Liem T. Tran

Comparison of fuzzy numbers using a fuzzy distance measure

A new approach for ranking fuzzy numbers based on a distance measure is introduced. A new class of distance measures for interval numbers that takes into account all the points in both intervals is developed first, and then it is used to formulate the distance measure for fuzzy numbers. The approach is illustrated by numerical examples, showing that it overcomes several shortcomings such as the indiscriminative and counterintuitive behavior of several existing fuzzy ranking approaches.

On the invalidity of fuzzifying numerical judgments in the Analytic Hierarchy Process

Fuzzy set theory has serious difficulties in producing valid answers in decision-making by fuzzifying judgments. No theorems are available about its workability when it is applied indiscriminately as a number crunching approach to numerical measurements that represent judgments. When judgments are allowed to vary in choice over the values of a fundamental scale, as in the Analytic Hierarchy Process, these judgments are themselves already fuzzy. To make them fuzzier can make the validity of the outcome, when the actual outcome is known, worse, as shown by several examples in this paper. Also, improving the consistency of a judgment matrix does not necessarily improve the validity of the outcome. Validity is the goal in decision-making, not consistency, which can be successively improved by manipulating the judgments as the answer gets farther and farther from reality. An example of this is included.

Transpiration in a small tropical forest patch

A field study was conducted of microclimate and transpiration within a 12 ha patch of advanced secondary forest surrounded by active or recently abandoned swidden fields. Differences in microclimate among stations located within and near the patch, give evidence of the effects of the adjacent clearing on the environment in the patch. Volumetric soil moisture content at the end of the dry season was lowest at the two edge sites, suggesting greater cumulative dry season evapotranspiration (ET) there than at swidden and forest interior sites. Total evaporation, based on energy balance methods, was also higher at the two edge sites than at the swidden or forest interior sites. Spatial differences in evaporation decreased as conditions became wetter. Measurements of sap flow in nine trees near the southwestern edge of the patch and nine trees in the patch interior indicate considerable variability in transpiration among the three monitored tree species, Vernicia montana, Alphonsea tonkinensis, and Garcinia planchonii. Dry-period transpiration averaged about 39 and 43% of total evaporation for edge and interior trees, respectively, increasing to 60 and 68% after the start of rains. Transpiration in both zones was well-correlated with micrometeorological conditions in the adjacent clearing, implying that transpiration edge effect is greatest when conditions are favorable for high positive heat advection from the clearing to the forest edge. Transpiration rates of well-exposed trees were higher than poorly-exposed trees, and decreased with distance from the edge at a statistically significant rate of −0.0135 mm per day m −1 . Although the results on the strength of transpiration edge effect are somewhat equivocal due to variability within the small sample, there is clear evidence that ET within the patch is influenced by the surrounding clearings. If edges experience higher ET, greater fragmentation would result in higher regional evaporative flux, which would partly compensate for the reduction in regional ET due to deforestation.

Integrated Environmental Assessment of the Mid-Atlantic Region with Analytical Network Process

A decision analysis method for integrating environmental indicators was developed. This was a combination of Principal Component Analysis (PCA) and the Analytic Network Process (ANP). Being able to take into account the interdependency among variables, the method was capable of ranking ecosystems in terms of environmental conditions and suggesting cumulative impacts across a large region. Using data on land cover, population, roads, streams, air pollution, and topography of the Mid-Atlantic Region of the United States, we were able to point out areas which were in relatively poor condition and/or vulnerable to future deterioration regarding various environmental aspects. The method offered an easy and comprehensive way to combine the strengths of conventional multivariate statistics (PCA) and decision-making science tool (ANP) for integrated environmental assessment.

Predicting shrimp disease occurrence: artificial neural networks vs. logistic regression

Abstract Predicting the occurrence of disease outbreaks in aquacultural farms can be of considerable value to the long-term sustainable development of the industry. Prior research on disease prediction has essentially depended upon traditional statistical models with varying degrees of prediction accuracy. Furthermore, the application of these models in sustainable aquaculture development and in controlling environmental deterioration has been very limited. In an attempt to look for a more reliable model, we developed a probabilistic neural network (PNN) to predict shrimp disease outbreaks in Vietnam using farm-level data from 480 Vietnamese shrimp farms, including 86 semi-intensive and 394 extensive farms. We also compared predictive performance of the PNN against the more traditional logistic regression approach on the same data set. Disease occurrence (a 0–1 variable) is hypothesized to be affected by a set of nearly 70 variables including site characteristics, farming systems, and farm practices. Results show that the PNN model has a better predictive power than the logistic regression model. However, the PNN model uses significantly more input (explanatory) variables than the logistic regression. The logistic regression is estimated using a stepwise procedure starting with the same input variables as in PNN model. Adapting the same input variables found in the logistic regression model to the PNN model yields results no better than the logistic regression model. More importantly, the key factors for prediction in the PNN model are difficult to interpret, suggesting besides prediction accuracy, model interpretation is an important issue for further investigation.

Application of fuzzy logic-based modeling to improve the performance of the Revised Universal Soil Loss Equation

Abstract This paper discusses the application of fuzzy logic-based modeling to improve the performance of the Revised Universal Soil Loss Equation (RUSLE). An analysis of over 1700 plot-years of data, taken from more than 200 plots at 21 sites in the U.S., showed that soil erosion was not adequately described merely by the multiplication of five RUSLE factor values in all cases. The fuzzy logic-based modeling approach was to make the RUSLEs structure more flexible in describing the relationship between soil erosion and other factors and in dealing with data and model uncertainties without requiring any further information. The approach used in this study consisted of two techniques: multi-objective fuzzy regression (MOFR) and fuzzy rule-based modeling (FRBM). First, MOFR was applied to small subsets of RUSLE factor values to derive the relationship between soil loss and the rainfall erosivity factor within each subset of data. These MOFR models, considered as single fuzzy rules, were in turn linked together in a FRBM framework to form a fuzzy rule set. Then the fuzzy rule set was applied to compute the soil loss prediction corresponding to each combination of RUSLE factors. The model efficiency [Journal of Hydrology (Amsterdam) 10 (1970) 282] of the fuzzy model on a yearly basis was 0.70 while the RUSLEs was 0.58. On an average annual basis, the model efficiency was 0.90 and 0.72 for the fuzzy model and the RUSLE, respectively.

Latent and Sensible Energy Flux Over Deforested Land Surfaces in the Eastern Amazon and Northern Thailand

Land cover change may impact watershed hydrology and regional climate by altering landatmosphere exchanges of energy and water. Conversion of forest to pasture has previously been shown to decrease the rate of evaporation (including transpiration) because of reduced aerodynamic roughness, less canopy rainfall interception and greater canopy resistance during dry periods. However, less is known about the effects of forest replacement land covers other than pasture. In this study, field measurements of meteorological processes, stomatal resistance, and Leaf Area Index (LAI) were taken over various deforested land surfaces in the eastern Amazon Basin and in northern Thailand. Based on these measurements, evaporation and sensible heat flux were estimated over each site. Results show the degree to which different land covers replacing tropical forest affect energy partitioning. In comparison with forest, non-irrigated, actively and recently cultivated sites had sharply lower rates of evaporation. Secondary vegetation generally had higher rates of evaporation than actively or recently used sites. The proportion of energy used for evaporation increased rapidly with age in secondary vegetation. Evaporation rates at sites eight and 25 years after abandonment in the Thailand study area were enhanced by positive sensible energy advection from nearby cultivated areas, and were similar to those of primary forest. Differences in dry season evaporation rates of 3-, 8- and 25-year secondary vegetation are explained, in part, by the differences in canopy resistance. Minimum canopy resistance was 223, 61 and 58 s m -1 , respectively at the 3-, 8 - and 25-year sites.

Soil‐vegetation‐atmosphere processes: Simulation and field measurement for deforested sites in northern Thailand

In recent efforts to predict the climatic impacts of tropical deforestation an extreme scenario of impoverished grassland has been used to represent the future deforested landscape. Currently, deforested areas of the tropics are composed of a mosaic of crops, bare soil, grassland, and secondary vegetation of various ages. The dominant feature of deforested land is often secondary vegetation. Parameter values for important forest replacement land covers, including secondary vegetation, have been shown to differ from those of forest much less than that assumed in general circulation model (GCM) deforestation experiments. For this study, the biosphere-atmosphere transfer scheme (BATS) is run in uncoupled mode using measured input data in place of GCM forcing and using the same parameter settings employed in recent deforestation experiments. Model output is compared with measurements taken over seven different deforested land surfaces in northern Thailand. Comparisons reveal that the simulation of deforested land overestimates reflected shortwave radiation, the diurnal range of surface temperature for secondary vegetation, surface soil moisture loss during periods without rain, and surface soil moisture increase at the start of a rainy period and underestimates net radiation, the diurnal range of surface temperature on recently used land surfaces, and root zone soil moisture increase at the start of a rainy period at most sites. Most deforested land surfaces, especially intermediate and advanced secondary vegetation, are more similar, in terms of land surface-atmosphere interaction, to the model simulation of forest than of deforested land as depicted in GCM experiments. These comparisons suggest that modelers aspiring to make realistic simulations of deforestation should adopt parameter settings representative of the diverse range of forest replacement land covers, instead of again using the grassland scenario.

Fuzzy Judgments and Fuzzy Sets

Using fuzzy set theory has become attractive to many people. However, the many references cited here and in other works, little thought is given to why numbers should be made fuzzy before plunging into the necessary simulations to crank out numbers without giving reason or proof that it works to one’s advantage. In fact it does not often do that, certainly not in decision making. Regrettably, many published papers that use fuzzy set theory presumably to get better answers were not judged thoroughly by reviewers knowledgeable in both fuzzy theory and decision making. Buede and Maxwell (1995), who had done experiments on different ways of making decisions, found that fuzzy does the poorest job of obtaining the right decision as compared with other ways. “These experiments demonstrated that the MAVT (Multiattribute Value Theory) and AHP (Analytic Hierarchy Process) techniques, when provided with the same decision outcome data, very often identify the same alternatives as ‘best’. The other techniques are noticeably less consistent with the Fuzzy algorithm being the least consistent.â€

Multicriterion decision support for a conflict over stream diversion and land-water reallocation in Hawaii

With the demise of the Hawaiian island of Oahus sugarcane cultivation in mid-1995, policymakers have been grappling with two related questions: how should former cane lands be used, and what portion, if any, of the diverted flow of windward streams should be returned to those streams. This paper proposes and illustrates a multiobjective methodology to aid decision making regarding this and similar problems. It consists of the elicitation of concerns and the structuring of criterion hierarchies, the use of multicriterion optimization to formulate potential solutions, and the employment of discrete multicriterion methods to compare and evaluate those proposals. A discussion of the institutionalization of multicriterion decision support concludes the paper.