Refugees and residents: densities and habitat preferences of lorikeets in urban Melbourne

Lorikeet densities were measured across four habitat types in urban Melbourne. Musk Glossopsitta concinna and Rainbow Lorikeets Trichoglossus haematodus were shown to preferentially use established streetscapes with predominantly native vegetation. The high densities of Musk Lorikeets recorded possibly reflect a paucity of flowering in Victorian BoxIronbark forests during the autumnlwinter of 2002 and the availability of supplementary nectar resources in the urban environment. Future planting decisions in recently developed streetscapes will dictate the long-term resource potential for lorikeets and other nectarivores in urban Melbourne.

Culture-based fisheries in small reservoirs in northern Vietnam: effect of stocking densities and species combinations

This study was conducted in 20 reservoirs, ranging in size from 4 to 30 ha, in the mountainous, northern region of Vietnam, in ThaiNguyen and YenBai provinces, over two growth cycles in 2002/03 and 2003/04. The reservoirs are leased by farmers for fishery activities from the provincial administration, and the trials were managed by the lessee farmers. Three species combinations in ratios (by fingerling weight) of grass carp: silver carp: bighead carp: common carp: mrigal 1:2:1:1:3 (A), 1:3:1:1:2 (B) and 1:2:1:1:2 (C) were used as seed stock. The overall mean yield of stocked fish in 2002/03 and 2003/04 growth cycles in reservoirs in ThaiNguyen and YenBai provinces was 165 (±21) and 190 (39), and 287 (±22) and 325 (±24) kg ha⁻¹ respectively. The yield in reservoirs in both provinces, in both growth cycles and irrespective of the species combinations, increased in relation to stocking density. In reservoirs in ThaiNguyen Province, the species combination B gave the lowest yield (both growth cycles and overall), and differed significantly (P<0.05) from combinations A and C. The stocking efficiency (ratio of the yield of stocked fish in kg ha⁻¹ to the weight of the stocked fish in kg ha⁻¹) in reservoirs in ThaiNguyen Province ranged from 2.9 to 5.1 over the two growth cycles and that in YenBai from 2.8 to 3.9. There was no discernible trend between growth cycles and/or between species combinations. The major cost incurred was for fingerling procurement. In all instances, a net profit was accrued. The mean (±standard error (SE)) net profit ha⁻¹ (in 103VN dong; approximately 15 500 VND=1 US$) was 885 (±270) and 864 (±214), and 1322 (±176) and 1600 (±150) for the growth cycles 2002/03 and 2003/04 for reservoirs in ThaiNguyen and YenBai provinces respectively. Between the two growth cycles, the net profit increased in eight and three reservoirs from YenBai and ThaiNguyen, respectively, the maximum increase being recorded in Khuan Gio (165%) and Dong Man (39%) reservoirs.

Measuring expectations: forecast vs. ideal expectations. Does it really matter?

Consumer’s participation in service delivery is so central to cognition that it affects consumer’s quality evaluations. The study presented in this paper investigates the ways that visitor expectations change as a result of first hand experience with a service in the context of a major art exhibition. The research design allowed for two operational definitions of expectations, namely forecast and ideal expectations, in order to investigate differences between respondents’ pre and post experiences with a service. A total of 550 respondent visitors were interviewed during a major art exhibition, using two questionnaires delivered to two sub samples of respondents. The primary questionnaire was designed to capture recalled expectations after visitation while the parallel questionnaire captured forecast expectations prior to visitation and perceptions in the post experience phase. The findings suggest that forecast expectations were different to ideal expectations in both qualitative and quantitative ways and that these differences had important implications for perceptions of service quality. These differences can be explained, at least in part, by the way that expectations are formed and by the way that expectations are shaped by the actual visitation experience. For market researchers, the question of when and how to measure expectations has important implications for research design.

The forecast quality of CBOE implied volatility indexes

We examine the forecast quality of Chicago Board Options Exchange (CBOE) implied volatility indexes based on the Nasdaq 100 and Standard and Poor's 100 and 500 stock indexes. We find that the forecast quality of CBOE implied volatilities for the S&P 100 (VXO) and S&P 500 (VIX) has improved since 1995. Implied volatilities for the Nasdaq 100 (VXN) appear to provide even higher quality forecasts of future volatility. We further find that attenuation biases induced by the econometric problem of errors in variables appear to have largely disappeared from CBOE volatility index data since 1995.

Densities of individually marked migrants away from the marking site to estimate population sizes : a test with three wader populations

Capsule Population estimates based on the mark–resighting method can be a useful alternative to population-wide counts.

Aims To investigate whether the mark–resighting method can be used as an alternative to counts to estimate the size of wader populations.

Methods Individual colour-marking and subsequent resightings allowed accurate estimates of annual survival for three populations of waders, on which basis we could estimate the actual number of marked birds alive. Densities of marked birds were determined on sites away (2000–4300 km) from the ringing locations expecting marked birds to be randomly distributed among non-marked conspecifics. Population sizes are estimated by combining these densities with the number of marked birds alive.

Results We found indications that the distribution of marked birds was indeed random in the locations away from the site of marking. The estimated population size of Red Knot Calidris canutus canutus was in accordance with the most recent estimates based on counts. Our estimate of the Calidris c. islandica population was somewhat lower, and that of the Bar-tailed Godwit Limosa lapponica taymyrensis population was considerably lower than the latest estimates based on counts.

Conclusion Population estimates based on the mark–resighting method can be a useful alternative for, or addition to, population-wide counts, as long as the assumption of random distribution of marked birds at the reading sites is taken into account. We conclude that the Afro-Siberian Bar-tailed Godwit population has recently decreased in size or has been substantially overestimated during the counts.

Evaluation and comparison of type reduction algorithms from a forecast accuracy perspective

A variety of type reduction (TR) algorithms have been proposed for interval type-2 fuzzy logic systems (IT2 FLSs). The focus of existing literature is mainly on computational requirements of TR algorithm. Often researchers give more rewards to computationally less expensive TR algorithms. This paper evaluates and compares five frequently used TR algorithms from a forecasting performance perspective. Algorithms are judged based on the generalization power of IT2 FLS models developed using them. Four synthetic and real world case studies with different levels of uncertainty are considered to examine effects of TR algorithms on forecasts accuracies. It is found that Coupland-Jonh TR algorithm leads to models with a better forecasting performance. However, there is no clear relationship between the width of the type reduced set and TR algorithm.

State dependent asymmetric loss and the consensus forecast of real U.S. GDP growth

It has been well documented that the consensus forecast from surveys of professional forecasters shows a bias that varies over time. In this paper, we examine whether this bias may be due to forecasters having an asymmetric loss function. In contrast to previous research, we account for the time variation in the bias by making the loss function depend on the state of the economy. The asymmetry parameter in the loss function is specified to depend on set state variables which may cause forecaster to intentionally bias their forecasts. We consider both the Lin–Ex and asymmetric power loss functions. For the commonly used Lin–Ex and Lin–Lin loss functions, we show the model can be easily estimated by least squares. We apply our methodology to the consensus forecast of real U.S. GDP growth from the Survey of Professional Forecasters. We find that forecast uncertainty has an asymmetric effect on the asymmetry parameter in the loss function dependent upon whether the economy is in expansion or contraction. When the economy is in expansion, forecaster uncertainty is related to an overprediction in the median forecast of real GDP growth. In contrast, when the economy is in contraction, forecaster uncertainty is related to an underprediction in the median forecast of real GDP growth. Our results are robust to the particular loss function that is employed in the analysis.

Aggregation of Pi-based forecast to enhance prediction accuracy

In contrast to point forecast, prediction interval-based neural network offers itself as an effective tool to quantify the uncertainty and disturbances that associated with process data. However, single best neural network (NN) does not always guarantee to predict better quality of forecast for different data sets or a whole range of data set. Literature reported that ensemble of NNs using forecast combination produces stable and consistence forecast than single best NN. In this work, a NNs ensemble procedure is introduced to construct better quality of Pis. Weighted averaging forecasts combination mechanism is employed to combine the Pi-based forecast. As the key contribution of this paper, a new Pi-based cost function is proposed to optimize the individual weights for NN in combination process. An optimization algorithm, named simulated annealing (SA) is used to minimize the PI-based cost function. Finally, the proposed method is examined in two different case studies and compared the results with the individual best NNs and available simple averaging Pis aggregating method. Simulation results demonstrated that the proposed method improved the quality of Pis than individual best NNs and simple averaging ensemble method.

A novel fuzzy multi-objective framework to construct optimal prediction intervals for wind power forecast

The forecasting behavior of the high volatile and unpredictable wind power energy has always been a challenging issue in the power engineering area. In this regard, this paper proposes a new multi-objective framework based on fuzzy idea to construct optimal prediction intervals (Pis) to forecast wind power generation more sufficiently. The proposed method makes it possible to satisfy both the PI coverage probability (PICP) and PI normalized average width (PINAW), simultaneously. In order to model the stochastic and nonlinear behavior of the wind power samples, the idea of lower upper bound estimation (LUBE) method is used here. Regarding the optimization tool, an improved version of particle swam optimization (PSO) is proposed. In order to see the feasibility and satisfying performance of the proposed method, the practical data of a wind farm in Australia is used as the case study.

Incorporating wind power forecast uncertainties into stochastic unit commitment using neural network-based prediction intervals

Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

Improving load forecast accuracy by clustering consumers using smart meter data

Utility companies provide electricity to a large number of consumers. These companies need to have an accurate forecast of the next day electricity demand. Any forecast errors will result in either reliability issues or increased costs for the company. Because of the widespread roll-out of smart meters, a large amount of high resolution consumption data is now accessible which was not available in the past. This new data can be used to improve the load forecast and as a result increase the reliability and decrease the expenses of electricity providers. In this paper, a number of methods for improving load forecast using smart meter data are discussed. In these methods, consumers are first divided into a number of clusters. Then a neural network is trained for each cluster and forecasts of these networks are added together in order to form the prediction for the aggregated load. In this paper, it is demonstrated that clustering increases the forecast accuracy significantly. Criteria used for grouping consumers play an important role in this process. In this work, three different feature selection methods for clustering consumers are explained and the effect of feature extraction methods on forecast error is investigated.