Note: Intro -- Preface -- Contents -- Contributors -- About the Editors -- Part I: General -- Chapter 1: Computational Intelligence Techniques and Applications -- 1.1 Introduction -- 1.2 Neural Networks -- 1.2.1 Basic Principles -- 1.2.2 Applications -- 1.3 Evolutionary Computation -- 1.3.1 Basic Principles -- 1.3.2 Applications -- 1.4 Swarm Intelligence -- 1.4.1 Basic Principles -- 1.4.2 Applications -- 1.5 Artificial Immune Systems -- 1.5.1 Basic Principles -- 1.5.2 Applications -- 1.6 Fuzzy Systems -- 1.6.1 Basic Principles -- 1.6.2 Applications -- 1.7 Conclusions -- References -- Part II: Classical Intelligence Techniques in Earth and Environmental Sciences -- Chapter 2: Vector Autoregression (VAR) Modeling and Forecasting of Temperature, Humidity, and Cloud Coverage -- 2.1 Introduction -- 2.2 Materials and Methods -- 2.2.1 Data -- 2.2.2 Test of Stationarity -- 2.2.2.1 Augmented Dickey-Fuller Test -- 2.2.2.2 Phillips-Perron Test -- 2.2.2.3 Kwiatkowski-Phillips-Schmidt-Shin Test -- 2.2.3 Vector Autoregression Model -- 2.2.3.1 Selection of Variables Under Study -- 2.2.3.2 Making a Model of Order p (Arbitrary) -- 2.2.3.3 Determining the Value of Order p -- 2.2.3.4 Estimating Parameters -- 2.2.3.5 Diagnostic Checking -- 2.2.3.6 Cross Validity of the Fitted VAR Models -- 2.2.3.7 Forecasting -- 2.2.3.8 Forecast Error Variance Decomposition -- 2.2.3.9 Impulse Response Function -- 2.3 Results and Discussion -- 2.3.1 Descriptive Statistics -- 2.3.2 Tests for Stationarity -- 2.3.3 Selection of Variables Under Study -- 2.3.4 Selection of Order (p) -- 2.3.5 Estimation of Parameters -- 2.3.6 Diagnostic Checking -- 2.3.7 Cross Validity of the Fitted VAR Models -- 2.3.8 Forecast Error Variance Decomposition -- 2.3.9 Impulse Response Function -- 2.3.10 Forecasting Using VAR(8) Model -- 2.4 Conclusion -- References. , Chapter 3: Exploring the Behavior and Changing Trends of Rainfall and Temperature Using Statistical Computing Techniques -- 3.1 Introduction -- 3.2 Materials and Methods -- 3.2.1 K-Means Clustering -- 3.2.2 Mann-Kendall Trend Test -- 3.2.3 Seasonal Mann-Kendall Trend Test -- 3.2.4 Sen´s Slope Estimator -- 3.2.5 Naive Model -- 3.2.6 Autoregressive Integrated Moving Average Model -- 3.2.7 Random Walk with Drift -- 3.2.8 Theta Model -- 3.2.9 Wilcoxon Test -- 3.2.10 RMS Error -- 3.2.11 SMAPE -- 3.2.12 Materials Used During the Study -- 3.2.13 Data Used in the Study -- 3.3 Results and Discussion -- 3.3.1 Change in Temperature -- 3.3.2 Changes in Monthly Total Rainfall -- 3.3.3 Clustering of Rainfall -- 3.3.4 Comparison of Different Time Series Forecasting Models -- 3.4 Concluding Remarks -- References -- Chapter 4: Time Series Model Building and Forecasting on Maximum Temperature Data -- 4.1 Introduction -- 4.2 Materials and Methods -- 4.2.1 Time Series Data on Temperature -- 4.2.2 Testing Stationarity of the Time Series -- 4.2.3 Box-Jenkins Modeling Strategy -- 4.2.3.1 Identification of Order for the SARIMA Structure -- 4.2.3.2 Parameter Estimation of the SARIMA Model -- 4.2.3.3 Diagnostic Checking of the Fitted Model -- 4.2.3.4 Forecasting of the Study Variable -- 4.3 Results and Discussion -- 4.3.1 Testing Stationarity Status of Temperature -- 4.3.2 Model Building and Forecasting -- 4.3.2.1 Identification of Parameters Value of the SARIMA Structure -- 4.3.2.2 Estimation of Parameters for Selected SARMA Models -- 4.3.2.3 Diagnostic Checking for Estimated SARMA Models -- 4.3.2.4 Forecasting of Temperature Using Selected SARMA Models -- 4.4 Conclusion -- References -- Chapter 5: GIS Visualization of Climate Change and Prediction of Human Responses -- 5.1 Introduction -- 5.2 Method and Materials. , 5.3 Wet Bulb (Twb) and Globe (Tg) Temperature as Physical Model to Predict WBGT -- 5.4 WBGT and Tolerance Time a Tool for Climate Change Assessment -- 5.4.1 Tolerance Time -- 5.5 Discussion -- 5.6 Conclusion -- References -- Part III: Probabilistic and Transforms Intelligence Techniques in Earth and Environmental Sciences -- Chapter 6: Markov Chain Analysis of Weekly Rainfall Data for Predicting Agricultural Drought -- 6.1 Introduction -- 6.2 Study Area and Method -- 6.2.1 Climatic Condition of the Barind Region -- 6.2.2 Markov Chain Agricultural Drought Index -- 6.2.3 Test of Null Hypothesis -- 6.2.4 Mapping the Spatial Extent of Agricultural Drought -- 6.3 Results and Discussion -- 6.3.1 Temporal and Spatial Characteristics of Agricultural Drought -- 6.3.2 Probability of Wet Spell -- 6.3.3 Relative Frequency of Occurrence of Agricultural Drought -- 6.3.4 Result of Hypothesis Testing -- 6.4 Conclusions -- References -- Chapter 7: Forecasting Tropical Cyclones in Bangladesh: A Markov Renewal Approach -- 7.1 Introduction -- 7.2 Data Sources and Description -- 7.3 Methods -- 7.3.1 Markov Renewal Process and Its Properties -- 7.3.2 Likelihood Construction and Parameter Estimation -- 7.3.3 Cross-State Prediction -- 7.3.4 Asymptotic Behavior: Mean Recurrence Time -- 7.4 Results and Discussion -- 7.5 Conclusion -- References -- Chapter 8: Performance of Wavelet Transform on Models in Forecasting Climatic Variables -- 8.1 Introduction -- 8.2 Wavelet Transformation -- 8.3 Data Processing and Forecasting Framework -- 8.3.1 Approach-1 -- 8.3.2 Approach-2 -- 8.4 Comparison of Forecasting Performance -- 8.5 Empirical Results -- 8.5.1 Forecasting Based on Original Series -- 8.5.2 Forecasting Based on Decomposed Series Using Wavelet Transformation -- 8.5.2.1 Approach-1 -- 8.5.2.2 Approach-2 -- 8.5.3 Comparison -- 8.6 Conclusions -- References. , Chapter 9: Analysis of Inter-Annual Climate Variability Using Discrete Wavelet Transform -- 9.1 Introduction -- 9.2 Multiband Decomposition of Climate Signals -- 9.2.1 Fourier-Based Filter Bank and Its Limitations -- 9.2.2 Wavelet-Based Filter Bank -- 9.3 Annual Cycle Extraction -- 9.4 Results and Discussion -- 9.5 Conclusions -- References -- Part IV: Hybrid Intelligence Techniques in Earth and Environmental Sciences -- Chapter 10: Modeling of Suspended Sediment Concentration Carried in Natural Streams Using Fuzzy Genetic Approach -- 10.1 Introduction -- 10.2 Methods -- 10.2.1 Fuzzy Logic -- 10.2.2 Genetic Algorithm -- 10.2.3 Fuzzy Genetic Approach -- 10.2.4 Adaptive Network-Based Fuzzy Inference System -- 10.2.5 Neural Networks -- 10.2.6 Sediment Rating Curve -- 10.3 Applications and Results -- 10.4 Conclusion -- References -- Chapter 11: Prediction of Local Scour Depth Downstream of Bed Sills Using Soft Computing Models -- 11.1 Introduction -- 11.2 Material and Methods -- 11.2.1 Physical Definition of Scouring -- 11.2.2 Scouring Prediction at Bed Sills -- 11.2.2.1 Empirical Equations -- 11.2.2.2 Genetic Algorithm -- 11.2.2.3 Gene Expression Programming -- 11.2.2.4 M5 Tree Model -- 11.2.2.5 Data Set -- 11.2.2.6 Selection of Input and Output Parameters -- 11.2.3 Experimental Setup -- 11.3 Results -- 11.3.1 GA Model -- 11.3.2 GEP Model -- 11.3.3 M5 Tree Equations -- 11.4 Performance Analysis of Results -- 11.5 Conclusions -- References -- Chapter 12: Evaluation of Wavelet-Based De-noising Approach in Hydrological Models Linked to Artificial Neural Networks -- 12.1 Introduction -- 12.2 Study Area and Data -- 12.3 Materials and Methods -- 12.3.1 Wavelet De-noising Procedure -- 12.3.2 Artificial Neural Network and Efficiency Criteria -- 12.4 Results and Discussion -- 12.4.1 Ad Hoc ANN -- 12.4.1.1 Stream-Flow Forecasting -- 12.4.1.2 SSL Forecasting. , 12.4.2 Hybrid ANN-Wavelet -- 12.4.2.1 Wavelet-Based De-noising Approach -- 12.4.2.2 Stream-Flow Forecasting -- 12.4.2.3 SSL Forecasting -- 12.4.3 Comparison of Models -- 12.5 Concluding Remarks -- References -- Chapter 13: Evaluation of Mathematical Models with Utility Index: A Case Study from Hydrology -- 13.1 Introduction -- 13.2 Study Area and Data Used -- 13.3 Models -- 13.3.1 LLR Model -- 13.3.2 ANN and ANFIS Models -- 13.3.3 Support Vector Machines -- 13.3.4 Wavelet Hybrid Models -- 13.3.5 Index of Model Utility (U) -- 13.4 Results and Discussions -- 13.4.1 Comparison of Data Models Using Utility Index -- 13.4.2 Comparison of Data Models Using Statistical Indices -- 13.5 Conclusions -- References -- Index.