Use of support vector machines (SVMs) to predict distribution of an invasive water fern Azolla filiculoides (Lam.) in Anzali wetland, southern Caspian Sea, Iran

  title={Use of support vector machines (SVMs) to predict distribution of an invasive water fern Azolla filiculoides (Lam.) in Anzali wetland, southern Caspian Sea, Iran},
  author={Roghayeh Sadeghi and Rahmat Zarkami and Karim Sabetraftar and Patrick Van Damme},
  journal={Ecological Modelling},


The aim of this work is to examine the feasibilities of the support vector machines (SVMs) and K-nearest neighbor (K-NN) classifier methods for the classification of an aquifer in the Khuzestan

Support vector machine and artificial neural network to model soil pollution: a case study in Semnan Province, Iran

The results indicated that because of some problems associated with ANNs (such as local minima), for cases in which there are quite comparable results for ANNs and SVMs, the usage of SVMs is preferable.

Statistical Learning Methods for Classification and Prediction of Groundwater Quality Using a Small Data Record

The objective of this study was to consider the efficiency of support vector machine (SVM) and artificial neural network (ANN) for the classification and prediction of groundwater quality using a

Predicting Habitat Suitability and Conserving Juniperus spp. Habitat Using SVM and Maximum Entropy Machine Learning Techniques

Support vector machine (SVM) and maximum entropy (MaxEnt) machine learning techniques are well suited to model the habitat suitability of species. In this study, SVM and MaxEnt models were developed

Prediction of water quality index in constructed wetlands using support vector machine

This research highlights that the SVM and FFBP can be successfully employed for the prediction of water quality in a free surface constructed wetland environment and reduce substantial efforts and time by optimizing the computations.

Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh

The results showed that wetland areas at present are declining less than one-third of those in 1988 due to the construction of the dam at Farakka, which is situated at the upstream of the Padma River.

Assessment, monitoring and modelling of the abundance of Dunaliella salina Teod in the Meighan wetland, Iran using decision tree model

The microalga Dunaliella salina has been broadly studied for different purposes such as beta-carotene production, toxicity assessment and salinity tolerance, yet research on the habitat suitability



Use of classification tree methods to study the habitat requirements of tench (Tinca tinca) (L., 1758)

Classification trees (J48) were induced to predict the habitat requirements of tench (Tinca tinca). 306 datasets were used for the given fish during 8 years in the river basins in Flanders (Belgium).

Application of classification trees-J48 to model the presence of roach (Rutilus rutilus) in rivers

In the present study, classification trees (CTs-J48 algorithm) were used to study the occurrence of roach in rivers in Flanders (Belgium). The presence/absence of roach was modelled based on a set of

Decision Tree Models for Prediction of Macroinvertebrate Taxa in the River Axios (Northern Greece)

In this study, decision tree models were induced to predict the habitat suitability of six macroinvertebrate taxa: Asellidae, Baetidae, Caenidae, Gammaridae, Gomphidae and Heptageniidae. The

Development and Application of Predictive River Ecosystem Models Based on Classification Trees and Artificial Neural Networks

Prediction of freshwater organisms based on machine learning techniques is becoming more and more reliable due to the availability of appropriate datasets and modelling techniques, and models have several interesting applications in river management.

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

This analysis revealed that the applied model training and validation methodologies can often be improved and moreover crucial steps in the modelling process are often poorly documented, so suggestions to improve model development, assessment and application in ecological river management are presented.