Artificial Neural Networks for Coastal and Ocean studies

نویسنده

  • Pooja Jain
چکیده

Artificial neural network (ANN) is a major tool in artificial intelligence computing and it is also extensively used across all disciplines of coastal and ocean engineering including offshore, deep-ocean and marine engineering. A review of important research studies reported so far in these areas is presented. ANN in general has provided either substitutive or complementary option to traditional computational schemes of statistical regression, time series analysis, pattern matching and numerical methods. A reduced data requirement in case of ANN applications was also noticed in some cases. In future advanced and hybrid ANN architectures are likely to be applied in new and unexplored areas of applications. Similarly attempts are likely to be made to tackle issues such as large variations in input, longer prediction intervals and prediction of extremes by extrapolation beyond the sample time. A recent application of the ANN technology to forecast waves in real time sense over the vast coastline of India has been briefly described.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Estimation of Daily Evaporation Using of Artificial Neural Networks (Case Study; Borujerd Meteorological Station)

Evaporation is one of the most important components of hydrologic cycle.Accurate estimation of this parameter is used for studies such as water balance,irrigation system design, and water resource management. In order to estimate theevaporation, direct measurement methods or physical and empirical models can beused. Using direct methods require installing meteorological stations andinstruments ...

متن کامل

Forecasting Industrial Production in Iran: A Comparative Study of Artificial Neural Networks and Adaptive Nero-Fuzzy Inference System

Forecasting industrial production is essential for efficient planning by managers. Although there are many statistical and mathematical methods for prediction, the use of intelligent algorithms with desirable features has made significant progress in recent years. The current study compared the accuracy of the Artificial Neural Networks (ANN) and Adaptive Nero-Fuzzy Inference System (ANFIS) app...

متن کامل

پیش یابی ارتفاع موج شاخص در خلیج فارس با استفاده از شبکه های عصبی مصنوعی و مقایسه آن با درخت های تصمیم رگرسیونی

Prediction of wave height is of great importance in marine and coastal engineering. In this study, the performances of artificial neural networks (feed forward with back propagation algorithm) for online significant wave heights prediction, in Persian Gulf, were investigated. The data set used in this study comprises wave and wind data gathered from shallow water location in Persian Gulf. Curre...

متن کامل

Prediction the Return Fluctuations with Artificial Neural Networks' Approach

Time changes of return, inefficiency studies performed and presence of effective factors on share return rate are caused development modern and intelligent methods in estimation and evaluation of share return in stock companies. Aim of this research is prediction of return using financial variables with artificial neural network approach. Therefore, the statistical population of this study incl...

متن کامل

Application of Neural Networks in Coastal Engineering – an Overview

Artificial Neural Network (ANN) is being applied to solve a wide variety of coastal/ocean engineering problems. In practical terms ANNs are non-linear modeling tools and they can be used to model complex relationship between the input and output system. In addition, ANNs have a very high degree of freedom and are very simple to train the system for any number of input values, which makes the ne...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2008