Book 2014

Computational Intelligence Techniques in Earth and Environmental Sciences

ISBN: 978-94-017-8641-6 (Print) 978-94-017-8642-3 (Online)

Table of contents (13 chapters)

  1. Front Matter

    Pages i-xv

  2. General

    1. Front Matter

      Pages 1-1

    2. Chapter

      Pages 3-26

      Computational Intelligence Techniques and Applications

  3. Classical Intelligence Techniques in Earth and Environmental Sciences

    1. Front Matter

      Pages 27-27

    2. Chapter

      Pages 29-51

      Vector Autoregression (VAR) Modeling and Forecasting of Temperature, Humidity, and Cloud Coverage

    3. Chapter

      Pages 53-78

      Exploring the Behavior and Changing Trends of Rainfall and Temperature Using Statistical Computing Techniques

    4. Chapter

      Pages 79-91

      Time Series Model Building and Forecasting on Maximum Temperature Data

    5. Chapter

      Pages 93-105

      GIS Visualization of Climate Change and Prediction of Human Responses

  4. Probabilistic and Transforms Intelligence Techniques in Earth and Environmental Sciences

    1. Front Matter

      Pages 107-107

    2. Chapter

      Pages 109-128

      Markov Chain Analysis of Weekly Rainfall Data for Predicting Agricultural Drought

    3. Chapter

      Pages 129-140

      Forecasting Tropical Cyclones in Bangladesh: A Markov Renewal Approach

    4. Chapter

      Pages 141-154

      Performance of Wavelet Transform on Models in Forecasting Climatic Variables

    5. Chapter

      Pages 155-171

      Analysis of Inter-Annual Climate Variability Using Discrete Wavelet Transform

  5. Hybrid Intelligence Techniques in Earth and Environmental Sciences

    1. Front Matter

      Pages 173-173

    2. Chapter

      Pages 175-196

      Modeling of Suspended Sediment Concentration Carried in Natural Streams Using Fuzzy Genetic Approach

    3. Chapter

      Pages 197-208

      Prediction of Local Scour Depth Downstream of Bed Sills Using Soft Computing Models

    4. Chapter

      Pages 209-241

      Evaluation of Wavelet-Based De-noising Approach in Hydrological Models Linked to Artificial Neural Networks

    5. Chapter

      Pages 243-264

      Evaluation of Mathematical Models with Utility Index: A Case Study from Hydrology

  6. Back Matter

    Pages 265-266