The city of Venice is an extraordinary architectural, artistic and cultural heritage.
Unfortunately, its conservation is increasingly threatened by particularly significant high …

This study demonstrates a combined application of chaos theory and support vector
machine (SVM) in the analysis of chaotic time series with a very large sample data record. A …

In the Venice Lagoon some of the highest tides in the Mediterranean occur, which have
influenced the evolution of the city of Venice and the surrounding lagoon for centuries. The …

A new approach to the fast-developing world of neural hydrological modelling, this book is
essential reading for academics and researchers in the fields of water sciences, civil …

Support vector machine (SVM) is one of the most elegant data mining engines developed
most recently. It has been shown in various studies that SVM provides higher accuracy level …

This paper describes a novel approach to river flood prediction using Time Series Data
Mining which combines chaos theory and data mining to characterize and predict events in …

Sparse learning machines provide a viable framework for modeling chaotic time-series
systems. A powerful state-space reconstruction methodology using both support vector …

Rainfall-runoff simulation and forecasting is essential for disaster management, planning,
designing and operation of water resource projects. This paper reviews the till date …

Quantitative analyses of groundwater flow and transport typically rely on a physically‐based
model, which is inherently subject to error. Errors in model structure, parameter and data …

Sea level rise is a threat to coastal habitation and is corroborating evidence for global
warming. The present study investigated the combined use of quantitative forecasting …