An application of artificial intelligence for rainfall-runoff modeling
نویسندگان
چکیده
منابع مشابه
Application of Artificial Neural Networks for Rainfall-runoff Modelling
Rainfall-Runoff models are mostly empirical in nature demanding the knowledge of a large number of catchment parameters. On the contrary Artificial Neural Networks (ANN) can be deployed in cases where the available data is limited. The present work involves the development of an ANN model using Backward Propagation algorithm. The hydrologic variables used were Rainfall, Soil Moisture, Evaporati...
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در ایران بیمه خودرو سهم بزرگی در صنعت بیمه دارد. تعیین حق بیمه مناسب و عادلانه نیازمند طبقه بندی خریداران بیمه نامه براساس خطرات احتمالی آنها است. عوامل ریسکی فراوانی می تواند بر این قیمت گذاری تاثیر بگذارد. طبقه بندی و تعیین میزان تاثیر گذاری هر عامل ریسکی بر قیمت گذاری بیمه خودرو پیچیدگی خاصی دارد. در این پایان نامه سعی در ارائه راهی جدید برای طبقه بندی عوامل ریسکی با استفاده از اصول و روش ها...
application of several data-driven techniques for rainfall-runoff modeling
in this study, several data-driven techniques including system identification, adaptive neuro-fuzzy inference system (anfis), artificial neural network (ann) and wavelet-artificial neural network (wavelet-ann) models were applied to model rainfall-runoff (rr) relationship. for this purpose, the daily stream flow time series of hydrometric station of hajighoshan on gorgan river and the daily rai...
متن کاملArtificial neural networks as rainfall- runoff models
A series of numerical experiments, in which flow data were generated from synthetic storm sequences routed through a conceptual hydrological model consisting of a single nonlinear reservoir, has demonstrated the closeness of fit that can be achieved to such data sets using Artificial Neural Networks (ANNs). The application of different standardization factors to both training and verification s...
متن کاملEvolutionary Regression Modeling with Active Learning: An Application to Rainfall Runoff Modeling
Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a feasible alternative. However, due to the computational cost of these high fidelity simulations, the use of neural networks, kernel methods, and other surrogate modeling techniques has become indispensable. Surrogate models are com...
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ژورنال
عنوان ژورنال: Journal of Earth System Science
سال: 2008
ISSN: 0253-4126,0973-774X
DOI: 10.1007/s12040-008-0005-2