Wave simulation and forecasting using wind time history and data-driven methods

Simulation and forecasting of significant wave heights and average zero-cross wave periods in real time are done for a specified location, given the past observed sequence of wind speed and wind direction. This is based on time series forecasting implemented using the two recent data-driven methods of genetic programming (GP) and model trees (MT). The wave buoy measurements made at eight different offshore locations around the west as well as the east coast in India are considered. Both genetic programming and model trees perform satisfactorily in the given task of wind-wave simulation and forecasting as reflected in the values of the six different error statistics employed to assess the performance of developed models over testing sets of data. Although the magnitudes of error statistics do not indicate a significant difference between the performance of GP and MT, qualitative scatter diagrams and time histories showed the tendency of MT to estimate higher waves more correctly

Estimation of pile group scour using neural networks

The interaction between ocean environment and pile structure is so complex that despite considerable laboratory as well as prototype studies estimation of scour depth and its geometry in a generalized and accurate form are still difficult to make. One of the reasons underlying this uncertainty could be the limitation of the statistical curve fitting technique, commonly employed to analyse the collected data. The present work therefore attempts to carry out scour data analysis using another technique of data mining: neural networks. Neural networks have ability to map a random input vector with the random output vector in a model-free manner unlike the model oriented non-linear regression methods. Different networks were developed to predict the scour depth as well as scour width for a group of piles supporting a pier situated at a coastal location off Japan using the input of wave height, wave period, water depth and pile diameter as well as pile Reynold's number, maximum wave particle velocity, maximum shear velocity, Shield's parameter and Keulegan–Carpenter number. The networks were of feed forward as well as recurrent type trained using back propagation and cascade correlation algorithms. The testing results showed that the neural network could provide a better alternative to the statistical curve fitting. Individual input parameters yielded better results than their grouped combinations. The depth of scour was predicted more accurately than its width. A matrix of weights is specified for use at any given location.© Elsevie

Hydrodynamic flow modeling and effect of roughness on river stage forecasting

In recent times, undesirable climatic conditions have been attributed to climate change. The intensity of rainfall has amplified extremely, causing floods in many areas worldwide. It is desirable to regulate and minimize the consequences of floods and excess downpour. Using geospatial data for the development of hydraulic models and mapping of simulation results has become standard practice for floodplain assessment. The objective of the current investigation is to use one-dimensional floodplain modeling of the Bhima River between Lonikand and Rahu using the RAS-mapper tool (HEC-RAS). The modeled river reach is about 67 km long, near the Pune administrative division of Maharashtra, India. The hydrodynamic flow computations were carried out for the years 2005 and 2017. A total of 595 cross sections along the main river was employed for hydrodynamic flow simulations. In this study, cross-sections and past observed flood data have been used to develop a 1-D integrated hydraulic model of the Bhima River. The simulated water levels are also validated with observed water levels and found to be reasonably correlated