Modelling fish habitat preference with a genetic algorithm-optimized Takagi-Sugeno model based on pairwise comparisons

Species-environment relationships are used for evaluating the current status of target species and the potential impact of natural or anthropogenic changes of their habitat. Recent researches reported that the results are strongly affected by the quality of a data set used. The present study attempted to apply pairwise comparisons to modelling fish habitat preference with Takagi-Sugeno-type fuzzy habitat preference models (FHPMs) optimized by a genetic algorithm (GA). The model was compared with the result obtained from the FHPM optimized based on mean squared error (MSE). Three independent data sets were used for training and testing of these models. The FHPMs based on pairwise comparison produced variable habitat preference curves from 20 different initial conditions in the GA. This could be partially ascribed to the optimization process and the regulations assigned. This case study demonstrates applicability and limitations of pairwise comparison-based optimization in an FHPM. Future research should focus on a more flexible learning process to make a good use of the advantages of pairwise comparisons

A short review on the application of computational intelligence and machine learning in the bioenvironmental sciences

This paper aims to provide a short review on the application of computational intelligence (CI) and machine learning (ML) in the bioenvironmental sciences. To clearly illustrate the current status, we limit our focus to some key approaches, namely fuzzy systems (FSs), artificial neural networks (ANNs) and genetic algorithms (GAs) as well as some ML methods. The trends in the application studies are categorized based on the targets of the model such as animal, fish, plant, soil and water. We give an overview of specific topics in the bioenvironmental sciences on the basis of the review papers on model comparisons in the field. The summary of the modelling approaches with respect to their aim and potential application fields can promote the use of CI and ML in the bioenvironmental sciences

Crop Coverage Data Classification using Support Vector Machine

A statistical tool which can be used in various applications ranging from medical science to agricultural science is support vector machines. The proposed methodology used is support vector machine and it isused to classify a raster map. The dataset used herein is of Gujarat state agriculture map. The proposed approach is used to classify raster map into groups based on crop coverage of various crops. One group represents rice crop coverageand the othermillets crop coverage and yet another that of cotton crop coverage.Various statistical parameters are used to measure the efficacy of the proposed methodology employed

Supervised Classification of Remote Sensed Data using Support Vector Machine

Support vector machines have been used as a classification method in various domains including and not restricted to species distribution and land cover detection Support vector machines offer many key advantages like its capacity to handle huge feature spaces and its flexibility in selecting a similarity function In this paper the support vector machine classification method is applied to remote sensed data Two different formats of remote sensed data is considered for the same The first format is a comma separated value format wherein a classification model is developed to predict whether a specific bird species belongs to Darjeeling area or any other region The second format used is raster format which contains image of Andhra Pradesh state in India Support vector machine classification method is used herein to classify the raster image into categories One category represents land and the other water wherein green color is used to represent land and light blue color is used to represent water Later the classifier is evaluated using kappa statistics and accuracy parameter

Crop Coverage Data Classification using Support Vector Machine

A statistical tool which can be used in various applications ranging from medical science to agricultural science is support vector machines. The proposed methodology used is support vector machine and it isused to classify a raster map. The dataset used herein is of Gujarat state agriculture map. The proposed approach is used to classify raster map into groups based on crop coverage of various crops. One group represents rice crop coverageand the othermillets crop coverage and yet another that of cotton crop coverage.Various statistical parameters are used to measure the efficacy of the proposed methodology employed

MAPEAMENTO DA CAPACIDADE DE USO DA TERRA COMO CONTRIBUIÇÃO AO PLANEJAMENTO DE USO DO SOLO EM SUB-BACIA HIDROGRÁFICA PILOTO NO SUL DE MINAS GERAIS

A gestão sustentável dos solos ainda é um desafio para a sociedade, visto que é cada vez maior o número de áreas que atingiram o limite de sua capacidade produtiva. De acordo com a Organização das Nações Unidas para Alimentação e Agricultura (FAO), atualmente 25% dos solos do planeta estão degradados, provocando sérios prejuízos econômicos e ambientais. O planejamento racional do uso da terra é essencial para a manutenção da sustentabilidade dos solos, produção agrícola e proteção da biodiversidade, uma vez que, orienta o desenvolvimento de atividades adequadas às potencialidades e limitações do meio físico. Nesse contexto, o presente trabalho teve por objetivo o mapeamento e avaliação da capacidade de uso da terra e do uso atual do solo na sub-bacia hidrográfica do ribeirão José Pereira, situada no município de Itajubá/MG e que apresenta sérios problemas erosivos. Foi verificado que mais de 95% da sub-bacia pertence as Classes VIe e VIIe, condicionadas, sobretudo, pelas condições de relevo e susceptibilidade à erosão hídrica. As pastagens, o reflorestamento e a preservação da vida silvestre são os usos do solo mais recomendados para as classes identificadas. Cerca de 50% da área é coberta por pastagens, uso condizente à capacidade produtiva dos solos. No entanto, a ausência de técnicas conservacionistas no manejo das pastagens constitui fator preponderante para a instalação dos processos de degradação