scholarly journals A Comparison of In-Sample and Out-of-Sample Model Selection Approaches for Artificial Neural Network (ANN) Daily Streamflow Simulation

Water ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 2525
Author(s):  
Xiaohan Mei ◽  
Patricia K. Smith
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Model Selection ◽  
Meteorological Data ◽  
Information Criterion ◽  
Back Propagation Algorithm ◽  
Daily Streamflow ◽  
South Central ◽  
Out Of Sample ◽  
Artificial Neural

Artificial Neural Networks (ANN) have been widely applied in hydrologic and water quality (H/WQ) modeling in the past three decades. Many studies have demonstrated an ANN’s capability to successfully estimate daily streamflow from meteorological data on the watershed level. One major challenge of ANN streamflow modeling is finding the optimal network structure with good generalization capability while ameliorating model overfitting. This study empirically examines two types of model selection approaches for simulating streamflow time series: the out-of-sample approach using blocked cross-validation (BlockedCV) and an in-sample approach that is based on Akaike’s information criterion (AIC) and Bayesian information criterion (BIC). A three-layer feed-forward neural network using a back-propagation algorithm is utilized to create the streamflow models in this study. The rainfall–streamflow relationship of two adjacent, small watersheds in the San Antonio region in south-central Texas are modeled on a daily time scale. The model selection results of the two approaches are compared, and some commonly used performance measures (PMs) are generated on the stand-alone testing datasets to evaluate the models selected by the two approaches. This study finds that, in general, the out-of-sample and in-sample approaches do not converge to the same model selection results, with AIC and BIC selecting simpler models than BlockedCV. The ANNs were found to have good performance in both study watersheds, with BlockedCV selected models having a Nash–Sutcliffe coefficient of efficiency (NSE) of 0.581 and 0.658, and AIC/BIC selected models having a poorer NSE of 0.574 and 0.310, for the two study watersheds. Overall, out-of-sample BlockedCV selected models with better predictive ability and is preferable to model streamflow time series.

scholarly journals Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Back Propagation ◽  
Armed Forces ◽  
Activation Function ◽  
Optimal Number ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Classification Rate ◽  
Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

Test on Flood Prediction-Model Using Artificial Neural Network for ShiiLiAn Hydrologic Station on MinChiang,China

2010 ◽  
Vol 39 ◽  
pp. 555-561 ◽  
Author(s):  
Qing Hua Luan ◽  
Yao Cheng ◽  
Zha Xin Ima
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flood Control ◽  
Back Propagation ◽  
Forecast Model ◽  
Flood Forecast ◽  
Back Propagation Algorithm ◽  
Nonlinear Method ◽  
Flood Prediction ◽  
Artificial Neural

The establishing of a precise simulation model for runoff prediction in river with several tributaries is the difficulty of flood forecast, which is also one of the difficulties in hydrologic research. Due to the theory of Artificial Neural Network, using Back Propagation algorithm, the flood forecast model for ShiLiAn hydrologic station in Minjiang River is constructed and validated in this study. Through test, the result shows that the forecast accuracy is satisfied for all check standards of flood forecast and then proves the feasibility of using nonlinear method for flood forecast. This study provides a new method and reference for flood control and water resources management in the local region.

scholarly journals Artificial Neural Network Model for Monitoring Oil Film Regime in Spur Gear Based on Acoustic Emission Data

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Yasir Hassan Ali ◽  
Roslan Abd Rahman ◽  
Raja Ishak Raja Hamzah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Film Thickness ◽  
Spur Gear ◽  
Back Propagation Algorithm ◽  
Elman Neural Network ◽  
Feed Forward ◽  
Oil Film ◽  
Feed Forward Network ◽  
Artificial Neural

The thickness of an oil film lubricant can contribute to less gear tooth wear and surface failure. The purpose of this research is to use artificial neural network (ANN) computational modelling to correlate spur gear data from acoustic emissions, lubricant temperature, and specific film thickness (λ). The approach is using an algorithm to monitor the oil film thickness and to detect which lubrication regime the gearbox is running either hydrodynamic, elastohydrodynamic, or boundary. This monitoring can aid identification of fault development. Feed-forward and recurrent Elman neural network algorithms were used to develop ANN models, which are subjected to training, testing, and validation process. The Levenberg-Marquardt back-propagation algorithm was applied to reduce errors. Log-sigmoid and Purelin were identified as suitable transfer functions for hidden and output nodes. The methods used in this paper shows accurate predictions from ANN and the feed-forward network performance is superior to the Elman neural network.

Prognosis of water levels in a moor groundwater system influenced by hydrology and water extraction using an artificial neural network 

2021 ◽  
Author(s):  
Sascha Flaig ◽  
Timothy Praditia ◽  
Alexander Kissinger ◽  
Ulrich Lang ◽  
Sergey Oladyshkin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Meteorological Data ◽  
Water Levels ◽  
Typical Structure ◽  
Water Abstraction ◽  
Physical Knowledge ◽  
Artificial Neural ◽  
The Impact

<p>In order to prevent possible negative impacts of water abstraction in an ecologically sensitive moor south of Munich (Germany), a “predictive control” scheme is in place. We design an artificial neural network (ANN) to provide predictions of moor water levels and to separate hydrological from anthropogenic effects. As the moor is a dynamic system, we adopt the „Long short-term memory“ architecture.</p><p>To find the best LSTM setup, we train, test and compare LSTMs with two different structures: (1) the non-recurrent one-to-one structure, where the series of inputs are accumulated and fed into the LSTM; and (2) the recurrent many-to-many structure, where inputs gradually enter the LSTM (including LSTM forecasts from previous forecast time steps). The outputs of our LSTMs then feed into a readout layer that converts the hidden states into water level predictions. We hypothesize that the recurrent structure is the better structure because it better resembles the typical structure of differential equations for dynamic systems, as they would usually be used for hydro(geo)logical systems. We evaluate the comparison with the mean squared error as test metric, and conclude that the recurrent many-to-many LSTM performs better for the analyzed complex situations. It also produces plausible predictions with reasonable accuracy for seven days prediction horizon.</p><p>Furthermore, we analyze the impact of preprocessing meteorological data to evapotranspiration data using typical ETA models. Inserting knowledge into the LSTM in the form of ETA models (rather than implicitly having the LSTM learn the ETA relations) leads to superior prediction results. This finding aligns well with current ideas on physically-inspired machine learning.</p><p>As an additional validation step, we investigate whether our ANN is able to correctly identify both anthropogenic and natural influences and their interaction. To this end, we investigate two comparable pumping events under different meteorological conditions. Results indicate that all individual and combined influences of input parameters on water levels can be represented well. The neural networks recognize correctly that the predominant precipitation and lower evapotranspiration during one pumping event leads to a lower decrease of the hydrograph.</p><p>To further demonstrate the capability of the trained neural network, scenarios of pumping events are created and simulated.</p><p>In conclusion, we show that more robust and accurate predictions of moor water levels can be obtained if available physical knowledge of the modeled system is used to design and train the neural network. The artificial neural network can be a useful instrument to assess the impact of water abstraction by quantifying the anthropogenic influence.</p>

Forecasting Crude Oil price using Artificial Neural Network model

2021 ◽  
pp. 321-326
Author(s):  
Sivaprakash J. ◽  
Manu K. S.
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Crude Oil ◽  
Back Propagation ◽  
Oil Prices ◽  
Oil Price ◽  
Back Propagation Algorithm ◽  
Crude Oil Price ◽  
Propagation Algorithm ◽  
Crude Oil Prices

In the advanced global economy, crude oil is a commodity that plays a major role in every economy. As Crude oil is highly traded commodity it is essential for the investors, analysts, economists to forecast the future spot price of the crude oil appropriately. In the last year the crude oil faced a historic fall during the pandemic and reached all time low, but will this situation last? There was analysis such as fundamental analysis, technical analysis and time series analyses which were carried out for predicting the movement of the oil prices but the accuracy in such prediction is still a question. Thus, it is necessary to identify better methods to forecast the crude oil prices. This study is an empirical study to forecast crude oil prices using the neural networks. This study consists of 13 input variables with one target variable. The data are divided in the ratio 70:30. The 70% data is used for training the network and 30% is used for testing. The feed forward and back propagation algorithm are used to predict the crude oil price. The neural network proved to be efficient in forecasting in the modern era. A simple neural network performs better than the time series models. The study found that back propagation algorithm performs better while predicting the crude oil price. Hence, ANN can be used by the investors, forecasters and for future researchers.

Novel Application to Recognize a Breakdown Pressure Event on Time Series Frac Data Vs. an Artificial Intelligence Approach

2021 ◽  
Author(s):  
Alberto Jose Ramirez ◽  
Jessica Graciela Iriarte
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Service Providers ◽  
Complex Model ◽  
Rate Of Change ◽  
Computational Time ◽  
Breakdown Pressure ◽  
Constant Multiple ◽  
Artificial Neural ◽  
Rule Based Approach

Abstract Breakdown pressure is the peak pressure attained when fluid is injected into a borehole until fracturing occurs. Hydraulic fracturing operations are conducted above the breakdown pressure, at which the rock formation fractures and allows fluids to flow inside. This value is essential to obtain formation stress measurements. The objective of this study is to automate the selection of breakdown pressure flags on time series fracture data using a novel algorithm in lieu of an artificial neural network. This study is based on high-frequency treatment data collected from a cloud-based software. The comma separated (.csv) files include treating pressure (TP), slurry rate (SR), and bottomhole proppant concentration (BHPC) with defined start and end time flags. Using feature engineering, the model calculates the rate of change of treating pressure (dtp_1st) slurry rate (dsr_1st), and bottomhole proppant concentration (dbhpc_1st). An algorithm isolates the initial area of the treatment plot before proppant reaches the perforations, the slurry rate is constant, and the pressure increases. The first approach uses a neural network trained with 872 stages to isolate the breakdown pressure area. The expert rule-based approach finds the highest pressure spikes where SR is constant. Then, a refining function finds the maximum treating pressure value and returns its job time as the predicted breakdown pressure flag. Due to the complexity of unconventional reservoirs, the treatment plots may show pressure changes while the slurry rate is constant multiple times during the same stage. The diverse behavior of the breakdown pressure inhibits an artificial neural network's ability to find one "consistent pattern" across the stage. The multiple patterns found through the stage makes it difficult to select an area to find the breakdown pressure value. Testing this complex model worked moderately well, but it made the computational time too high for deployment. On the other hand, the automation algorithm uses rules to find the breakdown pressure value with its location within the stage. The breakdown flag model was validated with 102 stages and tested with 775 stages, returning the location and values corresponding to the highest pressure point. Results show that 86% of the predicted breakdown pressures are within 65 psi of manually picked values. Breakdown pressure recognition automation is important because it saves time and allows engineers to focus on analytical tasks instead of repetitive data-structuring tasks. Automating this process brings consistency to the data across service providers and basins. In some cases, due to its ability to zoom-in, the algorithm recognized breakdown pressures with higher accuracy than subject matter experts. Comparing the results from two different approaches allowed us to conclude that similar or better results with lower running times can be achieved without using complex algorithms.

A Novel Intuitionistic Fuzzy Time Series Method Based on Bootstrapped Combined Pi-Sigma Artificial Neural Network

2021 ◽  
Vol 01 ◽  
Author(s):  
Eren Bas ◽  
Erol Egrioglu ◽  
Emine Kölemen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Stock Exchange ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Intuitionistic Fuzzy ◽  
Forecasting Methods ◽  
Time Series Method ◽  
Artificial Neural

Background: Intuitionistic fuzzy time series forecasting methods have been started to solve the forecasting problems in the literature. Intuitionistic fuzzy time series methods use both membership and non-membership values as auxiliary variables in their models. Because intuitionistic fuzzy sets take into consideration the hesitation margin and so the intuitionistic fuzzy time series models use more information than fuzzy time series models. The background of this study is about intuitionistic fuzzy time series forecasting methods. Objective: The study aims to propose a novel intuitionistic fuzzy time series method. It is expected that the proposed method will produce better forecasts than some selected benchmarks. Method: The proposed method uses bootstrapped combined Pi-Sigma artificial neural network and intuitionistic fuzzy c-means. The combined Pi-Sigma artificial neural network is proposed to model the intuitionistic fuzzy relations. Results and Conclusion: The proposed method is applied to different sets of SP&500 stock exchange time series. The proposed method can provide more accurate forecasts than established benchmarks for the SP&500 stock exchange time series. The most important contribution of the proposed method is that it creates statistical inference: probabilistic forecasting, confidence intervals and the empirical distribution of the forecasts. Moreover, the proposed method is better than the selected benchmarks for the SP&500 data set.

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