The application of fully distributed watershed models has the advantage of providing location-specific outputs. However, the calibration of these models is very challenging due to over-parameterization. A typical strategy is to aggregate parameters and screen out insensitive parameters in order...

This paper concerns situations similar to the ones occurring in the city of Naples, which result in a continuous degradation that destroys resources and detracts from their future usability. The main difficulties are to be found in terms of organisational and managerial skills in integrated...

A Multi objective, Multireservoir operation model for maximization of irrigation releases and maximization of hydropower production is proposed using Genetic Algorithm. These objectives are fuzzified and are simultaneously maximized by defining and then maximizing level of satisfaction (λ). In...

In this paper, urban network design is analysed through a heuristic multi-criteria technique based on genetic algorithms. Both network layout and link capacity (link layout and traffic lights) are optimised. Different optimisation criteria are included for users, non-users and public system...

Artificial neural networks (ANNs) have become common data driven tools for modeling complex, nonlinear problems in science and engineering. Many previous applications have relied on gradient-based search techniques, such as the back propagation (BP) algorithm, for ANN training. Such techniques,...

Decision making to determine government support policy for agro-energy industry can be assisted by mathematical programming and Multiple Criteria procedures. In this case study, tax credit policy in the French bio-fuel industry producing ethanol and esters is determined. Micro-economic models...