Fire is the main cause of forest destruction in Mediterranean basin countries. Long-term prediction is intended for long-term planning which may serve to characterize and cluster regions as subject to high or low fire risk. This will enable the development of a rational and sensible forest fire prevention and protection policy. The problem with the existing approaches of long-term forest fire risk clustering is that they use crisp sets applying specific cluster boundaries. On the other hand, fuzzy algebra can provide reliable and flexible means of modeling that can be applied by a suitable decision support system. Given a specific area of interest, the evaluation of the long-term forest fire risk can be performed by the use of a triangular and a trapezoidal membership function. The decision support system that has been developed applies an inference mechanism that is based on various aspects of fuzzy sets and fuzzy machine learning techniques. The system has been applied in Greece, but it can be used on a global basis. Results show that the system successfully estimates the forest fire risky areas.

A decision support system applying an integrated fuzzy model for long- term forest fire risk estimation

This paper proposes a risk assessment model for settling problems of microbiological origin in activated sludge systems (filamentous bulking, foaming and rising sludge). The aim of the model is not to diagnose microbiology-related solids separation problems with absolute certainty but to quantify in dynamic scenarios whether simulated operational procedures and control strategies lead to favourable conditions for them to arise or not. The rationale behind the model (which integrates the mechanisms of standard activated sludge models with empirical knowledge), its implementation in a fuzzy rule-based system and the details of its operation are illustrated in the different sections of the paper. The performance of the risk assessment model is illustrated by evaluating a number of control strategies facing different short-term influent conditions as well as long-term variability using the IWA/COST simulation benchmark. The results demonstrate that some control strategies, although performing better regarding operating costs and effluent quality, induce a higher risk for solids separation problems. In view of these results, it is suggested to integrate empirical knowledge into mechanistic models to increase reliability and to allow assessment of potential side-effects when simulating complex processes.

Risk assessment modelling of microbiology-related solids separation problems in activated sludge systems

One of the important issues related with all types of data analysis, either statistical data analysis, machine learning, data mining, data science or whatever form of data-driven modeling, is data quality. The more complex the reality to be analyzed is, the higher the risk of getting low quality data. Unfortunately real data often contain noise, uncertainty, errors, redundancies or even irrelevant information. Useless models will be obtained when built over incorrect or incomplete data. As a consequence, the quality of decisions made over these models, also depends on data quality. This is why pre-processing is one of the most critical steps of data analysis in any of its forms. However, pre-processing has not been properly systematized yet, and little research is focused on this. In this paper a survey on most popular pre-processing steps required in environmental data analysis is presented, together with a proposal to systematize it. Rather than providing technical details on specific pre-processing techniques, the paper focus on providing general ideas to a non-expert user, who, after reading them, can decide which one is the more suitable technique required to solve his/her problem.

/pdf/a-survey-on-pre-processing-techniques-relevant-issues-in-the-3a25ijhz0h.pdf

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

The present study investigated heavy metal contaminations in Mangonbangon River, an urban river of Tacloban City, Philippines. Sediment samples were collected from 14 sampling sites and analyzed for total heavy metal content using acid digestion. Heavy metal concentrations ranged from 76.83-263.63 mg/Kg for Zn, 32.80-131.82 mg/Kg for Cr, 29.40-217.06 mg/Kg for Cu, 12.08-98.07 mg/Kg for Ni, 4.06-25.34 mg/Kg for Co and 12934.00-27332.00 mg/Kg for Fe. The average enrichment factor (EF) showed moderate and moderately severe enrichment for Zn and Cu, respectively, minor enrichment for Cr, Ni, and Co and no enrichment for Mn. The average contamination factor (CF) of Cu and Zn indicated moderate contamination while Cr, Ni, Mn, Co and Fe showed background concentrations. Results on geoaccumulation index (Igeo) showed that sediments were uncontaminated to moderately contaminated with Cu and Zn whereas Igeo for all other heavy metals indicated no contamination. For the overall pollution, pollution load index (PLI) showed that Mangonbangon River was unpolluted. Based on the comparison of heavy metals concentrations with the consensus-based sediment quality guidelines (SQGs), Zn, Cr, Cu and Ni were likely to result to the deleterious effect on bottom-dwelling organisms. Potential ecological risk index (RI) indicated low ecological risk in the sediments. Multivariate analyses revealed that Cu, Cr, Ni and Fe had common origin or geochemical behaviour and were associated with anthropogenic activities.

http://www.pjoes.com/pdf-75204-26101?filename=Assessing Heavy Metal.pdf

Assessing Heavy Metal Contamination in SurfaceSediments in an Urban River in the Philippines

In this idealized numerical modeling study, the composition of residual sediment fluxes in energetic (e.g., weakly or periodically stratified) tidal estuaries is investigated by means of one-dimensional water column models, with some focus on the sediment availability. Scaling of the underlying dynamic equations shows dependence of the results on the Simpson number (relative strength of horizontal density gradient) and the Rouse number (relative settling velocity) as well as impacts of the Unsteadiness number (relative tidal frequency). Here, the parameter space given by the Simpson and Rouse numbers is mainly investigated. A simple analytical model based on the assumption of stationarity shows that for small Simpson and Rouse numbers sediment flux is down estuary and vice versa for large Simpson and Rouse numbers. A fully dynamic water column model coupled to a second-moment turbulence closure model allows to decompose the sediment flux profiles into contributions from the transport flux (product of subtidal velocity and sediment concentration profiles) and the fluctuation flux profiles (tidal covariance between current velocity and sediment concentration). Three different types of bottom sediment pools are distinguished to vary the sediment availability, by defining a time scale for complete sediment erosion. For short erosion times scales, the transport sediment flux may dominate, but for larger erosion time scales the fluctuation sediment flux largely dominates the tidal sediment flux. When quarter-diurnal components are added to the tidal forcing, up-estuary sediment fluxes are strongly increased for stronger and shorter flood tides and vice versa. The theoretical results are compared to field observations in a tidally energetic inlet.

/pdf/residual-sediment-fluxes-in-weakly-to-periodically-4jpjgecpg1.pdf

Residual sediment fluxes in weakly-to-periodically stratified estuaries and tidal inlets

We design a fuzzy model of the loadings of 10 heavy metals in Liverpool bay. Each metal concentration is associated with a fuzzy set “contaminated”, defined over the set of 70 sampling sites. The higher the concentration, the higher the degree of membership of the site. Six overall loading indices are calculated using aggregation connectives between fuzzy sets. The loading indices are then interpolated and plotted on a map. A visual inspection shows that: (1) product aggregation is most indicative for the locations of the disposal grounds; (2) mean aggregation reflects sediment movement in the bay well; and (3) maximum aggregation indicates all highly contaminated sites. The proposed fuzzy model is easy to implement and the results are directly interpretable.

A fuzzy model of heavy metal loadings in Liverpool bay

We design a fuzzy model of the loadings of heavy metals for two coastal areas of the Irish Sea (Liverpool Bay and Morecambe Bay). Each metal concentration is associated with a fuzzy set “contami­nated” , defined over a set of sampling sites (70 in Liverpool Bay and 203 in Morecambe Bay). The higher the concentration, the higher the degree of membership of the site. Six overall loading indices are cal­culated using aggregation connectives between fuzzy sets. The loading indices are then interpolated and plotted on a map. A visual inspection shows that: (i) product aggregation is most indicative for the locations of the disposal grounds; (ii) mean aggregation reflects well the sediment movement in the bay; (iii) maximum aggregation indicates all highly contaminated sites. The proposed fuzzy model is easy to implement and the results are directly interpretable.

J. Wrench

Papers

A fuzzy model of heavy metal loadings in Liverpool bay

A Fuzzy Model of Heavy Metal Loadings in Marine Environment