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A fuzzy logic expert system for evaluating policy progress towards sustainability goals.

ABSTRACT: Evaluating progress towards environmental sustainability goals can be difficult due to a lack of measurable benchmarks and insufficient or uncertain data. Marine settings are particularly challenging, as stakeholders and objectives tend to be less well defined and ecosystem components have high natural variability and are difficult to observe directly. Fuzzy logic expert systems are useful analytical frameworks to evaluate such systems, and we develop such a model here to formally evaluate progress towards sustainability targets based on diverse sets of indicators. Evaluation criteria include recent (since policy enactment) and historical (from earliest known state) change, type of indicators (state, benefit, pressure, response), time span and spatial scope, and the suitability of an indicator in reflecting progress toward a specific objective. A key aspect of the framework is that all assumptions are transparent and modifiable to fit different social and ecological contexts. We test the method by evaluating progress towards four Aichi Biodiversity Targets in Canadian oceans, including quantitative progress scores, information gaps, and the sensitivity of results to model and data assumptions. For Canadian marine systems, national protection plans and biodiversity awareness show good progress, but species and ecosystem states overall do not show strong improvement. Well-defined goals are vital for successful policy implementation, as ambiguity allows for conflicting potential indicators, which in natural systems increases uncertainty in progress evaluations. Importantly, our framework can be easily adapted to assess progress towards policy goals with different themes, globally or in specific regions.

SUBMITTER: Cisneros-Montemayor AM

PROVIDER: S-EPMC6072633 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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A fuzzy logic expert system for evaluating policy progress towards sustainability goals.

Cisneros-Montemayor Andrés M AM Singh Gerald G GG Cheung William W L WWL

Ambio 20171216 5

Evaluating progress towards environmental sustainability goals can be difficult due to a lack of measurable benchmarks and insufficient or uncertain data. Marine settings are particularly challenging, as stakeholders and objectives tend to be less well defined and ecosystem components have high natural variability and are difficult to observe directly. Fuzzy logic expert systems are useful analytical frameworks to evaluate such systems, and we develop such a model here to formally evaluate progr ...[more]

PMID: 29249050

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Project description:BackgroundEthiopia signed the VISION 2020 Global Declaration and launched its eye health program in 2002. Since then, there has been limited systematic and comprehensive evaluation of the progress towards VISION 2020 goals in Ethiopia.ObjectiveTo evaluate Gurage Zone progress towards VISION 2020 targets and process indicators.MethodAn institutional-based cross-sectional study was conducted among all public and private eye health care facilities in the Gurage Zone within the Southern Nations, Nationalities, and People Region of Ethiopia. The evaluation protocol was adopted from the VISION 2020 situational analysis data collection tool. We used this structure to evaluate progress in terms of human resources for eye health, infrastructure, and service delivery at the zonal health office and health facilities. At the time of the study, Gurage Zone had a 1.7 million catchment area population. There were a total of five eye care centers, one of which was established by a non-governmental organization. Three of these facilities were secondary eye care centers with an operating theatre and two facilities were primary eye care centers. At the zonal level, there was no survey data available on the prevalence of blindness.ResultThere was no systemic evaluation of VISION 2020 process indicators. The budget allocation specific to eye health care was less than 0.7% of the total budget of the zonal health office. The human resources for eye health (HReH) in the catchment area were: one ophthalmologist, two cataract surgeons, five optometrists, and 12 ophthalmic nurses, which is below the VISION 2020 targets for HReH. In terms of equipment, neither primary eye care center had a slit lamp biomicroscope, and two of the three secondary eye care centers did not have intraocular pressure measuring equipment. Only one secondary eye care center was providing glaucoma surgical services, and no center provided emergency and elective pediatric surgery. The cataract surgical rate determined by the study was 1967.ConclusionGurage Zone showed significant improvement in terms of cataract surgical rate. But it had not achieved VISION 2020 goals in terms of critical HReH and service delivery. We recommend that the zonal health office carries out a focused and baseline evaluation of eye health care service achievements.

Project description:Similarity measure, also called information measure, is a concept used to distinguish different objects. It has been studied from different contexts by employing mathematical, psychological, and fuzzy approaches. Image steganography is the art of hiding secret data into an image in such a way that it cannot be detected by an intruder. In image steganography, hiding secret data in the plain or non-edge regions of the image is significant due to the high similarity and redundancy of the pixels in their neighborhood. However, the similarity measure of the neighboring pixels, i.e., their proximity in color space, is perceptual rather than mathematical. Thus, this paper proposes an interval type-2 fuzzy logic system (IT2 FLS) to determine the similarity between the neighboring pixels by involving an instinctive human perception through a rule-based approach. The pixels of the image having high similarity values, calculated using the proposed IT2 FLS similarity measure, are selected for embedding via the least significant bit (LSB) method. We term the proposed procedure of steganography as 'IT2 FLS-LSB method'. Moreover, we have developed two more methods, namely, type-1 fuzzy logic system based least significant bits (T1FLS-LSB) and Euclidean distance based similarity measures for least significant bit (SM-LSB) steganographic methods. Experimental simulations were conducted for a collection of images and quality index metrics, such as PSNR (peak signal-to-noise ratio), UQI (universal quality index), and SSIM (structural similarity measure) are used. All the three steganographic methods are applied on dataset and the quality metrics are calculated. The obtained stego images and results are shown and thoroughly compared to determine the efficacy of the IT2 FLS-LSB method. We have also demonstrated the high payload capacity of our proposed method. Finally, we have done a comparative analysis of the proposed approach with the existing well-known steganographic methods to show the effectiveness of our proposed steganographic method.

Project description:Experts use knowledge to infer the distribution of species based on fuzzy logical assumptions about the relationship between species and the environment. Thus, expert knowledge is amenable to fuzzy logic modelling, which give to propositions a continuous truth value between 0 and 1. In species distribution modelling, fuzzy logic may also be used to model, from a number of records, the degree to which conditions are favourable to the occurrence of a species. Therefore, fuzzy logic operations can be used to compare and combine models based on expert knowledge and species records. Here, we applied fuzzy logic modelling to the distribution of amphibians in Uruguay as inferred from expert knowledge and from observed records to infer favourable locations, with favourability being the commensurable unit for both kinds of data sources. We compared the results for threatened species, species considered by experts to be ubiquitous, and non-threatened, non-ubiquitous species. We calculated the fuzzy intersection of models based on both knowledge sources to obtain a unified prediction of favourable locations. Models based on expert knowledge involved a larger number of variables and were less affected by sampling bias. Models based on experts had the same overprediction rate for the three types of species, whereas models based on species records had a lower prediction rate for ubiquitous species. Models based on expert knowledge performed equally as well or better than corresponding models based on species records for threatened species, even when they had to discriminate and classify the same set of records used to build the models based on species records. For threatened species, expert models predicted more restrictive favourable territories than those predicted based on records. Observed records generated the best-fitted models for non-threatened non-ubiquitous species, and ubiquitous species. Fuzzy modelling permitted the objective comparison of the potential of expert knowledge and incomplete distribution records to infer the territories favourable for different species. Distribution of threatened species was able to be better explained by subjective expert knowledge, while for generalist species models based on observed data were more accurate. These results have implications for the correct use of expert knowledge in conservation planning.

Dataset Information

A fuzzy logic expert system for evaluating policy progress towards sustainability goals.

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A fuzzy logic expert system for evaluating policy progress towards sustainability goals.

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