Accurate forecasting of streamflow is essential for the efficient operation of water resources systems. The streamflow process is complex and highly nonlinear. Therefore, researchers try to devise alterative techniques to forecast streamflow with relative ease and reasonable accuracy, although traditional deterministic and conceptual models are available. The present work uses three data-driven techniques, namely artificial neural networks (ANN), genetic programming (GP) and model trees (MT) to forecast river flow one day in advance at two stations in the Narmada catchment of India, and the results are compared. All the models performed reasonably well as far as accuracy of prediction is concerned. It was found that the ANN and MT techniques performed almost equally well, but GP performed better than both these techniques, although only marginally in terms of prediction accuracy in normal and extreme events.Key words streamflow; data-driven modelling; artificial neural networks; genetic programming; M5 model trees Comparaison de techniques de modélisation conditionnée par les données pour la prévision des débits fluviaux Résumé La prévision précise des débits est essentielle pour l'opération efficace des systèmes de ressources en eau. Le processus d'écoulement est complexe et fortement non-linéaire. Par conséquent, les chercheurs essaient de mettre au point des techniques alternatives pour prévoir le débit avec une facilité relative et une précision raisonnable, bien que des modèles traditionnels déterministes et conceptuels soient disponibles. Le présent travail utilise trois techniques conditionnées par les données, en l'occurence des réseaux de neurones artificiels (RNA), la programmation génétique (PG) et les arbres modèles (MT) pour prévoir les débits de rivière un jour à l'avance en deux stations du bassin versant de Narmada (Inde), et les résultats sont comparés. Tous les modèles sont assez performants en termes de précision de prévision. Les techniques RNA et MT ont des performances presque égales, mais PG donne de meilleurs résultats, bien que seulement de façon marginale en termes de précision de prévision pour des événements normaux et extrêmes.
The prediction of tidal currents in the coastal region on a real-time or online basis is useful in taking operation-and planning-related decisions such as towing of vessels and monitoring of oil slick movements. Currently, however, this is done in offline mode on the basis of the statistical method of harmonic analysis involving fitting of harmonic functions to measured data. Alternatively, numerical solutions of hydrodynamic models can also provide spatial and temporal information on currents. Owing to the complex real sea conditions, such methods may not always yield satisfactory results. This paper discusses a few alternative approaches based on the soft computing tools of artificial neural networks (ANNs) and genetic programming (GP), as well as the hard mathematical approaches of stochastic and statistical methods. The suggested schemes use only a univariate time series of currents to forecast their future values. The measurements of coastal currents made at two locations in the Gulf of Khambhat along the west coast of India have been analysed. The current predictions over a time step of 20 min, a few hours, and a day at the specified locations were carried out. It was found that the soft computing schemes of GP and ANN performed better than the traditional hard technique of harmonic analysis in the present application. This work should initiate more application of GP in coastal engineering. Addressing the problem of current predictions in real-time mode based on analysis of observed time series of ocean currents is a specialty of this work.
Data leak is a major problem in all the organization of any land. A deliberate risk to institution and private security is the disclosure of secure data in transmission and storage. To check content for exposed sensitive data is the main aim for exposed sensitive data. There are large numbers of data-leak cases but human flaws are one of the main reasons of data leak. This paper proposed a data-leak detection model for preventing accidental and intentional data leak in network. If someone succeed to steal some kind of data and send that data to outsider then data owner has obtain to use two methods to find out guilty employee or leaker. This work suggests use of shingling and rabin filter system performs Data Leak Detection (DLD) and Prevention task. The results show that this approach can be effectively implemented in various organizations; however rigorous testing on various data division of such methods will be required to implement the same in sector of importance like defence and other even in large establishment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.