Agricultural Water Allocation by Integration of Hydro-Economic Modeling with Bayesian Networks and Random Forest Approaches

Water Resources Management - Sustainable utilization of water resources requires preventive measures that must be taken to promote optimal use of water resources together with consideration of...     

An expert system for predicting shear stress distribution in circular open channels using gene expression programming

The shear stress distribution in circular channels was modeled in this study using gene expression programming (GEP). 173 sets of reliable data were collected under four flow conditions for use in the training and testing stages. The effect of input variables on GEP modeling was studied and 15 different GEP models with individual, binary, ternary, and quaternary input combinations were investigated. The sensitivity analysis results demonstrate that dimensionless parameter y/P, where y is the transverse coordinate, and P is the wetted perimeter, is the most influential parameter with regard to the shear stress distribution in circular channels. GEP model 10, with the parameter y/P and Reynolds number (Re) as inputs, outperformed the other GEP models, with a coefficient of determination of 0.7814 for the testing data set. An equation was derived from the best GEP model and its results were compared with an artificial neural network (ANN) model and an equation based on the Shannon entropy proposed by other researchers. The GEP model, with an average RMSE of 0.0301, exhibits superior performance over the Shannon entropy-based equation, with an average RMSE of 0.1049, and the ANN model, with an average RMSE of 0.2815 for all flow depths.     

Lack of progress in tourist information search research: a critique of citation behaviour and knowledge development

ABSTRACT

Knowledge construction is a crucial factor in the development of any field of study. While empirical papers sustain knowledge development in a field, systematic analysis of the literature and critiques of influential papers are core components of high-level knowledge construction. The aim of this paper is to critically evaluate Fodness and Murray’s ([1999]. A model of tourist information search behavior. Journal of Travel Research, 37(3), 220–230.) seminal paper on information search and to assess how the model has been developed by the subsequent literature. A critique of the model highlighted that the original work had a number of shortcomings, particularly given recent advances in information technologies. The method consisted of a meta-analysis of 575 studies that cited Fodness and Murray’s work. The meta-analysis demonstrated that none of the subsequent studies attempted to further develop this well-cited model. The analysis also highlighted that many of the citations to the original study were superficial, and in some cases, incorrect, raising serious questions about citation practices in the tourism field more broadly. The main implication is that, in order to advance our understanding of tourism, we need robust studies that confirm and build on past research efforts to move the field forward.     

European Multi Model Ensemble (EMME): A New Approach for Monthly Forecast of Precipitation

Regarding the ability of data mining algorithms for post-processing the output of climate models, and on the other hand, the successful application of multi-model ensemble approaches in climate forecasts, in this paper, some important data mining algorithms are evaluated for the monthly forecast of precipitation over Iran. For this purpose, four European climate models, from DWD, ECMWF, CMCC and Meteo-France, with six lead times, are used to be post-processed by applying four different algorithms including artificial neural networks, support vector regression, decision tree and random forests. Based on the proposed approach, 72 different models are provided for 12 months, each month with six lead times. The approach is applied for the monthly forecast of precipitation over Iran. According to the results, the neural network and random forest methods performed better than the decision tree and the support vector machine. This advantage preserved for all months of the year. Also, the proposed multi-model approach outperformed any of the individual European models.

     

Combining Radial Basis Function Neural Network Models and Inclusive Multiple Models for Predicting Suspended Sediment Loads

Water Resources Management - An important issue in water engineering is predicting suspended sediment load (SSL). For the Telar River and its tributaries, this study employs an inclusive multiple...     

Social Economics and Evolutionary Learning

The major premise of this paper is that social and individual well-being depends significantly on people's capacity to learn and unlearn in communication with each other. This paper builds on social economic traditions that see communication and conversation as evolutionary generative and adaptive mechanisms through which individual and social learning occurs. Drawing on educational psychology and organizational behavior scholarship, five dynamic processes of conversational learning are introduced with the contention that they can help social economists understand at a micro level more deeply and more concretely how learning happens in the give-and-take of conversation. The paper explores the role of the state, organizations, and communities in fostering individual freedom and dignity, human rights, and economic democracy and concludes that the investment of value in people and their capability for purposeful action as social economic stakeholders can be enhanced through conversation as learning.