Forecasting daily streamflow using hybrid ANN models

2006 ◽  
Vol 324 (1-4) ◽  
pp. 383-399 ◽  
Author(s):  
Wen Wang ◽  
Pieter H.A.J.M. Van Gelder ◽  
J.K. Vrijling ◽  
Jun Ma
Keyword(s):  
Daily Streamflow ◽  
Ann Models

ASSESSMENT OF MAXIMUM INSTANT DISCHARGE OF VARIOUS FREQUENCY AT UNGAUGED MOUNTAINOUS RIVER KHEMCHIK (TUVA REPUBLIC) BASED ON MATHEMATICAL MODELLING

2019 ◽  
Vol 13 (2) ◽  
pp. 36-51 ◽  
Author(s):  
O. M. Makarieva ◽  
N. V. Nesterova ◽  
G. P. Yampolsky ◽  
E. Y. Kudymova
Keyword(s):  
Coniferous Forest ◽  
Frequency Interval ◽  
Time Step ◽  
Model Calculations ◽  
Runoff Formation ◽  
Emergency Situations ◽  
Daily Streamflow ◽  
Standard Methodology ◽  
Maximum Elevation ◽  
Daily Time Step

Abstract: the article presents the results of application of distributed deterministic hydrological model Hydrograph for estimation of maximum discharge values of different frequency at the ungauged catchment of the Khemchik River (Khemchik village, Tuva Republic). The catchment area is 1750 km2 , the average and maximum elevation — 2200 and 3600 m, respectively. Due to the lack of detailed information, a schematization of the catchment and the parameterization of the model are proposed, based on general ideas about the water balance and the processes of runoff formation of the main landscapes — rocky talus, coniferous forest and steppe. Parameters and algorithms are verified based on the results of streamflow modeling at two studied catchments: the Tapsy River — Kara-Khol (302 km2 ) and the Khemchik River — Iyme (25500 km2 ). Modelling of runoff formation processes with daily time step for the Khemchik River — Khemchik village was conducted for the period 1966–2012 using observational data at Teeli meteorological station. For the transition from daily to instant discharges, the dependence of the observed values of instant and daily streamflow at the studied gauges has been applied. On the basis of simulated discharge series, the frequency curve was built and the obtained curve was compared with the calculation data according to the standard methodology SP 33-101-2003 “Determination of the main calculated hydrological characteristics” using the analogue river. Simulated maximum instant discharges for entire frequency interval of up to 1% are 1.3–5 times higher than the values obtained by standard methodology SP 33-101-2003. The results of model calculations is indirectly confirmed by the evidences of regular flooding of the Khemchik village provided by the Ministry of Emergency Situations of the Tuva Republic, which is not predicted by the values obtained by the standard methods.

Prediction of Oral Acute Toxicity of Organophosphates Using QSAR Methods

2019 ◽  
Vol 16 ◽  
Author(s):  
Mina Kianpour ◽  
Esmat Mohammadinasab ◽  
Tahereh Momeni Esfahani
Keyword(s):  
Acute Toxicity ◽  
Molecular Descriptors ◽  
Back Propagation ◽  
Quantitative Structure Activity Relationship ◽  
Average Deviation ◽  
Qsar Models ◽  
Ann Models ◽  
Similar Class ◽  
New Compounds ◽  
Oral Acute Toxicity

: The aim of the present study was to develop quantitative structure-activity relationship (QSAR) models, based on molecular descriptors to predict the oral acute toxicity (LD50) of organophosphate compounds. The QSAR models based on genetic algorithm-multiple linear regression (GA-MLR) and back-propagation artificial neural network (BP-ANN) methods were proposed. The prediction experiment showed that the BP-ANN method was a reliable model for screening molecular descriptors, and molecular descriptors obtained by BP-ANN models could well characterize the molecular structure of each compound. It was indicated that among molecular descriptors to predict the LD50 (mgkg-1) of organophosphates, ALOGP2, RDF030u, RDF065p and GATS5m descriptors have more importance than the other descriptors. Also BP-ANN approach with the values of root mean square error (RMSE= 0.00168), square correlation coefficient (R2= 0.9999) and absolute average deviation (AAD=0.6981631) gave the best outcome, and the model predictions were in good agreement with experimental data. The proposed model may be useful for predicting LD50 (mgkg-1) of new compounds of similar class.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

scholarly journals A Comparative Analysis on Probability of Volatility Clusters on Cryptocurrencies, and FOREX Currencies

2021 ◽  
Vol 14 (7) ◽  
pp. 308
Author(s):  
Usha Rekha Chinthapalli
Keyword(s):  
Investment Decision ◽  
Theoretical Work ◽  
Financial Asset ◽  
Dynamic Features ◽  
Asset Return ◽  
Financial Variables ◽  
Ann Models ◽  
Hidden Layer ◽  
Association Structure ◽  
The Impact

In recent years, the attention of investors, practitioners and academics has grown in cryptocurrency. Initially, the cryptocurrency was designed as a viable digital currency implementation, and subsequently, numerous derivatives were produced in a range of sectors, including nonmonetary activities, financial transactions, and even capital management. The high volatility of exchange rates is one of the main features of cryptocurrencies. The article presents an interesting way to estimate the probability of cryptocurrency volatility clusters. In this regard, the paper explores exponential hybrid methodologies GARCH (or EGARCH) and through its portrayal as a financial asset, ANN models will provide analytical insight into bitcoin. Meanwhile, more scalable modelling is needed to fit financial variable characteristics such as ANN models because of the dynamic, nonlinear association structure between financial variables. For financial forecasting, BP is contained in the most popular methods of neural network training. The backpropagation method is employed to train the two models to determine which one performs the best in terms of predicting. This architecture consists of one hidden layer and one input layer with N neurons. Recent theoretical work on crypto-asset return behavior and risk management is supported by this research. In comparison with other traditional asset classes, these results give appropriate data on the behavior, allowing them to adopt the suitable investment decision. The study conclusions are based on a comparison between the dynamic features of cryptocurrencies and FOREX Currency’s traditional mass financial asset. Thus, the result illustrates how well the probability clusters show the impact on cryptocurrency and currencies. This research covers the sample period between August 2017 and August 2020, as cryptocurrency became popular around that period. The following methodology was implemented and simulated using Eviews and SPSS software. The performance evaluation of the cryptocurrencies is compared with FOREX currencies for better comparative study respectively.

scholarly journals Inverse Design of Fe-Based Bulk Metallic Glasses Using Machine Learning

Metals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 729
Author(s):  
Junhyub Jeon ◽  
Namhyuk Seo ◽  
Hwi-Jun Kim ◽  
Min-Ha Lee ◽  
Hyun-Kyu Lim ◽  
...  
Keyword(s):  
Thermal Properties ◽  
Metallic Glasses ◽  
Alloy Composition ◽  
Relevant Literature ◽  
Bulk Metallic Glasses ◽  
X Ray Diffraction ◽  
X Ray ◽  
Ann Models ◽  
Wide Scale ◽  
Artificial Neural Network Ann

Fe-based bulk metallic glasses (BMGs) are a unique class of materials that are attracting attention in a wide variety of applications owing to their physical properties. Several studies have investigated and designed the relationships between alloy composition and thermal properties of BMGs using an artificial neural network (ANN). The limitation of the wide-scale use of these models is that the required composition is yet to be found despite numerous case studies. To address this issue, we trained an ANN to design Fe-based BMGs that predict the thermal properties. Models were trained using only the composition of the alloy as input and were created from a database of more than 150 experimental data of Fe-based BMGs from relevant literature. We adopted these ANN models to design BMGs with thermal properties to satisfy the intended purpose using particle swarm optimization. A melt spinner was employed to fabricate the designed alloys. X-ray diffraction and differential thermal analysis tests were used to evaluate the specimens.

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