Assessment of Predictive Ability of Artificial Neural Networks Using Holographic Mapping

The volume of material required for the construction of new and expansion of existing beach sites is an important parameter for coastal management. This information may play a crucial role when deciding which beach sites to develop. This work examines whether artificial neural networks (ANNs) can predict the spatial variability of nourishment requirements on the Croatian coast. We use survey data of the nourishment volume requirements and gravel diameter from 2016 to 2020, fetch length, beach area and orientation derived from national maps which vary from location to location due to a complex coastal configuration on the East Adriatic coast, and wind, tide, and rainfall data from nearby meteorological/oceanographic stations to train and test ANNs. The results reported here confirm that an ANN can adequately predict the spatial variability of observed nourishment volumes (R and MSE for the test set equal 0.87 and 2.24 × 104, respectively). The contributions of different parameters to the ANN’s predictive ability were examined. Apart from the most obvious parameters like the beach length and the beach areas, the fetch length proved to be the most important input contribution to ANN’s predictive ability, followed by the beach orientation. Fetch length and beach orientation are parameters governing the wind wave height and direction and hence are proxies for forcing.

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Artificial Neural Networks in Drug Design 11: Influence of Learning Rate and Momentum Factor on the Predictive Ability

Scientia Pharmaceutica ◽

10.3797/scipharm.aut-00-05 ◽

2000 ◽

Vol 68 (1) ◽

pp. 57-64 ◽

Cited By ~ 1

Author(s):

D. Kaiser ◽

C. Tmej ◽

P. Chiba ◽

K.-J. Schaper ◽

G. Ecker

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Predictive Power ◽

Predictive Ability ◽

Learning Rate ◽

Data Set ◽

Lead Compounds ◽

Learning Rates ◽

Artificial Neural ◽

Nanomolar Range

A data set of 48 propafenone-type modulators of multidrug resistance was used to investigate the influence of learning rate and momentum factor on the predictive power of artificial neural networks of different architecture. Generally, small learning rates and medium sized momentum factors are preferred. Some of the networks showed higher cross validated Q2 values than the corresponding linear model (0.87 vs. 0.83). Screening of a 158 compound virtual library identified several new lead compounds with activities in the nanomolar range.

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Groundwater Level Forecasting with Artificial Neural Networks: A Comparison of LSTM, CNN and NARX

10.5194/hess-2020-552 ◽

2020 ◽

Author(s):

Andreas Wunsch ◽

Tanja Liesch ◽

Stefan Broda

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Groundwater Level ◽

Short Term Memory ◽

State Of The Art ◽

Predictive Ability ◽

Data Set ◽

Groundwater Levels ◽

Narx Models ◽

Artificial Neural

Abstract. It is now well established to use shallow artificial neural networks (ANN) to obtain accurate and reliable groundwater level forecasts, which are an important tool for sustainable groundwater management. However, we observe an increasing shift from conventional shallow ANNs to state-of-the-art deep learning (DL) techniques, but a direct comparison of the performance is often lacking. Although they have already clearly proven their suitability, especially shallow recurrent networks frequently seem to be excluded from the study design despite the euphoria about new DL techniques and its successes in various disciplines. Therefore, we aim to provide an overview on the predictive ability in terms of groundwater levels of shallow conventional recurrent ANN namely nonlinear autoregressive networks with exogenous inputs (NARX), and popular state-of-the-art DL-techniques such as long short-term memory (LSTM) and convolutional neural networks (CNN). We compare both the performance on sequence-to-value (seq2val) and sequence-to-sequence (seq2seq) forecasting on a 4-year period, while using only few, widely available and easy to measure meteorological input parameters, which makes our approach widely applicable. We observe that for seq2val forecasts NARX models on average perform best, however, CNNs are much faster and only slightly worse in terms of accuracy. For seq2seq forecasts, mostly NARX outperform both DL-models and even almost reach the speed of CNNs. However, NARX are the least robust against initialization effects, which nevertheless can be handled easily using ensemble forecasting. We showed that shallow neural networks, such as NARX, should not be neglected in comparison to DL-techniques; however, LSTMs and CNNs might perform substantially better with a larger data set, where DL really can demonstrate its strengths, which is rarely available in the groundwater domain though.

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Investigation into the production and conformation traits associated with clinical mastitis using artificial neural networks

Canadian Journal of Animal Science ◽

10.4141/a98-100 ◽

2000 ◽

Vol 80 (3) ◽

pp. 415-426 ◽

Cited By ~ 7

Author(s):

X. Z. Yang ◽

R. Lacroix ◽

K. M. Wade

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Milk Yield ◽

Predictive Ability ◽

Dairy Herd ◽

Clinical Mastitis ◽

Neural Nets ◽

Sensitivity Analyses ◽

Data Set ◽

Artificial Neural

A data set comprising milk-recording and conformation data was used to investigate the usefulness of artificial neural networks in detecting influential variables in the prediction of incidences of clinical mastitis. Specifically, these data contained test-day records from dairy herd analysis, phenotypic cow scores for conformation and genetic conformation proofs for cows and their sires. The data were analysed using the milk-recording data only, the conformation data only, and a combination of the two. Results from sensitivity analyses, performed with trained neural nets, indicated that stage of lactation, milk yield on test day, cumulative milk yield and somatic cell count were the major production factors influencing the ability to detect the occurrence of clinical mastitis. Among the conformation traits, such variables as phenotypic scores for rear-teat placement, dairy character and size, cow proof for dairy character, sire reliability for final score and sire proofs for pin-setting (desirability) and loin strength were found to have some influence on the network's predictive ability, although they were all very minor in relation to the production variables mentioned. As a group, cow genetic proofs seemed more important than either sire genetic proofs or cow phenotypic scores. Given the neural network's general abilities to determine the major factors related to the presence or absence of mastitis on a given test day, it may be appropriate to investigate the possibility of using this technology for actual prediction purposes. Key words: Artificial neural networks, clinical mastitis, milk-recording data, conformation traits, sensitivity analysis, milk production

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Neural Network Optimization of Ligament Stiffnesses for the Enhanced Predictive Ability of a Patient-Specific, Computational Foot/Ankle Model

Journal of Biomechanical Engineering ◽

10.1115/1.4037101 ◽

2017 ◽

Vol 139 (9) ◽

Author(s):

Ruchi D. Chande ◽

Jennifer S. Wayne

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Computational Model ◽

Computational Models ◽

Soft Tissues ◽

Predictive Ability ◽

Patient Specific ◽

Specific Work ◽

Diarthrodial Joints ◽

Artificial Neural

Computational models of diarthrodial joints serve to inform the biomechanical function of these structures, and as such, must be supplied appropriate inputs for performance that is representative of actual joint function. Inputs for these models are sourced from both imaging modalities as well as literature. The latter is often the source of mechanical properties for soft tissues, like ligament stiffnesses; however, such data are not always available for all the soft tissues nor is it known for patient-specific work. In the current research, a method to improve the ligament stiffness definition for a computational foot/ankle model was sought with the greater goal of improving the predictive ability of the computational model. Specifically, the stiffness values were optimized using artificial neural networks (ANNs); both feedforward and radial basis function networks (RBFNs) were considered. Optimal networks of each type were determined and subsequently used to predict stiffnesses for the foot/ankle model. Ultimately, the predicted stiffnesses were considered reasonable and resulted in enhanced performance of the computational model, suggesting that artificial neural networks can be used to optimize stiffness inputs.

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