts of interest
References
Chapter 6 Impression of climatic variation on flora, fauna, and human being: A present state of art
6.1 Introduction
6.2 Climatic warming and their influences on biodiversity
6.2.1 Impact of greenhouse gases like CO2 emissions on ecosystem
6.2.2 Impact of temperature and UV radiations on biodiversity
6.3 Environmental stress factors on agriculture productivity
6.3.1 Environmental stress affects soil fertility by suppressing plant growth promoting microbial
(PGPM) growth
6.3.2 Environmental stress influence plant diseases by plant pathogenic microbial growth and resistance microbial growth
6.3.3 Environmental stress affects economical services
(e.g., pollination, provision of another species inhabitants, etc.)
6.4 Effects of climate changes on food web
6.5 Encroachment of climatic changes upon genetics-based diversity and evolutionary biology of fauna
6.6 Climatic alteration effects on genetic diversity and evolutionary biology of human
6.7 Metabolic engineering and synthetic biology approaches toward minimize various climate change issues for improving environmental conditions
6.8 Conclusions
Acknowledgment
References
Chapter 7 Impact of air quality as a component of climate change on biodiversity-based ecosystem services
7.1 Introduction
7.2 Air pollution and climate change
7.3 Effects of climate change and air quality on human population and infrastructure
7.3.1 Population decline in some cities
7.3.2 Ranking of cities and mega cities
7.3.3 Sustainable urbanization
7.3.4 Environmental impacts of air pollution
7.4 Impact of climate change and air quality on human health
7.4.1 Climate change and human health
7.4.2 Temperature-related death and illness
7.4.3 Air quality impacts
7.4.4 Extreme events
7.4.5 Vector-borne diseases
7.4.6 Water-related illness
7.4.7 Food safety, nutrition, and distribution
7.5 Impacts of climate change and air quality on biodiversity
7.5.1 State of biodiversity globally
7.5.2 Impacts of climate change on biodiversity
7.5.3 Causes of biodiversity loss
7.6 Mitigative and adaptive strategies toward sustainability
7.6.1 Mitigative strategies
7.6.2 Adaptive strategies
7.7 Discussions of the interplay of factors promoting climate change and biodiversity loss
7.8 Conclusion
References
Chapter 8 Role of climate change in disasters occurrences: Forecasting and management options
8.1 Introduction
8.2 Climate change and its effects
8.2.1 Impact of climate change on agriculture
8.2.2 Impact of climate change on irrigation water quality
8.2.3 Impact of climate change on food security
8.2.4 Impact of climate change on coastal ecosystems
8.2.5 Impact of climate change on biodiversity: A threat
8.3 Forecasting of climate changes
8.3.1 Agriculture forecasting
8.3.2 Forecasting flood water levels in the real-time manner
8.3.3 Global renewable energy forecasts
8.4 Disasters due to temperature and rainfall
8.5 Disasters due to urbanization
8.6 Management in terms of wetland utilization and agricultural discharge
8.7 Future perspectives
8.8 Conclusion
References
Chapter 9 Forecasting and management of disasters triggered by climate change
9.1 Introduction
9.1.1 Flood hazards and forecast models
9.1.2 Droughts and forecast models
9.2 Disaster risk management cycle
9.2.1 Prevention and/or mitigation
9.2.2 Preparedness
9.2.3 Response
9.2.4 Recovery
9.3 Forecasting and disaster management
9.4 Summary and conclusion
References
Chapter 10 El-Niño Southern Oscillation
and its effects
10.1 Introduction
10.1.1 Diverse nature of ENSO
10.1.2 Monitoring of ENSO
10.1.3 The ENSO teleconnection mechanism
10.2 Impact on global weather and climate
10.2.1 Impact on US weather
10.2.2 Impacts on Asian weather
10.2.3 Impact on Australian weather
10.2.4 Impacts on mid-latitude and polar climates
10.2.5 Impacts on tropical oceans
10.2.6 Impact of El-Niño on disasters
10.3 Indirect impact of ENSO
10.3.1 Impact on agriculture
10.3.2 Impact of El-Niño on economy
10.3.3 Impact of El-Niño on ecosystems
10.3.4 Impact of fishing industries
10.3.5 Impacts of El Niño on coral reefs
10.4 The profit or forfeiture outcome of EL NINO
10.5 Conclusion
10.6 The benefits of socialism
References
Chapter 11 Impact of socioeconomic parameters on adoption of climate resilient technology under varying vulnerability conditions: Evidences from Himalayan region
11.1 Introduction
11.2 Climate change resilience is necessary for the sustainable growth
11.3 About Himachal Pradesh
11.4 Strategies adopted to mitigate climate change impacts
11.5 Change in timing of sowing and harvesting of crops
11.6 Change in crop length period of different crops
11.7 Change in fertilizer, farm yard manure, pesticide, insecticide, and weedicide use
11.8 Strategies adopted in order to cope with the climate change for different crops
11.9 Factors influencing the choice of the strategy
11.10 Conclusions and policy implications
References
Chapter 12 Artificial intelligence/machine learning techniques in hydroclimatology: A demonstration of deep learning for future assessment of stream flow under climate change
12.1 Introduction
12.1.1 Impact of climate change on water resources
12.1.2 Different approaches in tackling the water resources problems
12.2 Artificial intelligence techniques in hydroclimatological/hydrometeorological problems
12.2.1 Artificial neural network
12.2.2 Radial basis function
12.2.3 K-nearest neighbors
12.2.4 Decision tree
12.2.5 Random forest
12.2.6 Support vector regression
12.2.7 Deep learning techniques
12.3 Application of deep learning techniques in simulating and forecasting streamflow
12.3.1 Streamflow modeling over historical period
12.3.2 Future assessment of streamflow
12.4 Concluding remarks and way forward
References
Chapter 13 The role of artificial intelligence strategies to mitigate abiotic stress and climate change in crop production
13.1 Introduction
13.2 Accelerating climate changes in plant breeding by applying artificial intelligence
13.3 Effect of abiotic stress on crops
13.4 Physiological changes
13.5 Biochemical and molecular changes
13.6 Artificial intelligence as a tool to improve the resilience of crop production
13.7 Databases of artificial intelligence involved in crop production
13.8 Drones-dependent agricultural practices: agricultural drones
13.9 Application of drones in the agriculture sector
13.10 Application of big data and Internet of Things in agriculture
13.10.1 IOT_ sensing layer
13.10.2 IOT_ network layer
13.10.3 IOT_application layer
13.11 Robotics in farm management
13.12 Applications of robotics in agriculture
13.12.1 Robotics in planting
13.12.2 Robotics in spraying
13.12.3 Robotics in harvesting
13.12.4 Robotics in self-driving
(autonomous tractors)
13.12.5 Artificial neural networks in accessing meteorological changes
13.13 Rainfall prediction
13.14 Evaluation of crop evapotranspiration
13.15 Estimation of air precipitation
13.16 Estimation of dew point temperature
13.17 Conclusion and future prospects of artificial intelligence in crop management
References
Chapter 14 Application of artificial intelligence in environmental sustainability and climate change
14.1 Introduction
14.2 Artificial intelligence
14.2.1 Components of AI
14.2.2 Machine learning and neural networks
14.3 SDGs and AI
14.4 Application of AI in environment sustainability
14.4.1 SDG 13-climate action
14.4.2 SDG 15 life on land
14.4.3 SDG 14 life below water
14.5 Challenges of AI
14.6 Conclusion
References
Chapter 15 Machine learning approach for climate change impact assessment in agricultural production
15.1 Introduction
15.2 Crop yield and climate change
15.3 Crop response or adaptation to increased climatic stress
15.4 Modeling approaches to monitor climate change impacts
15.5 Application of remote sensing
(RS) and geographic information system (GIS)
15.6 Machine learning techniques
15.7 Application of various machine learning techniques in agriculture
15.7.1 Artificial neural network
(ANN)
15.7.2 Decision tree modeling
15.7.3 Regression analysis
15.7.4 Fuzzy network
15.7.5 Principal component analysis
15.7.6 Cluster analysis
15.7.7 Markov chain modeling
15.7.8 Time series analysis
15.8 Conclusion
Conflict of Interest
References
Chapter 16 Climate change: Prediction of solar radiation using advanced machine learning techniques
16.1 Introduction
16.2 Literature review
16.3 Approach
16.3.1 Data exploration
16.3.2 Time series modeling
16.4 Results and discussions
16.5 Conclusion
16.6 Discussions: climate change and solar radiation prediction
References
Chapter 17 Concept of climate smart villages using artificial intelligence/machine learning
17.1 Introduction
17.2 The CSV procedure being segmented into several steps
17.3 Climate smart villages approach divided into different steps for decision support to farmers
17.4 Climate change resilience for the sustainable development of villages
17.5 Current projects of climate-smart village around the world
17.6 CSV approach in South Asia
17.7 Application of artificial intelligence and machine learning in the development of resilience in agriculture
17.8 Artificial neural networks
(ANN) in agriculture
17.9 Climate-smart village with the use of mobile apps to provide crop-specific and weather services to farmers
17.10 Drone technologies adaptation for sustainable agriculture
17.11 Agromet advisory services in India for climate smart agriculture
17.11.1 Components of agromet advisory bulletin
17.12 GKMS present and future work
17.13 Conclusion
References
Chapter 18 Significance of artificial intelligence to develop mitigation strategies against climate change in accordance with sustainable development goal \(climate action\)
18.1 Introduction
18.2 Factors affecting the climate change
18.2.1 Sun
18.2.2 Greenhouse gases
18.2.3 Volcanic activity
18.2.4 Variation in the Earth's path and spinning
18.2.5 Particles and aerosols
18.3 Consequences of climate change
18.4 Mitigating measures for adapting to climate change
18.5 Advantages of using artificial intelligence to develop mitigation strategies against climate change
18.5.1 Information collection and monitoring
18.5.2 Forecasting
18.5.3 Advancement in research and experimentation
18.6 Artificial intelligence-centered approach for climate change mitigation
18.6.1 Greenhouse gases emissions
18.6.2 Farms and forestry
18.6.3 Climate models
18.6.4 Cities and buildings
18.7 Conclusions
References
Chapter 19 A cross-sectional study about the impacts of climate change on living organisms: A case study of Odisha province of India
19.1 Introduction
19.2 What are the problems and who is responsible for climate change?
19.3 Impact of climate change in Odisha
19.4 Climate change and its impact on Fauna
19.5 Climate change and its impact on flora
19.6 Climate change and its impact on human societies
19.7 Conclusion
References
Chapter 20 Development of mitigation strategies for the climate change using artificial intelligence to attain sustainability
20.1 Introduction
20.2 The artificial intelligence and sustainable development goals
20.3 The application of AI
20.4 Artificial intelligence and its applications to cope with climate change
20.4.1 Climate intelligence
20.4.2 Carbon offsets
20.4.3 Carbon footprint
20.4.4 Buildings
20.4.5 Precision agriculture
20.4.6 Renewable and the grid
20.4.7 Fires
20.4.8 Weather forecasting
20.4.9 Accelerating scientific experimentation
20.5 AI and remote sensing
20.6 Role of artificial intelligence in environmental management
20.6.1 Artificial intelligence and ecosystem services
20.6.2 Artificial intelligence and air pollution
20.6.3 Artificial intelligence and water pollution
20.6.4 Artificial intelligence and waste management
20.6.5 Artificial intelligence in agriculture, horticulture, and forestry
20.7 Mitigation and adaptation strategies through use of artificial intelligence
20.8 Conclusion and future perspectives
References
Chapter 21 Role of artificial intelligence in environmental sustainability
21.1 Introduction
21.2 The impact of climate change on the environmental resources
21.2.1 Influence of climate change on the food sustainability
21.2.2 Influence of climate change on biodiversity and security
21.2.3 Impacts related to human lifestyle
21.3 Sustainability-based development
21.4 Application of artificial intelligence
(AI) for achieving sustainability
21.4.1 Modeling-based guidance policy and future prediction
21.4.2 Addressing environmental problems
21.4.3 Preserving ecosystem
21.4.4 Utilization of artificial intelligence for development
21.4.5 Artificial intelligence in health care systems
21.4.6 Artificial intelligence for renewable energy
21.5 Artificial intelligence challenges
21.6 Conclusions and recommendations
References
Index
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