Kooperativer Bibliotheksverbund

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Format: 1 online resource (552 pages)

Edition: 1st ed.

ISBN: 9780443132926 , 0443132925

Content: This book explores the application of intelligent systems to Earth, environmental, and planetary sciences. Edited by Hossein Bonakdari and Silvio José Gumiere, it delves into methodologies and models essential for analyzing environmental data, including topics such as partial least squares regression, solute dynamics in soils, and the use of AI in soil erosion modeling. The book is structured to provide practical insights into the use of machine learning techniques for environmental management, including drought forecasting and climate change model downscaling. It serves as a comprehensive resource for researchers and practitioners involved in environmental science and engineering, focusing on innovative approaches to data analysis and sustainable management practices.

Note: Intro -- Intelligence Systems for Earth, Environmental and Planetary Sciences -- Copyright -- Dedication -- Contents -- Contributors -- Acknowledgments -- Chapter 1: Partial least squares regression to explore and predict environmental data -- 1. Introduction -- 1.1. The basis of PLS: Understanding the fundamentals -- 1.2. Examples of PLSR application in environmental science: Demonstrating its relevance and effectiveness -- 1.3. State-of-the-art algorithms for improved PLS analysis: Enhancing accuracy and efficiency -- 1.3.1. NIPALS for principal component analysis -- 1.3.2. NIPALS for PLS -- 1.3.3. Variants of NIPALS -- 1.3.4. Selecting the number of latent variables to include -- 2. PLSR case study: A step-by-step illustration of applying PLSR to environmental data using R -- 2.1. Step 1: Install and load necessary packages -- 2.2. Step 2: Load the dataset -- 2.3. Step 3: Join and select the data in the dataset -- 2.4. Step 4: Preprocess the data -- 2.5. Step 5: Split the data into training and testing datasets -- 2.6. Step 6: Train the PLS model -- 2.7. Step 7: Make predictions on the test dataset -- 2.8. Step 8: Evaluate the model performance -- 2.9. Step 9: Visualize the results -- 3. Key practical considerations for PLS applications -- 3.1. Data quality -- 3.2. Variables selection -- 3.3. Assessment of variable importance -- 3.4. Retrospective analysis of PLSR performance for the case study -- 4. Future perspectives with PLSR -- Acknowledgment -- References -- Chapter 2: Study of solute dynamics in unsaturated sandy soil under controlled irrigation -- 1. Introduction -- 2. Materials and method -- 2.1. Data -- 2.2. The experiment -- 2.3. Climate scenarios -- 2.4. Convective lognormal transfer function -- 2.4.1. Estimation of the parameters of the model -- 2.5. Statistical method -- 2.6. Hydrus 2D -- 2.6.1. Flow and transport equations. , 2.6.2. Model parameters -- 2.6.3. Initial and boundary conditions of the model -- 2.7. Evaluation of models performance -- 3. Results and discussion -- 3.1. Measured data -- 3.2. Convective lognormal transfer function -- 3.2.1. Parameters estimated by the model and elution curves -- 3.3. Hydrus 2D -- 3.3.1. Calibration of soil hydrodynamic parameters -- 3.3.2. Simulation of bromide concentration by the Hydrus-2D model for the different climate scenarios -- 4. Conclusion -- Appendix -- References -- Chapter 3: Prediction of hydraulic heights water table for irrigation management in cranberry fields with Random Forest: ... -- 1. Introduction -- 2. Methodology -- 2.1. Study area -- 2.2. Random Forest model -- 2.3. Data -- 2.3.1. Model development -- Data splitting -- Training and test subsets -- Input selection -- Autoregressive values of the training dataset -- Autoregressive values of the test dataset -- 2.3.2. Model calibration and validation -- 2.4. Importance of input variables -- 2.5. Performance criteria -- 3. Results and discussion -- 3.1. Simulation of water table heights -- 3.2. Predictive variables relative importance -- 3.3. Forecasting water table heights -- 4. Conclusion -- References -- Chapter 4: Spatial intelligence in AI applications for assessing soil health to monitor farming systems and associated ES ... -- 1. Introduction -- 2. Data and methodology -- 2.1. Study area -- 2.2. Description of the dataset -- 2.3. Soil erosion hazard inventory preparation -- 2.4. Factors causing soil erosion hazards -- 2.4.1. Soil erodibility -- 2.4.2. Slope length and steepness -- 2.4.3. Land-use and land-cover -- 2.4.4. Rainfall erosivity -- 2.4.5. Aspect -- 2.4.6. Distance to stream -- 2.4.7. Stream power index -- 2.5. Climate variables and CMIP6 model simulation -- 2.6. Delineation of farming systems. , 2.7. AI tools for soil erosion susceptibility modeling -- 2.7.1. Artificial neural network -- 2.7.2. Adaptive neuro-fuzzy inference system -- 2.7.3. Support vector machine -- 2.7.4. Random forest -- 2.7.5. Combined long short-term memory and revised universal soil loss equation model -- 2.8. Performance analysis of the models -- 3. Results -- 3.1. Land-use and land-cover map and farming system delineation -- 3.2. Soil erosion factor map generation -- 3.3. Rainfall erosivity with climate change scenarios -- 3.4. Soil erosion susceptibility with AI tools -- 3.5. Selection of important variables for soil erosion hazards -- 3.6. Model validation for susceptibility maps -- 4. Discussion -- Conclusion -- Funding -- Author contributions -- Declaration of competing interest -- References -- Chapter 5: Drought forecasting based on machine learning techniques -- 1. Introduction -- 2. Background -- 2.1. Drought -- 2.2. Difference between drought and aridity -- 3. Frequency, severity, and areal extend of drought -- 3.1. Frequency of drought -- 3.2. Severity of drought -- 3.3. Areal extent of drought -- 4. Return period and indexes of drought -- 4.1. Return period -- 4.2. Different form of drought and their indexes -- 4.3. Indices of drought -- 4.3.1. SPI -- 4.3.2. SPEI -- 4.3.3. PDSI -- 4.3.4. SDI -- 4.3.5. SMAI -- 4.3.6. SMI -- 4.3.7. SWSDI -- 4.3.8. MSDI -- 4.4. Using AI methods for drought forecasting -- 4.4.1. Extract dry years -- 4.4.2. Using AI for drought forecasting -- 5. Conclusion -- References -- Chapter 6: How to use artificial intelligence to downscale climate change models data -- 1. Introduction -- 2. Background -- 2.1. Global circulation models -- 2.2. Climate sensitivity balance -- 3. The coupled model intercomparison project -- 3.1. Introduction to CMIP -- 3.2. Goals of CMIP -- 3.3. CMIP5 series -- 3.4. CMIP6 series. , 3.5. Representative concentration pathways and shared socioeconomic pathways -- 3.5.1. Representative concentration pathways -- 3.5.2. Shared socioeconomic pathways -- 3.6. Difference between CMIP5 & -- CMIP6 -- 3.6.1. Socioeconomic scenarios -- 3.6.2. Climate model improvements -- 3.6.3. Earth system components -- 3.6.4. Aerosol and chemistry representation -- 3.6.5. Data accessibility and intercomparison -- 3.6.6. Increased international collaboration -- 3.7. Downscaling -- 3.8. Statistical downscaling -- 3.9. Dynamical downscaling -- 3.10. Bias correction -- 3.11. Weather generator -- 3.12. Ensemble techniques -- 4. How to use -- 4.1. Data preparation -- 4.2. Model training -- 4.3. Model evaluation and validation -- 4.4. Downscaling process -- 4.5. Uncertainty estimation -- 4.5.1. Ensemble modeling -- 4.5.2. Bootstrap resampling -- 4.6. Model refinement and iteration -- 5. Conclusion -- References -- Chapter 7: Advanced methods of soil quality assessment for sustainable agriculture -- Abbreviations -- 1. Introduction -- 2. The concepts and applications of soil quality -- 3. Soil quality datasets -- 3.1. Soil quality indicators -- 3.1.1. Physical indicators -- 3.1.2. Chemical indicators -- 3.1.3. Biological indicators -- 3.2. Minimum dataset -- 4. Soil quality assessment methods -- 4.1. Farmer-based assessment of soil quality scorecards and soil quality test kits -- 4.2. Visual assessment -- 4.3. Soil quality indices -- 4.4. Advanced methods for soil quality assessment -- 4.4.1. Geospatial approach -- 4.4.2. Soil quality modeling -- 4.4.3. Digital soil mapping and machine learning -- 5. Radio isotope technique to assess soil quality -- 5.1. Fallout radionuclides-137Cs as a soil quality indicator -- 5.2. 137Cs to study erosion-induced soil carbon dynamics -- 6. Conclusion -- Acknowledgments -- References. , Chapter 8: Combining the RUSLE approach and GIS tools in soil water erosion monitoring and mapping (Northeastern Algeria) -- 1. Introduction -- 2. Location and geological setting -- 3. Materials and methods -- 3.1. Data source -- 3.2. Method -- 4. Results and discussion -- 4.1. Rainfall erosivity factor (R) -- 4.2. Soil erodibility (K) -- 4.3. Topographic factor (LS) -- 4.4. Land-use factor-plant cover (C) -- 4.5. The anti-erosion practical factor (P) -- 5. Conclusions -- Acknowledgments -- References -- Chapter 9: Leveraging the use of mechanistic and machine learning models to assess interactions between ammonia concentra ... -- 1. Introduction -- 2. Materials and methods -- 2.1. Data collection and experimental setup -- 2.2. Modeling frameworks -- 2.2.1. Mechanistic models -- Equilibrium gas phase NH3 concentration in laying-hen manure -- Carbon-dioxide accelerated ammonia release model -- Mechanistic NH3 emission model -- 2.2.2. Data-driven model: Random forest -- 2.3. Model performance metrics -- 2.4. Importance features -- 3. Results -- 3.1. Laying-hen manure characteristics -- 3.2. Hygrothermal conditions -- 3.3. CO2, CH4, and H2O gas concentrations -- 3.4. Observed NH3-N concentrations -- 3.5. Simulation of NH3 concentrations -- 3.5.1. Mechanistic NH3 emission model -- 3.5.2. Random forest -- Optimal model parameters -- NH3-N predictions -- 3.6. Importance features -- 4. Discussion -- 4.1. Properties of LHM samples -- 4.2. Estimation of NH3-N concentrations -- 4.3. Interactions between manure characteristics, atmospheric conditions, and simulated NH3 concentrations -- 5. Conclusions -- Appendix A: Carbon-dioxide accelerated ammonia release model -- A.1. Diffusion mass transfer module -- A.2. Chemistry of the ammonia at the manure surface -- A.3. Convection mass transfer module -- A.4. State space model. , Appendix B: Mechanistic NH3 emission model.

Additional Edition: ISBN 9780443132933

Additional Edition: ISBN 0443132933

Language: English