346 research outputs found
Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm
River flow prediction is essential in many applications of water resources planning and management. In this paper, the accuracy of multivariate adaptive regression splines (MARS), model 5 regression tree (M5RT), and conventional multiple linear regression (CMLR) is compared with a hybrid least square support vector regression-gravitational search algorithm (HLGSA) in predicting monthly river flows. In the first part of the study, all three regression methods were compared with each other in predicting river flows of each basin. It was found that the HLGSA method performed better than the MARS, M5RT, and CMLR in river flow prediction. The effect of log transformation on prediction accuracy of the regression methods was also examined in the second part of the study. Log transformation of the river flow data significantly increased the prediction accuracy of all regression methods. It was also found that log HLGSA (LHLSGA) performed better than the other regression methods. In the third part of the study, the accuracy of the LHLGSA and HLGSA methods was examined in river flow estimation using nearby river flow data. On the basis of results of all applications, it was found that LHLGSA and HLGSA could be successfully used in prediction and estimation of river flow.</jats:p
Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models with Real-Geography Boundary Conditions
We explore the potential of feed-forward deep neural networks (DNNs) for
emulating cloud superparameterization in realistic geography, using offline
fits to data from the Super Parameterized Community Atmospheric Model. To
identify the network architecture of greatest skill, we formally optimize
hyperparameters using ~250 trials. Our DNN explains over 70 percent of the
temporal variance at the 15-minute sampling scale throughout the mid-to-upper
troposphere. Autocorrelation timescale analysis compared against DNN skill
suggests the less good fit in the tropical, marine boundary layer is driven by
neural network difficulty emulating fast, stochastic signals in convection.
However, spectral analysis in the temporal domain indicates skillful emulation
of signals on diurnal to synoptic scales. A close look at the diurnal cycle
reveals correct emulation of land-sea contrasts and vertical structure in the
heating and moistening fields, but some distortion of precipitation.
Sensitivity tests targeting precipitation skill reveal complementary effects of
adding positive constraints vs. hyperparameter tuning, motivating the use of
both in the future. A first attempt to force an offline land model with DNN
emulated atmospheric fields produces reassuring results further supporting
neural network emulation viability in real-geography settings. Overall, the fit
skill is competitive with recent attempts by sophisticated Residual and
Convolutional Neural Network architectures trained on added information,
including memory of past states. Our results confirm the parameterizability of
superparameterized convection with continents through machine learning and we
highlight advantages of casting this problem locally in space and time for
accurate emulation and hopefully quick implementation of hybrid climate models.Comment: 32 Pages, 13 Figures, Revised Version Submitted to Journal of
Advances in Modeling Earth Systems April 202
Suspended sediment modeling using a heuristic regression method hybridized with kmeans clustering
The accurate estimation of suspended sediments (SSs) carries significance in determining the volume of dam storage, river carrying capacity, pollution susceptibility, soil erosion potential, aquatic ecological impacts, and the design and operation of hydraulic structures. The presented study proposes a new method for accurately estimating daily SSs using antecedent discharge and sediment information. The novel method is developed by hybridizing the multivariate adaptive regression spline (MARS) and the Kmeans clustering algorithm (MARSâKM). The proposed methodâs efficacy is established by comparing its performance with the adaptive neuro-fuzzy system (ANFIS), MARS, and M5 tree (M5Tree) models in predicting SSs at two stations situated on the Yangtze River of China, according to the three assessment measurements, RMSE, MAE, and NSE. Two modeling scenarios are employed; data are divided into 50â50% for model training and testing in the first scenario, and the training and test data sets are swapped in the second scenario. In Guangyuan Station, the MARSâKM showed a performance improvement compared to ANFIS, MARS, and M5Tree methods in term of RMSE by 39%, 30%, and 18% in the first scenario and by 24%, 22%, and 8% in the second scenario, respectively, while the improvement in RMSE of ANFIS, MARS, and M5Tree was 34%, 26%, and 27% in the first scenario and 7%, 16%, and 6% in the second scenario, respectively, at Beibei Station. Additionally, the MARSâKM models provided much more satisfactory estimates using only discharge values as inputs
Relative energetics and structural properties of zirconia using a self-consistent tight-binding model
We describe an empirical, self-consistent, orthogonal tight-binding model for
zirconia, which allows for the polarizability of the anions at dipole and
quadrupole levels and for crystal field splitting of the cation d orbitals.
This is achieved by mixing the orbitals of different symmetry on a site with
coupling coefficients driven by the Coulomb potentials up to octapole level.
The additional forces on atoms due to the self-consistency and polarizabilities
are exactly obtained by straightforward electrostatics, by analogy with the
Hellmann-Feynman theorem as applied in first-principles calculations. The model
correctly orders the zero temperature energies of all zirconia polymorphs. The
Zr-O matrix elements of the Hamiltonian, which measure covalency, make a
greater contribution than the polarizability to the energy differences between
phases. Results for elastic constants of the cubic and tetragonal phases and
phonon frequencies of the cubic phase are also presented and compared with some
experimental data and first-principles calculations. We suggest that the model
will be useful for studying finite temperature effects by means of molecular
dynamics.Comment: to be published in Physical Review B (1 march 2000
Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting
A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS)
Prediction of hydropower energy using ANN for the feasibility of hydropower plant installation to an existing irrigation dam
Recently, artificial neural networks (ANNs) have been used successfully for many engineering problems. This paper presents a practical way of predicting the hydropower energy potential using ANNs for the feasibility of adding a hydropower plant unit to an existing irrigation dam. Because the cost of energy has risen considerably in recent decades, addition of a suitable capacity hydropower plant (HPP) to the end of the pressure conduit of an existing irrigation dam may become economically feasible. First, a computer program to realistically calculate all local, frictional, and total head losses (THL) throughout any pressure conduit in detail is coded, whose end-product enables determination of the C coefficient of the highly significant model for total losses as: THL = C·Q2. Next, a computer program to determine the hydroelectric energies produced at monthly periods, the present worth (PW) of their monetary gains, and the annual average energy by a HPP is coded, which utilizes this simple but precise model for quantification of total energy losses from the inlet to the turbine. Inflows series, irrigation water requirements, evaporation rates, turbine running time ratios, and the C coefficient are the input data of this program. This model is applied to randomly chosen 10 irrigation dams in Turkey, and the selected input variables are gross head and reservoir capacity of the dams, recorded monthly inflows and irrigation releases for the prediction of hydropower energy. A single hidden-layered feed forward neural network using Levenberg-Marquardt algorithm is developed with a detailed analysis of model design of those factors affecting successful implementation of the model, which provides for a realistic prediction of the annual average hydroelectric energy from an irrigation dam in a quick-cut manner without the excessive operation studies needed conventionally. Estimation of the average annual energy with the help of this model should be useful for reconnaissance studies. © Springer Science+Business Media B.V. 2007
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