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Advanced Machine Learning Techniques includes the theoretical foundations of modern machine learning, as well as advanced methods and frameworks used in modern machine learning. Handbook of HydroInformatics, Volume II: Advanced Machine Learning Techniques presents both the art of designing good learning algorithms, as well as the science of analyzing an algorithm's computational and statistical properties and performance guarantees. The global contributors cover theoretical foundational topics such as computational and statistical convergence rates, minimax estimation, and concentration of measure as well as advanced machine learning methods, such as nonparametric density estimation, nonparametric regression, and Bayesian estimation; additionally, advanced frameworks such as privacy, causality, and stochastic learning algorithms are also included. Lastly, the volume presents Cloud and Cluster Computing, Data Fusion Techniques, Empirical Orthogonal Functions and Teleconnection, Internet of Things, Kernel-Based Modeling, Large Eddy Simulation, Patter Recognition, Uncertainty-Based Resiliency Evaluation, and Volume-Based Inverse Mode. This is an interdisciplinary book, and the audience includes postgraduates and early-career researchers interested in: Computer Science, Mathematical Science, Applied Science, Earth and Geoscience, Geography, Civil Engineering, Engineering, Water Science, Atmospheric Science, Social Science, Environment Science, Natural Resources, Chemical Engineering.
Intro -- Handbook of HydroInformatics: Volume II: Advanced Machine Learning Techniques -- Copyright -- To Late George Edward Pelham Box (British Statistician: 1919-2013) -- Contents -- Contributors -- About the Editors -- Preface -- Chapter 1: Analyzing spatiotemporal variation of land use and land cover data -- 1. Introduction -- 2. Data preparation -- 3. Visual interpretations -- 4. LULC distribution -- 5. LULC change detection -- 6. Image interpretation -- 7. LAI model -- 8. Compare the visual interpretation vs image interpretation -- 9. Conclusions -- References -- Chapter 2: Artificial Intelligence-based model fusion approach in hydroclimatic studies -- 1. Introduction -- 2. Mathematical concepts -- 2.1. Ensemble techniques -- 2.1.1. Bagging -- 2.1.2. Boosting -- 2.1.3. Random forest -- 2.1.4. Extremely randomized trees -- 2.1.5. Extreme gradient boosting -- 2.1.6. Simple linear averaging method -- 2.1.7. Linear weighted averaging method -- 2.1.8. Bayesian model averaging method -- 2.1.9. Stacking (nonlinear ensemble method) -- 2.2. Hybrid techniques -- 2.2.1. Wavelet-AI methods -- 2.2.2. ARIMA-AI methods -- 2.2.3. Clustering-based AI methods -- 2.2.4. Evolutionary-based AI methods -- 3. Some applications -- 3.1. Ensemble techniques -- 3.2. Hybrid techniques -- 4. Conclusions -- References -- Chapter 3: Computations of probable maximum precipitation estimates -- 1. Introduction -- 1.1. Background and importance of PMP estimations -- 2. Methodology of PMP estimation -- 2.1. Physical method -- 2.2. Statistical method -- 2.3. Multifractal approach -- 3. Statistical PMP estimates: A case-study -- 3.1. Hershfield PMP estimates in Malaysia -- 4. Conclusions -- References -- Chapter 4: Deep learning: Long short-term memory in hydrological time series -- 1. Introduction -- 2. Model description of long short-term memory (LSTM) -- 2.1. Neural network.
2.2. Recurrent neural network -- 2.3. LSTM -- 3. Training network and backpropagation -- 3.1. Feedforward and backward propagation -- 3.2. Gradient descent method -- 3.3. Backpropagation of RNN -- 3.4. Backpropagation through time of RNN -- 3.5. Backpropagation through time for LSTM -- 4. Variants of LSTM -- 4.1. Peephole LSTM -- 4.2. Gated recurrent unit -- 4.3. Multiplicative LSTM -- 4.4. Sequence-to-sequence (seq2seq) LSTM -- 4.5. Bidirectional LSTM -- 5. Normalization and hyperparameter selection -- 5.1. Normalization -- 5.2. Estimation of hyperparameters -- 6. LSTM applications in hydrometeorological variables -- 6.1. LSTM and its variants for prediction -- 6.2. Hybrid LSTM -- 6.3. Simulation modeling with LSTM -- 7. Employed deep learning programs for LSTM -- 7.1. Tensorflow and Keras with Python -- 7.2. Matlab -- 8. Conclusions -- References -- Chapter 5: Dimensionality reduction of correlated meteorological variables by Bayesian network-based graphical modeling -- 1. Introduction -- 2. Study area and data used -- 2.1. Study area -- 2.2. Data used -- 3. Methodology -- 4. Results and discussions -- 4.1. Directed acyclic graphs obtained from HC and MMHC algorithms -- 4.2. Utility of the conditional dependence structure -- 5. Conclusions -- References -- Chapter 6: The ecohydrological function of the tropical forest rainfall interception: Observation and modeling -- 1. Introduction -- 2. Canopy water balance: concepts and general aspects of the monitoring -- 2.1. Forest canopy water balance -- 2.1.1. Rainfall interception -- 2.1.2. Throughfall -- 2.1.3. Stemflow -- 2.2. Water balance in tropical forested watersheds -- 2.3. Soil moisture in forest areas -- 2.4. Geochemistry in tropical forests -- 3. Measurements of the rainfall interception components -- 3.1. Standard measurements -- 3.2. Ex situ methods for individual trees.
3.3. Forest parameters -- 4. Rainfall interception modeling -- 4.1. Conceptual models -- 4.2. Statistical and machine learning tools for ecohydrological data handling -- 5. Conclusions -- References -- Chapter 7: Emotional artificial neural network: A new ANN model in hydroinformatics -- 1. Introduction -- 2. Mathematical concepts of emotional artificial neural network -- 2.1. Feed forward neural network (FFNN) -- 2.2. Emotional artificial neural network (EANN) -- 2.3. Difference between FFNN and EANN -- 2.4. Data preprocessing and performance evaluation -- 2.5. Dominant inputs selection -- 3. Some applications of EANN -- 4. Conclusions -- References -- Chapter 8: Exploring nature-based adaptation solutions for urban ecohydrology: Definitions, concepts, institutional frame ... -- 1. Introduction -- 2. Nature-based adaptation solutions (NBaS): Conceptual framework and position -- 2.1. NBaS: Concepts and terminology -- 2.2. NBaS in the international, regional, and national frameworks -- 3. NbaS and ecohydrology -- 4. The need for physically-based evidence -- 4.1. Potential and limitations of green roofs -- 4.1.1. Potentials -- 4.1.2. Limitations -- 4.2. Green roofs: A means for hybridizing the gray -- 5. Conclusions -- 5.1. Adapt by learning and learning to adapt -- 5.2. Are NBaS the way forward? -- References -- Chapter 9: Fuzzy-based large-scale teleconnection modeling of monthly precipitation -- 1. Introduction -- 2. Materials and methods -- 2.1. Teleconnections impact on hydroclimatologic systems -- 2.2. Proposed methodology -- 2.3. Association rule -- 2.4. Fuzzy logic -- 2.5. Efficiency criteria -- 2.6. Study area and data -- 3. Results and discussion -- 4. Conclusions -- References -- Chapter 10: Hydrologic models classification, calibration, and validation -- 1. Introduction -- 2. Hydrological modeling for integrated water management.
2.1. Classification of hydrological models -- 2.2. Description of some common hydrological model types -- 2.2.1. Physical models -- 2.2.2. Deterministic models -- 2.2.3. Black box and statistical models -- 2.2.4. Conceptual models -- 2.2.5. Empirical models -- 2.3. Rainfall runoff transformation methods in physical models -- 2.4. Hydrologic model components -- 3. Model calibration and validation -- 3.1. Model parameterization -- 3.2. Model calibration -- 3.2.1. Calibration components -- 3.2.2. The objective function -- 3.2.3. Nash criteria -- 3.3. Optimization methods for model calibration -- 3.3.1. Manual calibration -- 3.3.2. Automatic calibration -- 3.3.3. Genetic algorithms -- 3.4. Model validation -- 3.5. Model uncertainties -- 3.5.1. Data uncertainty -- 3.5.2. Uncertainty due to parameters estimation -- 3.5.3. Model uncertainty -- 3.6. Regionalization methods in rainfall-runoff modeling (case of ungauged basins) -- 4. Conclusions -- References -- Further reading -- Chapter 11: Identification of soil erosion sites in semiarid zones: Using GIS, remote sensing, and PAP/RAC model -- 1. Introduction -- 2. Materials and method -- 2.1. Study area -- 2.2. PAP/RAC model application -- 2.2.1. The predictive phase -- 2.2.2. The descriptive phase -- 2.2.3. The integration phase -- 2.3. The accuracy test -- 3. Results and discussion -- 3.1. Predictive phase -- 3.1.1. The slope map -- 3.1.2. The lithology map -- 3.1.3. The erodibility map -- 3.1.4. Land use map -- 3.1.5. Vegetation density -- 3.1.6. The soil protection map -- 3.1.7. The predictive erosion map -- 3.2. Descriptive phase -- 3.3. Integration phase -- 3.4. Global diagnosis of the test accuracy assessment -- 4. Conclusions -- References -- Chapter 12: Metrics of the water performance engineering modeling -- 1. Introduction -- 2. Types of hydro-climatological modeling and metrics.
2.1. Point prediction -- 2.2. Prediction intervals of modeling -- 2.3. Binary classification -- 2.4. Input selection methods in modeling -- 2.5. Decision making models -- 2.6. Hydrographs in hydrological modeling -- 2.7. Flow-duration curves -- 2.8. Information theory -- 3. Some applications of the metrics -- 3.1. Application of the point prediction in hydro-climatological modeling -- 3.2. Application of the PIs in hydro-climatological modeling -- 3.3. Application of the binary classification in hydro-climatological modeling -- 3.4. Application of the input selection in hydro-climatological modeling -- 3.5. Application of the decision making in hydro-climatological modeling -- 3.6. Application of the hydrographs in hydro-climatological modeling -- 3.7. Application of the flow-duration curves in hydro-climatological modeling -- 3.8. Application of the information theory in hydro-climatological modeling -- 4. Agenda for future studies -- References -- Chapter 13: Outlier robust extreme learning machine: Predicting river water temperature in the absence of air temperature -- 1. Introduction -- 2. Study area and data -- 3. Materials and methods -- 3.1. Outlier robust extreme learning machine(ORELM) -- 3.2. Performance assessment of the models -- 4. Results and discussion -- 5. Conclusions -- References -- Chapter 14: Parametric and nonparametric methods for analyzing the trend of extreme events -- 1. Introduction -- 2. Trend calculation methods -- 2.1. Mann-Kendall test (MK) -- 2.2. Mann-Kendall test with trend-free prewhitening (MK2) -- 2.3. Modified Mann-Kendall tests (MK3) -- 2.4. Mann-Kendall test considering LTP (MK4) -- 2.4.1. Calculation of Hurst coefficient (H) -- 2.4.2. Significance level of H -- 2.4.3. Calculation of variance -- 2.5. Spearmans rho test -- 2.6. Linear regression method -- 2.7. Quantile regression method.
edited by Saeid Eslamian, Faezeh Eslamian.