Combining autoencoder neural network and Bayesian inversion to estimate heterogeneous permeability distributions in enhanced geothermal reservoir: Model development and verification

Accurate prediction of water table depth over long-term in arid agricultural areas are very much important for maintaining environmental sustainability. Because of intricate and diverse hydrogeological features, boundary conditions, and human activities researchers face enormous difficulties for predicting water table depth. A virtual study on forecast of water table depth using various neural networks is employed in this paper. Hybrid neural network approach like Adaptive Neuro Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), Radial Basis Function Neural Network (RBFN) is employed here to appraisal water levels as a function of average temperature, precipitation, humidity, evapotranspiration and infiltration loss data. Coefficient of determination (R2), Root mean square error (RMSE), and Mean square error (MSE) are used to evaluate performance of model development. While ANFIS algorithm is used, Gbell function gives best value of performance for model development. Whole outcomes establish that, ANFIS accomplishes finest as related to RNN and RBFN for predicting water table depth in watershed.

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Bayesian calibration of a large-scale geothermal reservoir model by a new adaptive delayed acceptance Metropolis Hastings algorithm

Water Resources Research ◽

10.1029/2010wr010352 ◽

2011 ◽

Vol 47 (10) ◽

Cited By ~ 65

Author(s):

T. Cui ◽

C. Fox ◽

M. J. O'Sullivan

Keyword(s):

Large Scale ◽

Geothermal Reservoir ◽

Reservoir Model ◽

Bayesian Calibration

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Model for Predicting the Machinability of Continuously Cast and Subsequently Rolled Steel Using the Artificial Neural Network

Tehnički glasnik ◽

10.31803/tg-20210619190926 ◽

2021 ◽

Vol 15 (3) ◽

pp. 381-386

Author(s):

Miha Kovačič ◽

Shpetim Salihu ◽

Uroš Župerl

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Production Process ◽

Chemical Elements ◽

Cast Steel ◽

Model Development ◽

Cutting Speed ◽

Steel Production ◽

Continuously Cast ◽

Artificial Neural

The paper presents a model for predicting the machinability of steels using the method of artificial neural networks. The model includes all indicators from the entire steel production process that best predict the machinability of continuously cast steel. Data for model development were obtained from two years of serial production of 26 steel grades from 255 batches and include seven parameters from secondary metallurgy, four parameters from the casting process, and the content of ten chemical elements. The machinability was determined based on ISO 3685, which defines the machinability of a batch as the cutting speed with a cutting tool life of 15 minutes. An artificial neural network is used to predict this cutting speed. Based on the modelling results, the steel production process was optimised. Over a 5-month period, an additional 39 batches of 20MnV6 steel were produced to verify the developed model.

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Development of Hybrid Artificial Neural Network for Quantifying Energy Saving using Measurement and Verification

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v8.i1.pp137-145 ◽

2017 ◽

Vol 8 (1) ◽

pp. 137

Author(s):

Wan n Nazirah Wan Md Adna ◽

Nofri Yenita Dahlan ◽

Ismail Musirin

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Energy Use ◽

Model Development ◽

Operating Time ◽

Electrical Energy ◽

Measurement And Verification ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hybrid Artificial Neural Network

This paper presents a Hybrid Artificial Neural Network (HANN) for chiller system Measurement and Verification (M&V) model development. In this work, hybridization of Evolutionary Programming (EP) and Artificial Neural Network (ANN) are considered in modeling the baseline electrical energy consumption for a chiller system hence quantifying saving. EP with coefficient of correlation (R) objective function is used in optimizing the neural network training process and selecting the optimal values of ANN initial weights and biases. Three inputs that are affecting energy use of the chiller system are selected; 1) operating time, 2) refrigerant tonnage and 3) differential temperature. The output is hourly energy use of building air-conditioning system. The HANN model is simulated with 16 different structures and the results reveal that all HANN structures produce higher prediction performance with R is above 0.977. The best structure with the highest value of R is selected as the baseline model hence is used to determine the saving. The avoided energy calculated from this model is 132944.59 kWh that contributes to 1.38% of saving percentage.

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Distinguishing sinus rhythm from atrial fibrillation on single-lead ECGs using a deep neural network

European Heart Journal ◽

10.1093/ehjci/ehaa946.3442 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

M Oudkerk Pool ◽

B.D De Vos ◽

J.M Wolterink ◽

S Blok ◽

M.J Schuuring ◽

...

Keyword(s):

Neural Network ◽

Atrial Fibrillation ◽

Sinus Rhythm ◽

Roc Curve ◽

Model Development ◽

Independent Set ◽

Funding Source ◽

National Budget ◽

Validation Set ◽

Public Grant

Abstract Background The growing availability of mobile phones increases the popularity of portable telemonitoring devices. An atrial fibrillation diagnosis can be reached with a recording of 30s on such telemonitoring devices. However, current commercially available automatic algorithms still require approval by experts. Purpose In this research we aimed to build an artificial intelligence (AI) algorithm to improve automatic distinction of atrial fibrillation (AF) from sinus rhythm (SR), to ultimately save time, costs, and to facilitate telemonitoring programs. Methods We developed a deep convolutional neural network (CNN), based on a residual neural network (ResNet), tailored to single-lead ECG analysis. The CNN was trained using publicly available single-lead ECGs from the 2017 PhysioNet/ Computing in Cardiology Challenge. This dataset consists of 60% SR, 9% AF, 30% alternative rhythm, and 1% noise ECGs. The 8528 available ECGs were divided into a training (90%) and validation set (10%) for model development and hyperparameter optimization. Results The trained CNN was applied to an independent set containing single-lead ECGs of 600 patients equally divided into two groups: SR and AF. Both groups comprised of 300 unique ECGs (SR; 60% male, 63±11 years, AF; 38% male, 56±14 years). In distinguishing between AF and SR, the method achieved an accuracy of 0.92, an F1-score of 0.91, and area under the ROC-curve of 0.98. Conclusion The results demonstrate that distinguishing SR and AF by a fully automatic AI algorithm is feasible. This approach has the potential to reduce cost by minimizing expert supervision, especially when extending the algorithm to other heart rhythms, like premature atrial/ventricular contractions and atrial flutter. Figure 1. ROC curve Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Dekkerbeurs - Hartstichting

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Enhancing PMW Satellite Precipitation Estimation: Detecting Convective Class

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-19-0008.1 ◽

2019 ◽

Vol 36 (12) ◽

pp. 2349-2363 ◽

Cited By ~ 2

Author(s):

Veljko Petković ◽

Marko Orescanin ◽

Pierre Kirstetter ◽

Christian Kummerow ◽

Ralph Ferraro

Keyword(s):

Neural Network ◽

Deep Learning ◽

Large Scale ◽

Model Development ◽

Precipitation Rate ◽

Neural Network Approach ◽

Satellite Precipitation ◽

Precipitation Estimation ◽

Microwave Imager ◽

Deep Learning Neural Network

AbstractA decades-long effort in observing precipitation from space has led to continuous improvements of satellite-derived passive microwave (PMW) large-scale precipitation products. However, due to a limited ability to relate observed radiometric signatures to precipitation type (convective and stratiform) and associated precipitation rate variability, PMW retrievals are prone to large systematic errors at instantaneous scales. The present study explores the use of deep learning approach in extracting the information content from PMW observation vectors to help identify precipitation types. A deep learning neural network model (DNN) is developed to retrieve the convective type in precipitating systems from PMW observations. A 12-month period of Global Precipitation Measurement mission Microwave Imager (GMI) observations is used as a dataset for model development and verification. The proposed DNN model is shown to accurately predict precipitation types for 85% of total precipitation volume. The model reduces precipitation rate bias associated with convective and stratiform precipitation in the GPM operational algorithm by a factor of 2 while preserving the correlation with reference precipitation rates, and is insensitive to surface type variability. Based on comparisons against currently used convective schemes, it is concluded that the neural network approach has the potential to address regime-specific PMW satellite precipitation biases affecting GPM operations.

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A numerical method for the solution of geothermal reservoir model equations

Geothermics ◽

10.1016/0375-6505(89)90064-3 ◽

1989 ◽

Vol 18 (3) ◽

pp. 377-391 ◽

Cited By ~ 1

Author(s):

J.J. Gelegenis ◽

V.A. Lygerou ◽

N.G. Koumoutsos

Keyword(s):

Numerical Method ◽

Geothermal Reservoir ◽

Reservoir Model ◽

Model Equations

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Monthly Rainfall-Runoff Modeling for Sondur Reservoir

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst/21/08336 ◽

2021 ◽

Vol 23 (07) ◽

pp. 1453-1459

Author(s):

Shashi Kant Jaiswal ◽

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Model Development ◽

Monthly Rainfall ◽

Nonlinear Mapping ◽

Rainfall Runoff ◽

Ann Model ◽

Regression Procedure ◽

Runoff Modeling ◽

Artificial Neural Network Ann

This study presents the application of Artificial Neural Network (ANN) to modeling the rainfall-inflow relationship for Sondur Reservoir located in Chhattisgarh State of India. ANNs are usually assumed to be powerful tools for nonlinear mapping in various applications. ANN is superior to linear regression procedure used for rainfallinflow modeling. For model development twenty nine years data of monthly rainfall and inflow have been used. The results extracted from study indicated that the ANN model is efficient for rainfall-inflow modeling.

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Legitimising neural network river forecasting models: a new data-driven mechanistic modelling framework

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-145-2013 ◽

2013 ◽

Vol 10 (1) ◽

pp. 145-187 ◽

Cited By ~ 1

Author(s):

N. J. Mount ◽

C. W. Dawson ◽

R. J. Abrahart

Keyword(s):

Neural Network ◽

Goodness Of Fit ◽

Model Development ◽

Relative Sensitivity ◽

Data Driven ◽

Neural Network Models ◽

Core Element ◽

Mechanistic Modelling ◽

Mechanistic Understanding ◽

River Forecasting

Abstract. In this paper we address the difficult problem of gaining an internal, mechanistic understanding of a neural network river forecasting (NNRF) model. Neural network models in hydrology have long been criticised for their black-box character, which prohibits adequate understanding of their modelling mechanisms and has limited their broad acceptance by hydrologists. In response, we here present a new, data-driven mechanistic modelling (DDMM) framework that incorporates an evaluation of the legitimacy of a neural network's internal modelling mechanism as a core element in the model development process. The framework is exemplified for two NNRF modelling scenarios, and uses a novel adaptation of first order, partial derivate, relative sensitivity analysis methods as the means by which each model's mechanistic legitimacy is explored. The results demonstrate the limitations of standard, goodness-of-fit validation procedures applied by NNRF modellers, by highlighting how the internal mechanisms of complex models that produce the best fit scores can have much lower legitimacy than simpler counterparts whose scores are only slightly inferior. The study emphasises the urgent need for better mechanistic understanding of neural network-based hydrological models and the further development of methods for elucidating their mechanisms.

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