Application of k-means clustering to environmental risk zoning of the chemical industrial area

The homogeneous risk characteristics within a sub-area and the heterogeneous from one sub-area to another are unclear using existing environmental risk zoning methods. This study presents a new zoning method by determining and categorizing the risk characteristics using the k-means clustering data mining technology. The study constructs indices and develops index quantification models for environmental risk zoning by analyzing the mechanism of environmental risk occurrence. We calculate the source risk index, air risk field index, water risk field index, and target vulnerability of the study area with Nanjing Chemical Industrial Park using a 100 m - 100 m mesh grid as the basic zoning unit, and then use k-means clustering to analyze the environmental risk in the area. We obtain the optimal clustering number with the largest average silhouette coefficient by calculating the average silhouette coefficients of clustering at different k-values. The clustering result with the optimal clustering number is then used for the environmental risk zoning, and the zoning result is mapped using the geographic information system. The study area is divided into five sub-areas. The common environmental risk characteristics within the same sub-area, as well as the differences between sub- areas, are presented. The zoning is helpful in risk management and is convenient for decision makers to distribute limited resources to different sub-areas in the design of risk reducing intervention.     

Determining factors that influence the dispersal of a pelagic species: A comparison between artificial neural networks and evolutionary algorithms

Because of increasing transport and trade there is a growing threat of marine invasive species being introduced into regions where they do not presently occur. So that the impacts of such species can be mitigated, it is important to predict how individuals, particularly passive dispersers are transported and dispersed in the ocean as well as in coastal regions so that new incursions of potential invasive species are rapidly detected and origins identified. Such predictions also support strategic monitoring, containment and/or eradication programs. To determine factors influencing a passive disperser, around coastal New Zealand, data from the genus Physalia (Cnidaria: Siphonophora) were used. Oceanographic data on wave height and wind direction and records of occurrences of Physalia on swimming beaches throughout the summer season were used to create models using artificial neural networks (ANNs) and Na?ve Bayesian Classifier (NBC). First, however, redundant and irrelevant data were removed using feature selection of a subset of variables. Two methods for feature selection were compared, one based on the multilayer perceptron and another based on an evolutionary algorithm. The models indicated that New Zealand appears to have two independent systems driven by currents and oceanographic variables that are responsible for the redistribution of Physalia from north of New Zealand and from the Tasman Sea to their subsequent presence in coastal waters. One system is centred in the east coast of northern New Zealand and the other involves a dynamic system that encompasses four other regions on both coasts of the country. Interestingly, the models confirm, molecular data obtained from Physalia in a previous study that identified a similar distribution of systems around New Zealand coastal waters. Additionally, this study demonstrates that the modelling methods used could generate valid hypotheses from noisy and complicated data in a system about which there is little previous knowledge.     

Simulation of aquatic food web and species interactions by adaptive agents embodied with evolutionary computation: a conceptual framework

Individual-based and state variable-based adaptive agents (AA) are discussed regarding their relevance to different types of ecosystems. Individual-based AA proved applicable to a spatially explicit simulation of highly simplified terrestrial food webs. State variable-based AA with evolutionary computation (EC) embodied are suggested for the simulation of aquatic food webs and plankton species interactions. Embodiment of EC in AA can be achieved by evolving predictive rules (ER), differential equations (EDE) or artificial neural networks (ANN) derived from a diverse lake database. In order to provide ecosystem simulation with resilience to environmental change, agent banks can be created containing alternative agents for same species or functional groups from different lakes. State variable-based AA are currently tested for aquatic ecosytem simulation by means of a diverse lake database. It promises to overcome constraints by the rigidity of traditional lake ecosystem models.     

On the risk of extinction of a wild plant species through spillover of a biological control agent: Analysis of an ecosystem compartment model

Invasive plant species can be controlled by introducing natural enemies (insect herbivores) from their native range. However, such introduction entails the risk that the introduced herbivores attack indigenous plant species in the area of introduction. Here, we study the effect of spillover of a herbivore from a managed ecosystem compartment (agriculture) to a natural compartment (non-managed) and vice versa. In the natural compartment, an indigenous plant species is attacked by the introduced herbivores, whereas another indigenous plant species, which competes with the first, is not attacked. The combination of competition and herbivory may result in extinction of the attacked wild plant species. Using a modelling approach, we determine model parameters that characterize the risk of extinction for a wild plant species. Risk factors include: (1) a high attack rate of the herbivores on the wild non-target species, (2) niche overlap expressed as strong competition between the attacked non-target species and its competitor(s), and (3) factors favouring large spillover from the managed ecosystem compartment to the natural compartment; these include (3a) a high dispersal ability, and (3b) a moderate attack rate of the introduced herbivore on the target species, enabling large resident populations of the insect herbivore in the managed compartment. The analysis thus indicates that a high attack rate on the target species, which is a selection criterion for biocontrol agents with respect to their effectiveness, also mitigates risks resulting from spillover and non-target effects. While total eradication of an invasive plant species is not possible in the one-compartment-one-plant-one-herbivore system, natural enemy spillover from a natural to a managed compartment can make the invasive weed go extinct.     

Optimization of an artificial neural network for identifying fishing set positions from VMS data: An example from the Peruvian anchovy purse seine fishery

The spatial behavior of numerous fishing fleets is nowadays well documented thanks to satellite Vessel Monitoring Systems (VMS). Vessel positions are recorded on a frequent and regular basis which opens promising perspectives for improving fishing effort estimation and management. However, no specific information is provided on whether the vessel is fishing or not. To answer that question, existing works on VMS data usually apply simple criteria (e.g. threshold on speed). Those simple criteria generally focus in detecting true positives (a true fishing set detected as a fishing set); conversely, estimation errors are given no attention. For our case study, the Peruvian anchovy fishery, those criteria overestimate the total number of fishing sets by 182%. To overcome this problem an artificial neural network (ANN) approach is presented here. In order to set both the optimal parameterization and use “rules” for this ANN, we perform an extensive sensitivity analysis on the optimization of (1) the internal structure and training algorithm of the ANN and (2) the “rules” used for choosing both the relative size and the composition of the databases (DBs) used for training and inferring with the ANN. The “optimized” ANN greatly improves the estimates of the number and location of fishing events. For our case study, ANN reduces the total estimation error on the number of fishing sets to 1% (in average) and obtains 76% of true positives. This spatially explicit information on effort, provided with error estimation, should greatly reduce misleading interpretations of catch per unit effort and thus significantly improve the adaptive management of fisheries. While fitted on Peruvian anchovy fishery data, this type of neural network approach has wider potential and could be implemented in any fishery relying on both VMS and at-sea observer data. In order to increase the accuracy of the ANN results, we also suggest some criteria for improving sampling design by at-sea observers and VMS data.