Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Foundations of Fuzzy Systems
Uncertainty in the environmental modelling process - A framework and guidance
Environmental Modelling & Software
Computational Intelligence: Methods and Techniques
Computational Intelligence: Methods and Techniques
A formal framework for scenario development in support of environmental decision-making
Environmental Modelling & Software
Knowledge-based versus data-driven fuzzy habitat suitability models for river management
Environmental Modelling & Software
Advanced Data Mining Techniques
Advanced Data Mining Techniques
Shallow water numerical model of the wave generated by the Vajont landslide
Environmental Modelling & Software
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Extreme torrent events in alpine regions have clearly shown a variety of process patterns involving morphological changes due to increased local erosion and deposition phenomena, and clogging of critical flow sections due to woody material accumulations. Simulation models and design procedures currently used in hazard and risk assessment are only partially able to explain these hydrological cause-effect relationships because the selection of appropriate and reliable scenarios still remains unsolved. Here we propose a scenario development technique, based on a system loading level and a system response level. By Formative Scenario Analysis we derived well-defined sets of assumptions about possible system dynamics at selected critical stream configurations that allowed us to reconstruct in a systematic manner the underlying loading mechanisms and the induced system responses. The derived system scenarios are a fundamental prerequisite to assure quality throughout the hazard assessment process and to provide a coherent problem setting for risk assessment. The proposed scenario development technique has proven to be a powerful modelling framework for the necessary qualitative and quantitative knowledge integration, and for coping with the underlying uncertainties, which are considered to be a key element in natural hazards risk assessment.