Three-dimensional hydrodynamic models coupled with GIS-based neuro-fuzzy classification for assessing environmental vulnerability of marine cage aquaculture

There is considerable opportunity to develop new modelling techniques within a Geographic Information Systems (GIS) framework for the development of sustainable marine cage culture. However, the spatial data sets are often uncertain and incomplete, therefore new spatial models employing “soft computing” methods such as fuzzy logic may be more suitable. The aim of this study is to develop a model using Neuro-fuzzy techniques in a 3D GIS (Arc View 3.2) to predict coastal environmental vulnerability for Atlantic salmon cage aquaculture. A 3D hydrodynamic model (3DMOHID) coupled to a particle-tracking model is applied to study the circulation patterns, dispersion processes and residence time in Mulroy Bay, Co. Donegal Ireland, an Irish fjard (shallow fjordic system), an area of restricted exchange, geometrically complicated with important aquaculture activities. The hydrodynamic model was calibrated and validated by comparison with sea surface and water flow measurements. The model provided spatial and temporal information on circulation, renewal time, helping to determine the influence of winds on circulation patterns and in particular the assessment of the hydrographic conditions with a strong influence on the management of fish cage culture. The particle-tracking model was used to study the transport and flushing processes. Instantaneous massive releases of particles from key boxes are modelled to analyse the ocean-fjord exchange characteristics and, by emulating discharge from finfish cages, to show the behaviour of waste in terms of water circulation and water exchange. In this study the results from the hydrodynamic model have been incorporated into GIS to provide an easy-to-use graphical user interface for 2D (maps), 3D and temporal visualization (animations), for interrogation of results. v Data on the physical environment and aquaculture suitability were derived from a 3- dimensional hydrodynamic model and GIS for incorporation into the final model framework and included mean and maximum current velocities, current flow quiescence time, water column stratification, sediment granulometry, particulate waste dispersion distance, oxygen depletion, water depth, coastal protection zones, and slope. The Neuro-fuzzy classification model NEFCLASS–J, was used to develop learning algorithms to create the structure (rule base) and the parameters (fuzzy sets) of a fuzzy classifier from a set of classified training data. A total of 42 training sites were sampled using stratified random sampling from the GIS raster data layers, and the vulnerability categories for each were manually classified into four categories based on the opinions of experts with field experience and specific knowledge of the environmental problems investigated. The final products, GIS/based Neuro Fuzzy maps were achieved by combining modeled and real environmental parameters relevant to marine fin fish Aquaculture. Environmental vulnerability models, based on Neuro-fuzzy techniques, showed sensitivity to the membership shapes of the fuzzy sets, the nature of the weightings applied to the model rules, and validation techniques used during the learning and validation process. The accuracy of the final classifier selected was R=85.71%, (estimated error value of ±16.5% from Cross Validation, N=10) with a Kappa coefficient of agreement of 81%. Unclassified cells in the whole spatial domain (of 1623 GIS cells) ranged from 0% to 24.18 %. A statistical comparison between vulnerability scores and a significant product of aquaculture waste (nitrogen concentrations in sediment under the salmon cages) showed that the final model gave a good correlation between predicted environmental vi vulnerability and sediment nitrogen levels, highlighting a number of areas with variable sensitivity to aquaculture. Further evaluation and analysis of the quality of the classification was achieved and the applicability of separability indexes was also studied. The inter-class separability estimations were performed on two different training data sets to assess the difficulty of the class separation problem under investigation. The Neuro-fuzzy classifier for a supervised and hard classification of coastal environmental vulnerability has demonstrated an ability to derive an accurate and reliable classification into areas of different levels of environmental vulnerability using a minimal number of training sets. The output will be an environmental spatial model for application in coastal areas intended to facilitate policy decision and to allow input into wider ranging spatial modelling projects, such as coastal zone management systems and effective environmental management of fish cage aquaculture

A review on the integration of artificial intelligence into coastal modeling

Author name used in this publication: Kwokwing Chau2005-2006 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

A review on integration of artificial intelligence into water quality modelling

2005-2006 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system

Electrical conductivity is an important indicator for water quality assessment. Since the composition of mineral salts affects the electrical conductivity of groundwater, it is important to understand the relationships between mineral salt composition and electrical conductivity. In this present paper, we develop an adaptive neuro-fuzzy inference system (ANFIS) model for groundwater electrical conductivity based on the concentration of positively charged ions in water. It is shown that the ANFIS model outperforms more traditional methods of modelling electrical conductivity based on the total solids dissolved in the water, even though ANFIS uses less information. Additionally, the fuzzy rules in the ANFIS model provide a categorization of ground water samples in a manner that is consistent with the current understanding of geophysical processes

Classifying the biodiversity of the Great Barrier Reef World Heritage Area for the classification phase of the representative areas program

This technical report outlines the methods that the Great Barrier Reef Marine Park Authority used to classify the biodiversity of the marine environs of the Great Barrier Reef World Heritage Area for the Representative Areas Program. Classification was the first step in the multiphase Representative Areas Program that eventuated in a new network of no-take areas, free from extractive activities, in the Great Barrier Reef Marine Park

Guidance for benthic habitat mapping: an aerial photographic approach

This document, Guidance for Benthic Habitat Mapping: An Aerial Photographic Approach, describes proven technology that can be applied in an operational manner by state-level scientists and resource managers. This information is based on the experience gained by NOAA Coastal Services Center staff and state-level cooperators in the production of a series of benthic habitat data sets in Delaware, Florida, Maine, Massachusetts, New York, Rhode Island, the Virgin Islands, and Washington, as well as during Center-sponsored workshops on coral remote sensing and seagrass and aquatic habitat assessment. (PDF contains 39 pages) The original benthic habitat document, NOAA Coastal Change Analysis Program (C-CAP): Guidance for Regional Implementation (Dobson et al.), was published by the Department of Commerce in 1995. That document summarized procedures that were to be used by scientists throughout the United States to develop consistent and reliable coastal land cover and benthic habitat information. Advances in technology and new methodologies for generating these data created the need for this updated report, which builds upon the foundation of its predecessor

Determine of Surface Water Quality Index in Iran

In modeling complex of environmental problems, researchers often fail to define precise statements about input and outcomes of contaminants, but fuzzy logic could help to dominate this logical indecision. The goal of this work is to propose a new river water quality indicator using fuzzy logic. The proposed index combines six indicators, and not only does it exhibit a tool that accounts for the discrepancy between the two base indices, but also provides a quantifiable score for the determined water quality. These classifications with a membership grade can be of a sound support for decision-making, and can help assign each section of a river a gradual quality sub-objective to be reached. To show the applicability of the proposed approach, the new indicator was used to classify water quality in a number of stations along the basins of Qarah-chai and Siminehrood. The obtained classifications were then compared to the conventional physicochemical water quality indicator currently in use in Iran. The results revealed that the fuzzy indicator provided stringent classifications compared to the conventional index in 38% and 44% of the cases for the two basins respectively. These noted exceptions are mainly due to the big disagreement between the different quality thresholds in the two standards, especially for fecal coliform and total phosphorus. These large disparities put forward an argument for the Iranian water quality law to be upgraded. Keywords: Fuzzy logic; Qarah-chai basin; Siminehrood; Water quality inde

Multi-Criteria Decision Analysis as a tool to extract fishing footprints and estimate fishing pressure: application to small scale coastal fisheries and implications for management in the context of the Maritime Spatial Planning Directive

In the context of the Maritime Spatial Planning Directive and with the intention of contributing to the implementation of a future maritime spatial plan, it was decided to analyze data from the small scale coastal fisheries sector of Greece and estimate the actual extent of its activities, which is largely unknown to date. To this end we identified the most influential components affecting coastal fishing: fishing capacity, bathymetry, distance from coast, Sea Surface Chlorophyll (Chl-a) concentration, legislation, marine traffic activity, trawlers and purse seiners fishing effort and no-take zones. By means of Multi-Criteria Decision Analysis (MCDA) conducted through a stepwise procedure, the potential fishing footprint with the corresponding fishing intensity was derived. The method provides an innovative and cost-effective way to assess the impact of the, notoriously hard to assess, coastal fleet. It was further considered how the inclusion of all relevant anthropogenic activities (besides fishing) could provide the background needed to plan future marine activities in the framework of Marine Spatial Planning (MSP) and form the basis for a more realistic management approach

Dynamics of phytoplankton community composition in the western Gulf of Maine

This dissertation is founded on the importance of phytoplankton community composition to marine biogeochemistry and ecosystem processes and motivated by the need to understand their distributions on regional to global scales. The ultimate goal was to predict surface phytoplankton communities using satellite remote sensing by relating marine habitats--defined through a statistical description of environmental properties--to different phytoplankton communities. While phytoplankton community composition is governed by the interplay of abiotic and biotic interactions, the strategy adopted here was to focus on the physical abiotic factors. This allowed for the detection of habitats from ocean satellites based on abiotic factors that were linked to associated phytoplankton communities. The research entailed three studies that addressed different aspects of the main goal using a dataset collected in the western Gulf of Maine over a 3-year period. The first study evaluated a chemotaxonomic method that quantified phytoplankton composition from pigment data. This enabled the characterization of three phytoplankton communities, which were defined by the relative abundance of diatoms and flagellates. The second study examined the cycles of these communities along with environmental variables, and the results revealed that the three phytoplankton communities exhibited an affinity to different hydrographic regimes. The third study focused on the implementation of a classifier that predicted phytoplankton communities from environmental variables. Its ability to differentiate communities dominated by diatoms versus flagellates was shown to be high. However, the increase in data imprecision when using satellite data led to lowered performance and favored an approach that incorporated fuzzy logic. The fuzzy method is well suited to characterize the uncertainties in phytoplankton community prediction, and provides a measure of confidence on predicted communities. The final product of the overall dissertation was a time series of maps generated from satellite observations depicting the likelihood of three phytoplankton communities. This dissertation reached the main goal and, moreover, demonstrated that improvements in the predictive power of the method can be achieved with increased precision and more advanced satellite-derived products. The results of this research can benefit present bio-optical and primary productivity models, and ecosystem-based models of the marine environment