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xvii | |
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Chapter 1 An introduction to Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering |
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1 | (10) |
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1 | (1) |
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Book structure and relevant audience |
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2 | (1) |
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Intelligent systems in environmental engineering research |
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3 | (1) |
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4 | (1) |
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4 | (7) |
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SECTION 1 Data-centric and intelligent systems in air quality monitoring, assessment and mitigation |
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Chapter 2 Application of deep learning and machine learning in air quality modeling |
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11 | (14) |
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11 | (1) |
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12 | (2) |
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12 | (1) |
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Air quality data and indices |
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13 | (1) |
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14 | (5) |
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Data integration and data preprocessing |
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15 | (2) |
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Machine learning and deep learning algorithms |
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17 | (1) |
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18 | (1) |
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Conclusions and further thoughts |
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19 | (1) |
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20 | (1) |
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20 | (5) |
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Chapter 3 Advances in data-centric intelligent systems for air quality monitoring, assessment, and control |
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25 | (34) |
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25 | (1) |
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Overview of Al-based technologies and data-centric systems for pollution control |
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26 | (22) |
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26 | (2) |
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Data-centric system design principles |
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28 | (1) |
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Data-centric/decision support systems |
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28 | (2) |
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Data interpretation and mining methods |
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30 | (1) |
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Fundamental principles of data mining with AI |
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31 | (1) |
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Machine learning and AI models |
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32 | (6) |
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AI methods for air quality monitoring |
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38 | (4) |
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Review of a few previous and more recent studies on air quality modeling |
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42 | (5) |
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Future opportunities: the next data wave |
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47 | (1) |
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48 | (1) |
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49 | (10) |
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Chapter 4 Intelligent systems in air pollution research: a review |
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59 | (24) |
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Yousef Golizadeh Akhlaghi |
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59 | (1) |
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The definition of atmosphere |
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60 | (1) |
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The structure of atmosphere |
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60 | (2) |
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Different contaminants in the air |
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62 | (1) |
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62 | (1) |
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62 | (2) |
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64 | (1) |
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Carbon monoxide and carbon dioxide |
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65 | (1) |
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66 | (1) |
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66 | (3) |
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69 | (1) |
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70 | (1) |
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The employed machine learning method |
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70 | (1) |
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70 | (1) |
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Input and output parameters |
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71 | (1) |
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71 | (1) |
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72 | (1) |
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The specifications and validation of the developed models |
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72 | (1) |
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Uncertainty of different models |
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73 | (1) |
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Analyzing error for smaller ranges of the input parameters |
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74 | (5) |
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79 | (1) |
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79 | (4) |
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Chapter 5 ESTABLISH---a decision support system for monitoring the quality of air for human health |
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83 | (22) |
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83 | (1) |
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84 | (1) |
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ESTABLISH pilot study: user requirements |
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85 | (5) |
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ESTABLISH decision support system |
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90 | (1) |
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90 | (1) |
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Deployment, data acquisition, and integration |
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91 | (1) |
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Preliminary testing of sensors |
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91 | (3) |
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Scenario A Different type of equipment |
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93 | (1) |
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Scenario B Same type of equipment |
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94 | (1) |
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Data acquisition and integration from wearable devices |
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94 | (3) |
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Data acquisition and integration from environmental monitoring devices |
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97 | (1) |
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Visualization of the air quality index |
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97 | (1) |
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ESTABLISH platform presentation |
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98 | (1) |
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99 | (1) |
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User guide for therapists |
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100 | (1) |
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101 | (1) |
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102 | (1) |
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102 | (3) |
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Chapter 6 Indoor air pollution: a comprehensive review on public health challenges and prevention policies |
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105 | (24) |
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105 | (2) |
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Indoor air quality and public health |
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107 | (2) |
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107 | (1) |
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Cardiovascular dysfunctions |
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107 | (1) |
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Neuropsychiatric complications |
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108 | (1) |
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Chronic pulmonary disease |
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108 | (1) |
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108 | (1) |
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Low birth weight and infant mortality |
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108 | (1) |
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109 | (1) |
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109 | (1) |
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Enhanced indoor air quality and prevention strategies |
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109 | (3) |
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Technologies and control policies for enhanced indoor air quality |
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112 | (2) |
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Indoor air quality management technologies |
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112 | (1) |
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Control policies for indoor air quality |
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113 | (1) |
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114 | (3) |
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117 | (2) |
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119 | (10) |
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SECTION 2 Data-centric and intelligent systems in water quality monitoring, assessment and mitigation |
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Chapter 7 Data-centric intelligent systems for water quality monitoring, assessment and control |
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129 | (32) |
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129 | (2) |
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Problems associated with numerical modeling in hydraulic transport and water quality prediction |
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131 | (1) |
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Why artificial intelligence? |
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131 | (18) |
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AI methods and machine learning methods for water quality modeling and contaminant hydrology |
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132 | (14) |
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Recent advances in water quality modeling |
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146 | (3) |
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149 | (1) |
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150 | (9) |
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159 | (2) |
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Chapter 8 ANN prognostication and GA optimization of municipal solid waste leachate treatment using aluminum electrodes via electrocoagulation-flocculation method |
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161 | (24) |
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Okechukwu Dominic Onukwuli |
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Chukwuemeka Daniel Ezeliora |
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161 | (3) |
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164 | (2) |
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Batch electrocoagulation experiments |
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164 | (2) |
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Artificial neural network modeling |
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166 | (3) |
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Genetic algorithm optimization of the ECF process |
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169 | (1) |
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Statistical analysis on the data |
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169 | (1) |
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Calculation of electrode and electrical consumption |
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170 | (1) |
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170 | (3) |
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170 | (3) |
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Genetic algorithm optimization results |
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173 | (2) |
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Statistical analysis results |
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175 | (2) |
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Electrode and electrical power consumption during the ECF process |
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177 | (1) |
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177 | (1) |
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177 | (1) |
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178 | (7) |
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Chapter 9 Application of deep learning and machine learning methods in water quality modeling and prediction: a review |
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185 | (34) |
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185 | (3) |
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Deep learning and machine learning in WQ modeling and prediction |
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188 | (1) |
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Overview of learning methods |
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189 | (1) |
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189 | (1) |
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190 | (1) |
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190 | (1) |
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190 | (1) |
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Machine learning architectures used in water quality modeling and prediction |
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191 | (1) |
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Artificial neural network |
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191 | (1) |
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192 | (1) |
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Multilayer perceptron neural network (MLP-ANN or MLP) |
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192 | (1) |
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Radial basis function (RBF-ANN) |
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192 | (1) |
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193 | (1) |
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193 | (1) |
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193 | (1) |
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Deep learning architectures used in water quality modeling and prediction |
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194 | (1) |
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Convolutional neural network |
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195 | (1) |
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196 | (1) |
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Generative unsupervised models |
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197 | (1) |
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Application of ML and DL models in WQ prediction of different water systems |
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198 | (1) |
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Modeling and prediction of different water systems |
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198 | (1) |
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199 | (5) |
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204 | (1) |
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204 | (1) |
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205 | (2) |
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Model structure determination |
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207 | (1) |
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208 | (1) |
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Performance evaluation measures |
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209 | (1) |
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Challenges facing DL and ML predictions |
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210 | (1) |
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Conclusion and future prospect |
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210 | (1) |
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211 | (8) |
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Chapter 10 Intelligent systems in water pollution research: a review |
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219 | (24) |
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Mohammad Hassan Shahverdian |
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219 | (1) |
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220 | (2) |
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The basis of water standards |
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220 | (1) |
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221 | (1) |
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Water desalination technologies |
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222 | (11) |
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Phase-changing desalination |
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222 | (8) |
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Without phase-changing desalination |
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230 | (3) |
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233 | (3) |
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Selected case study from the literature |
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236 | (1) |
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236 | (1) |
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236 | (1) |
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The utilized machine learning approaches |
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237 | (1) |
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237 | (3) |
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240 | (1) |
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240 | (3) |
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Chapter 11 A long short-term memory deep learning approach for river water temperature prediction |
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243 | (30) |
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Mohammad Zounemat-Kermani |
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243 | (2) |
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245 | (1) |
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245 | (1) |
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Performance assessment of the models |
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246 | (1) |
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246 | (7) |
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Gaussian process regression |
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246 | (2) |
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Gene expression programming |
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248 | (1) |
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Online sequential extreme learning machine |
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248 | (3) |
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Support vector regression |
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251 | (1) |
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251 | (2) |
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Multiple linear regression |
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253 | (1) |
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253 | (12) |
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253 | (3) |
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256 | (2) |
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USGS 422302071083801 station |
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258 | (1) |
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USGS 422622122004000 station |
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259 | (5) |
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264 | (1) |
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Conclusions and future recommendations |
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265 | (1) |
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266 | (7) |
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SECTION 3 Data-centric and intelligent systems in land pollution research |
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Chapter 12 Data-centric and intelligent systems in land pollution research |
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273 | (24) |
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273 | (1) |
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Application of deep learning and machine learning methods in flow modeling of landfill leachate |
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274 | (3) |
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274 | (1) |
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275 | (2) |
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Application of deep learning and machine learning methods in soil quality assessment and remediation |
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277 | (6) |
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277 | (4) |
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281 | (2) |
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Establishing a nexus between nonbiodegradable waste and data-centric systems |
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283 | (7) |
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283 | (3) |
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286 | (4) |
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Case studies of evaluations and analysis of solid waste management techniques by deep learning and machine learning methods |
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290 | (4) |
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The complexity of solid waste management techniques |
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290 | (2) |
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292 | (2) |
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294 | (1) |
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294 | (3) |
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Chapter 13 Application of artificial intelligence in the mapping and measurement of soil pollution |
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297 | (22) |
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297 | (1) |
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298 | (1) |
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Systematic review protocol |
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298 | (1) |
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Search and selection criteria |
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299 | (1) |
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Quality check and data extraction |
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299 | (1) |
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Theoretical background of the different AI models |
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300 | (1) |
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Artificial intelligence models applied in the field |
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300 | (1) |
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Artificial neural network |
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300 | (4) |
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The multilayer perceptron neural network |
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302 | (1) |
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The backpropagation neural network |
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302 | (1) |
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The radial basis function neural network |
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302 | (2) |
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304 | (1) |
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Adaptive neurofuzzy inference system |
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304 | (1) |
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305 | (1) |
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306 | (1) |
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Bayesian machine learning |
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306 | (1) |
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307 | (1) |
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Application domain of the different AI models |
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307 | (2) |
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AI models in soil pollution mapping |
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307 | (2) |
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AI models in soil pollutant measurement |
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309 | (3) |
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312 | (1) |
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313 | (5) |
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318 | (1) |
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Chapter 14 Artificial intelligence in the reduction and management of land pollution |
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319 | (18) |
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319 | (1) |
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The use of artificial intelligence and robotics in system modification for effective on-spot minimization of wastes in process industries |
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320 | (2) |
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Artificial intelligence in the disposal and smart recycling of wastes |
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321 | (1) |
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Convolutional neural network model system of waste classification |
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322 | (1) |
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323 | (1) |
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Artificial intelligence-robotics pickup system |
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323 | (1) |
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Artificial intelligence and robotics in waste recycling |
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324 | (1) |
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Robotic recycle sorting system |
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325 | (1) |
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Working principle of the robotic sorting system |
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325 | (1) |
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Advantages and disadvantages of recycling robotic sorting system |
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325 | (1) |
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Artificial intelligence-robotic quality assessment system |
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325 | (1) |
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Reforestation for land pollution management: impact of drones and neural network |
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326 | (2) |
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Land pollution management via sustainable green agriculture: use of machine learning and robotics |
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328 | (1) |
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329 | (1) |
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330 | (3) |
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333 | (4) |
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SECTION 4 Data-centric and intelligent systems in noise pollution research and other environmental engineering issues |
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Chapter 15 Advanced soft computing techniques in modeling noise pollution health impacts |
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337 | (16) |
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337 | (1) |
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Effect of noise pollution on human health |
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338 | (2) |
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338 | (1) |
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Interference with speech communication |
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339 | (1) |
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339 | (1) |
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Cardiovascular and physiological |
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339 | (1) |
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Disturbances in mental health |
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339 | (1) |
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The effects of noise on performance |
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339 | (1) |
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Negative social behavior and annoyance reactions |
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340 | (1) |
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Noise pollution health-impact modeling |
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340 | (2) |
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Exploratory factor analysis |
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340 | (1) |
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Structural equation modeling |
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341 | (1) |
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Stage 1 Defining individual constructs |
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342 | (1) |
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Stage 2 Developing and specifying the measurement model |
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342 | (1) |
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Stage 3 Designing a study to produce empirical results |
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343 | (1) |
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Stage 4 Assessing measurement model validity |
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344 | (1) |
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Stage 5 Specifying the structural model |
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344 | (1) |
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Stage 6 Assessing the structural model validity |
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345 | (2) |
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Adaptive neuro-fuzzy inference system |
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345 | (2) |
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SEM and ANFIS case studies |
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347 | (2) |
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349 | (1) |
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349 | (4) |
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Chapter 16 Intelligent and knowledge-based waste management: smart decision-support system |
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353 | (28) |
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353 | (1) |
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Trends in exploration and production wastes in the oil and gas industry |
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354 | (5) |
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Exploration waste in the oil and gas industry |
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355 | (2) |
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Production and refining waste in the oil and gas industry |
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357 | (2) |
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Oil and gas waste management |
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359 | (2) |
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Conventional waste management approach in oil and gas industry |
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361 | (3) |
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361 | (2) |
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Waste treatment techniques in oil and gas industry |
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363 | (1) |
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Environmental impact of oil and gas generated wastes |
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364 | (2) |
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Challenges of conventional waste management systems |
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366 | (1) |
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Expert system for oil and gas waste management system |
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367 | (4) |
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Sensor application in waste management expert system |
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368 | (2) |
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Algorithm of the proposed sensor approach |
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370 | (1) |
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Gaps in waste management expert system |
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371 | (2) |
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Effective utilization of expert systems in oil and gas industry waste management |
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373 | (1) |
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374 | (1) |
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375 | (6) |
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Chapter 17 Computer-aided modeling of solid waste conversion: case study of maize (Zea mays) residues air gasification |
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381 | (12) |
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381 | (1) |
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382 | (2) |
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382 | (1) |
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382 | (1) |
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383 | (1) |
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384 | (4) |
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Effect on temperature on product selectivity |
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385 | (1) |
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Effect of pressure on product selectivity |
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385 | (2) |
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Effect of air -- fuel ratio on product selectivity |
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387 | (1) |
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388 | (1) |
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389 | (4) |
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Chapter 18 Neural network model for biological waste management systems |
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393 | (24) |
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393 | (1) |
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394 | (1) |
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Data-driven modeling approaches |
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394 | (1) |
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Artificial neural network-based predictive modeling |
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394 | (1) |
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Choosing the activation function |
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395 | (1) |
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Choosing the appropriate training algorithm |
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396 | (1) |
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Data preprocessing and randomization |
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397 | (1) |
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397 | (1) |
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Internal parameters of the network and performance evaluation |
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397 | (1) |
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398 | (1) |
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398 | (1) |
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398 | (1) |
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Process modeling of biological reactors for DCM removal |
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398 | (1) |
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Artificial neural modeling of the different biological reactors |
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399 | (2) |
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Effect of internal network parameters on the network architecture-modified RBC |
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400 | (1) |
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Predictive capability of the model for modified RBC |
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401 | (3) |
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Sensitivity analysis of inputs |
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404 | (2) |
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Removal of DCM in biotrickling filter |
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406 | (1) |
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Effect of internal network parameters on the network architecture |
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406 | (1) |
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Predictive capability and sensitivity of the ANN model |
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407 | (5) |
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412 | (1) |
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412 | (5) |
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Chapter 19 The role of artificial neural network in byproducts development: a case of modeling and optimization studies |
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417 | (16) |
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417 | (1) |
|
|
|
418 | (1) |
|
|
|
419 | (1) |
|
|
|
420 | (1) |
|
Selected optimization tools used in bioprocess development as computational intelligence |
|
|
420 | (3) |
|
|
|
420 | (3) |
|
|
|
423 | (1) |
|
|
|
424 | (1) |
|
Application of optimization tools in bioprocessing operations |
|
|
424 | (1) |
|
|
|
425 | (1) |
|
|
|
426 | (1) |
|
|
|
426 | (1) |
|
Future development or trend |
|
|
427 | (1) |
|
|
|
427 | (1) |
|
|
|
427 | (6) |
|
Chapter 20 Modeling of grains sun drying: from theoretical methods to intelligent systems |
|
|
433 | (10) |
|
|
|
|
|
|
|
|
|
433 | (2) |
|
An account of early theoretical modeling efforts |
|
|
435 | (2) |
|
Intelligent systems in the modeling of grains sun drying |
|
|
437 | (1) |
|
|
|
438 | (1) |
|
|
|
439 | (4) |
| Index |
|
443 | |
Lisainfo e-raamatute kohta