A Survey of Applications of Artificial Intelligence Algorithms in Eco-environmental Modelling

Application of artificial intelligence (AI) approaches in eco-environmental modeling has gradually increased for the last decade. Comprehensive understanding and evaluation on the applicability of this approach to eco-environmental modeling are needed. In this study, we reviewed the previous studies that used AI-techniques in eco-environmental modeling. Decision Tree (DT) and Artificial Neural Network (ANN) were found to be major AI algorithms preferred by researchers in ecological and environmental modeling areas. When the effect of the size of training data on model prediction accuracy was explored using the data from the previous studies, the prediction accuracy and the size of training data showed nonlinear correlation, which was best-described by hyperbolic saturation function among the tested nonlinear functions including power and logarithmic functions. The hyperbolic saturation equations were proposed to be used as a guideline for optimizing the size of training data set, which is critically important in designing the field experiments required for training AI-based eco-environmental modeling.

[1]  R. Shariff,et al.  Drinking water quality and treatment: the use of artificial neural networks , 2001 .

[2]  Joon Heo,et al.  Sensitivity Analysis of the Effect of Soil Ecological Quality Information in Selecting Eco-Friendly Road Route , 2008 .

[3]  F. Recknagel,et al.  Artificial neural network approach for modelling and prediction of algal blooms , 1997 .

[4]  William Stafford Noble,et al.  Support vector machine , 2013 .

[5]  Dragos D. Margineantu,et al.  Learning to predict channel stability using biogeomorphic features , 2006 .

[6]  B. Ostendorf,et al.  The utility of artificial neural networks for modelling the distribution of vegetation in past, present and future climates , 2001 .

[7]  S. Lek,et al.  Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters , 2003 .

[8]  Young-Seuk Park,et al.  Patterning and predicting aquatic macroinvertebrate diversities using artificial neural network. , 2003, Water research.

[9]  Judi Scheffer,et al.  Data mining in the survey setting: why do children go off the rails? , 2002 .

[10]  Dong-Kyun Kim,et al.  River phytoplankton prediction model by Artificial Neural Network: Model performance and selection of input variables to predict time-series phytoplankton proliferations in a regulated river system , 2006, Ecological Informatics.

[11]  I. Seginer,et al.  Some artificial neural network applications to greenhouse environmental control , 1997 .

[12]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[13]  Nostrand Reinhold,et al.  the utility of using the genetic algorithm approach on the problem of Davis, L. (1991), Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York. , 1991 .

[14]  S. J. Flynn,et al.  Opening the black box of soil microbial diversity , 1999 .

[15]  Giles M. Foody,et al.  The effect of training set size and composition on artificial neural network classification , 1995 .

[16]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[17]  G. V. Kass.  An Exploratory Technique for Investigating Large Quantities of Categorical Data , 1980 .

[18]  Bhavani Thuraisingham,et al.  Data Mining: Technologies, Techniques, Tools, and Trends , 1998 .

[19]  David J. Hand,et al.  Data Mining: Statistics and More? , 1998 .

[20]  R. Plant,et al.  Classification trees: An alternative non‐parametric approach for predicting species distributions , 2000 .

[21]  Sy-Jye Guo.  Analysis of cycle excavation and productivity of large-scale rock tunnel projects — lesson learned in Taiwan , 2001 .

[22]  David A. Landgrebe.  On the relationship between class definition precision and classification accuracy in hyperspectral analysis , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[23]  Moshe Leshno,et al.  The Effect of Training Data Set Size and the Complexity of the Separation Function on Neural Network Classification Capability: The Two-Group Case , 1997 .

[24]  Lawrence O. Ticknor,et al.  Phylogenetic Specificity and Reproducibility and New Method for Analysis of Terminal Restriction Fragment Profiles of 16S rRNA Genes from Bacterial Communities , 2001, Applied and Environmental Microbiology.

[25]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[26]  Ingrid M. Schleiter,et al.  Modelling water quality, bioindication and population dynamics in lotic ecosystems using neural networks , 1999 .

[27]  S. J. Stanley,et al.  Development of a full-scale artificial neural network model for the removal of natural organic matter by enhanced coagulation , 1999 .

[28]  Alan Dix,et al.  An Introduction to Artificial Intelligence , 1996 .

[29]  Peter D. Kemp,et al.  A decision tree approach modelling functional group abundance in a pasture ecosystem , 2005 .

[30]  Chun-Chieh Yang,et al.  Application of decision tree technology for image classification using remote sensing data , 2003 .

[31]  Miquel Sànchez-Marrè,et al.  Artificial Intelligence and Environmental Decision Support Systems , 2000, Applied Intelligence.

[32]  Joonhong Park,et al.  A Weak Correlation of Field-determined Soil Microbial Diversity with Quantitative Ecological Map Information and its Methodological Implication in Estimation Soil Ecological Quality , 2007 .

[33]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[34]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1989, IJCAI 1989.

[35]  Felix T. S. Chan,et al.  Using case-based reasoning to evaluate supplier environmental management performance , 2003, Expert Syst. Appl..

[36]  Friedrich Recknagel,et al.  Applications of machine learning to ecological modelling , 2001 .

[37]  Pieter Adriaans,et al.  Data mining , 1996 .

[38]  H. Maier,et al.  The Use of Artificial Neural Networks for the Prediction of Water Quality Parameters , 1996 .

[39]  Giles M. Foody,et al.  An evaluation of some factors affecting the accuracy of classification by an artificial neural network , 1997 .

[40]  Ian D. Watson,et al.  Applying case-based reasoning - techniques for the enterprise systems , 1997 .

[41]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[42]  Steven L. Tanimoto.  The elements of artificial intelligence , 1800 .

[43]  Michael J. A. Berry,et al.  Data mining techniques - for marketing, sales, and customer support , 1997, Wiley computer publishing.

[44]  Taşkin Kavzoĝlu,et al.  An investigation of the design and use of feed-forward artificial neural networks in the classification of remotely sensed images , 2001 .

[45]  Ingoo Han,et al.  Case-based reasoning supported by genetic algorithms for corporate bond rating , 1999 .

[46]  Michele Scardi,et al.  Developing an empirical model of phytoplankton primary production: a neural network case study , 1999 .

[47]  A. Prasad,et al.  PREDICTING ABUNDANCE OF 80 TREE SPECIES FOLLOWING CLIMATE CHANGE IN THE EASTERN UNITED STATES , 1998 .

[48]  George F. Hepner,et al.  Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification , 1990 .

[49]  Per-Olof Gutman,et al.  Optimal CO2 control in a greenhouse modeled with neural networks , 1998 .

[50]  Davide Geneletti,et al.  Biodiversity Impact Assessment of roads: an approach based on ecosystem rarity , 2003 .

[51]  Thomas Brey,et al.  Artificial neural network versus multiple linear regression: predicting P/B ratios from empirical data , 1996 .

[52]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[53]  Sovan Lek,et al.  Artificial neural networks as a tool in ecological modelling, an introduction , 1999 .

[54]  I. Dimopoulos,et al.  Role of some environmental variables in trout abundance models using neural networks , 1996 .

[55]  Sovan Lek,et al.  Predictive models of collembolan diversity and abundance in a riparian habitat , 1999 .

[56]  Edward J. Rykiel,et al.  Artificial intelligence and expert systems in ecology and natural resource management , 1989 .

[57]  Paul M. Mather,et al.  An assessment of the effectiveness of decision tree methods for land cover classification , 2003 .

[58]  Qing Zhang,et al.  Forecasting raw-water quality parameters for the North Saskatchewan River by neural network modeling , 1997 .

[59]  Giles M. Foody,et al.  Training set size requirements for the classification of a specific class , 2006 .

[60]  Gavriel Salvendy,et al.  Survey of expert opinion: Which machine learning method may be used for which task? , 1996, Int. J. Hum. Comput. Interact..

[61]  Young-Seuk Park,et al.  Patternizing communities by using an artificial neural network , 1996 .

[62]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[63]  Paul E. Gessler,et al.  Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments , 2008 .

[64]  Chandra A. Madramootoo,et al.  Modeling nitrate leaching using neural networks , 1998 .

[65]  Sovan Lek,et al.  Energy availability and habitat heterogeneity predict global riverine fish diversity , 1998, Nature.

[66]  Drew McDermott,et al.  Introduction to artificial intelligence , 1986, Addison-Wesley series in computer science.

[67]  Ching-Gung Wen,et al.  A neural network approach to multiobjective optimization for water quality management in a river basin , 1998 .

[68]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.