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Artificial Intelligence Tools to Forecast Ocean Waves in Real Time
Pooja Jain and M.C. Deo
The Open Ocean Engineering Journal , 2008, DOI: 10.2174/1874835X00801010013]
Abstract: Prediction of wind generated ocean waves over short lead times of the order of some hours or days is helpful in carrying out any operation in the sea such as repairs of structures or laying of submarine pipelines. This paper discusses an application of different artificial intelligent tools for this purpose. The physical domain where the wave forecasting is made belongs to the western part of the Indian coastline in Arabian Sea. The tools used are artificial neural networks, genetic programming and model trees. Station specific forecasts are made at those locations where wave data are continuously observed. A time series forecasting scheme is employed. Based on a sequence of preceding observations forecasts are made over lead times of 3 hr to 72 hr. Large differences in the accuracy of the forecasts were not seen when alternative forecasting tools were employed and hence the user is free to use any one of them as per her convenience and confidence. A graphical user interface has been developed that operates on the received wave height data from the field and produces the forecasts and further makes them accessible to any user located anywhere in the world.
Wind speed prediction using different computing techniques
Munir Ahmad Nayak,M C Deo
Statistics , 2014,
Abstract: Wind is slated to become one of the most sought after source of energy in future. Both onshore as well as offshore wind farms are getting deployed rapidly over the world. This paper evaluates a neural network based time series approach to predict wind speed in real time over shorter duration of up to 12 hr based on analysis of three hourly wind data collected through a wave rider buoy deployed off Goa in deep water and far away from the shore. The data were collected for 4 years from February 1998 to February 2002. A simple feed forward type of network trained using a variety of algorithms was used. The input nodes selected by trial were three in number and belonged to the segment of preceding observations while the output node was single and it consisted of the predicted value of the wind speed over the subsequent 3, 6 and 12 hours one at a time. The number of hidden nodes was based on trials. The total sample was divided into a training set (first 70 percent) and a testing set (remaining 30 percent). The outcome of the network was compared with the actual observations with the help of scatter diagrams and time history plots as well as through the error statistics of the correlation coefficient, R, and mean square error, MSE. The testing of the network showed that it predicted the wind speed in a very satisfactory manner with R = 0.99 and MSE = 0.30 (m/s)2 for a 3 hour ahead prediction while these values for a 12 hour ahead predictions were 0.96 and 1.19 (m/s)2, respectively. Such a prediction based on neural network was found to be superior to that based on polynomial fittings as well as ARMA models. ARIMA models were also used but the predicted values showed significant lag.
Predicting Sea Surface Temperatures in the North Indian Ocean with Nonlinear Autoregressive Neural Networks
Kalpesh Patil,M. C. Deo,Subimal Ghosh,M. Ravichandran
International Journal of Oceanography , 2013, DOI: 10.1155/2013/302479
Abstract: Prediction of monthly mean sea surface temperature (SST) values has many applications ranging from climate predictions to planning of coastal activities. Past studies have shown usefulness of neural networks (NNs) for this purpose and also pointed to a need to do more experimentation to improve accuracy and reliability of the results. The present work is directed along these lines. It shows usefulness of the nonlinear autoregressive type of neural network vis-à-vis the traditional feed forward back propagation type. Neural networks were developed to predict monthly SST values based on 61-year data at six different locations around India over 1 to 12 months in advance. The nonlinear autoregressive (NAR) neural network was found to yield satisfactory predictions over all time horizons and at all selected locations. The results of the present study were more attractive in terms of prediction accuracy than those of an earlier work in the same region. The annual neural networks generally performed better than the seasonal ones, probably due to their relatively high fitting flexibility. 1. Introduction The temperature of water at around 1？m below the ocean surface, commonly referred to as sea surface temperature (SST), is an important parameter to understand the exchange of momentum, heat, gases, and moisture across air-sea interface. Its knowledge is necessary to explain and predict important climate and weather processes including the summer monsoon and El-Nino events. SST predictions are sought after by the users of coastal communities dealing with fishing and sports. Like the air above it SST changes significantly over time, although relatively less frequently due to a high specific heat. The changes in water temperature over a vertical are high at the sea surface due to large variations in the heat flux, radiation, and diurnal wind near the surface, and hence SST estimations involve considerable amount of uncertainty. There are a variety of techniques for measuring SST. These include the thermometers and thermistors mounted on drifting or moored buoys and remote sensing by satellites. In case of satellites the ocean radiation in certain wavelengths of an electromagnetic spectrum is sensed and related to SST. Microwave radiometry based on an imaging radiometer called the moderate resolution imaging spectroradiometer is also popularly used to record SST. In order to predict SST physicallybased as well as data driven methods are practiced. The latter type is many times preferred when site specific information is required and considering the convenience. The
Prediction of littoral drift with artificial neural networks
A. K. Singh, M. C. Deo,V. Sanil Kumar
Hydrology and Earth System Sciences (HESS) & Discussions (HESSD) , 2008,
Abstract: The amount of sand moving parallel to a coastline forms a prerequisite for many harbor design projects. Such information is currently obtained through various empirical formulae. Despite so many works in the past an accurate and reliable estimation of the rate of sand drift has still remained as a problem. The current study addresses this issue through the use of artificial neural networks (ANN). Feed forward networks were developed to predict the sand drift from a variety of causative variables. The best network was selected after trying out many alternatives. In order to improve the accuracy further its outcome was used to develop another network. Such simple two-stage training yielded most satisfactory results. An equation combining the network and a non-linear regression is presented for quick field usage. An attempt was made to see how both ANN and statistical regression differ in processing the input information. The network was validated by confirming its consistency with underlying physical process.
Prediction of littoral drift with artificial neural networks
A. K. Singh,M. C. Deo,V. S. Kumar
Hydrology and Earth System Sciences Discussions , 2007,
Abstract: The amount of sand moving parallel to a coastline forms a prerequisite for many harbour design projects. Such information is currently obtained through various empirical formulae. Despite much research in the past an accurate and reliable estimation of the rate of sand drift has still remained as a problem. The current study addresses this issue through the use of artificial neural networks (ANN). Feed forward networks were developed to predict the sand drift from a variety of causative variables. The best network was selected after trying out many alternatives. In order to improve the accuracy further its outcome was used to develop another network. Such simple two-stage training yielded most satisfactory results. An equation combining the network and a non-linear regression is presented for quick field usage. An attempt was made to see how both ANN and statistical regression differ in processing the input information. The network was validated by confirming its consistency with the underlying physical process.
Aharonov-Bohm effect in the presence of evanescent modes
B. C. Gupta,P. Singha Deo,A. M. Jayannavar
Physics , 1996, DOI: 10.1142/S021797929600194X
Abstract: It is known that differential magnetoconductance of a normal metal loop connected to reservoirs by ideal wires is always negative when an electron travels as an evanescent modes in the loop. This is in contrast to the fact that the magnetoconductance for propagating modes is very sensitive to small changes in geometric details and the Fermi energy and moreover it can be positive as well as negative. Here we explore the role of impurities in the leads in determining the magnetoconductance of the loop. We find that the change in magnetoconductance is negative and can be made large provided the impurities do not create resonant states in the systems. This theoretical finding may play an useful role in quantum switch operations.
Undergraduate medical students′ research in India
Deo M
Journal of Postgraduate Medicine , 2008,
Abstract:
Observation of beta decay of In-115 to the first excited level of Sn-115
C. M. Cattadori,M. De Deo,M. Laubenstein,L. Pandola,V. I. Tretyak
Physics , 2004, DOI: 10.1016/j.nuclphysa.2004.10.025
Abstract: In the context of the LENS R&D solar neutrino project, the gamma spectrum of a sample of metallic indium was measured using a single experimental setup of 4 HP-Ge detectors located underground at the Gran Sasso National Laboratories (LNGS), Italy. A gamma line at the energy (497.48 +/- 0.21) keV was found that is not present in the background spectrum and that can be identified as a gamma quantum following the beta decay of In-115 to the first excited state of Sn-115 (9/2+ --> 3/2+). This decay channel of In-115, which is reported here for the first time, has an extremely low Q-value, Q = (2 +/- 4) keV, and has a much lower probability than the well-known ground state-ground state transition, being the branching ratio b = (1.18 +/- 0.31) 10^-6. This could be the beta decay with the lowest known Q-value. The limit on charge non-conserving beta decay of In-115 is set at 90% C.L. as tau > 4.1 10^20 y.
Beta decay of 115-In to the first excited level of 115-Sn: Potential outcome for neutrino mass
C. M. Cattadori,M. De Deo,M. Laubenstein,L. Pandola,V. I. Tretyak
Physics , 2005, DOI: 10.1134/S1063778807010140
Abstract: Recent observation of beta decay of 115-In to the first excited level of 115-Sn with an extremely low Q_beta value (Q_beta ~ 1 keV) could be used to set a limit on neutrino mass. To give restriction potentially competitive with those extracted from experiments with 3-H (~2 eV) and 187-Re (~15 eV), atomic mass difference between 115-In and 115-Sn and energy of the first 115-Sn level should be remeasured with higher accuracy (possibly of the order of ~1 eV).
Conductance Quantization in a Periodically Modulated Quantum Channel: backscattering and mode mixing
P. Singha Deo,B. C. Gupta,A. M. Jayannavar,F. M. Peeters
Physics , 1997,
Abstract: It is known that the conductance of a quantum point contact is quantized in units of $2e^2/h$ and this quantization is destroyed by a non-adiabatic scatterer in the point contact, due to backscattering. Recently, it was shown [Phys. Rev. Lett. 71, 137 (1993)] that taking many non-adiabatic scatterers periodically in a quantum channel, the quantization can be recovered. We study this conductance quantization of a periodic system in the presence of a strong defect. A periodic arrangement of double-stubs give remarkable quantization of conductance. A periodic arrangement of double-constrictions also gives a very good quantization only when the separation between the constrictions is small. We conclude that conductance quantization of a periodically modulated channel is robust.
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