scholarly journals Technical note: Application of artificial neural networks in groundwater table forecasting – a case study in a Singapore swamp forest

2016 ◽  
Vol 20 (4) ◽  
pp. 1405-1412 ◽  
Author(s):  
Yabin Sun ◽  
Dadiyorto Wendi ◽  
Dong Eon Kim ◽  
Shie-Yui Liong
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Numerical Models ◽  
Groundwater Resources ◽  
Computational Cost ◽  
Groundwater Table ◽  
Physical Parameters ◽  
Swamp Forest ◽  
Freshwater Swamp ◽  
Artificial Neural

Abstract. Accurate prediction of groundwater table is important for the efficient management of groundwater resources. Despite being the most widely used tools for depicting the hydrological regime, numerical models suffer from formidable constraints, such as extensive data demanding, high computational cost, and inevitable parameter uncertainty. Artificial neural networks (ANNs), in contrast, can make predictions on the basis of more easily accessible variables, rather than requiring explicit characterization of the physical systems and prior knowledge of the physical parameters. This study applies ANN to predict the groundwater table in a freshwater swamp forest of Singapore. The inputs to the network are solely the surrounding reservoir levels and rainfall. The results reveal that ANN is able to produce an accurate forecast with a leading time of 1 day, whereas the performance decreases when leading time increases to 3 and 7 days.

scholarly journals Technical Note: Application of artificial neural networks in groundwater table forecasting – a case study in Singapore swamp forest

2015 ◽  
Vol 12 (9) ◽  
pp. 9317-9336 ◽  
Author(s):  
Y. Sun ◽  
D. Wendi ◽  
D. E. Kim ◽  
S.-Y. Liong
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Numerical Models ◽  
Groundwater Resources ◽  
Computational Cost ◽  
Technical Note ◽  
Groundwater Table ◽  
Physical Parameters ◽  
Swamp Forest ◽  
Artificial Neural

Abstract. Accurate prediction of groundwater table is important for the efficient management of groundwater resources. Despite being the most widely used tools for depicting the hydrological regime, numerical models suffer from formidable constraints, such as extensive data demanding, high computational cost and inevitable parameter uncertainty. Artificial neural networks (ANNs), in contrast, can make predictions on the basis of more easily accessible variables, rather than requiring explicit characterization of the physical systems and prior knowledge of the physical parameters. This study applies ANN to predict the groundwater table in a swamp forest of Singapore. A standard multilayer perceptron (MLP) is selected, trained with the Levenberg–Marquardt (LM) algorithm. The inputs to the network are solely the surrounding reservoir levels and rainfall. The results reveal that ANN is able to produce accurate forecast with a leading time up to 7 days, whereas the performance slightly decreases when leading time increases.

IMPROVEMENT OF DOSE ESTIMATION PROCESS USING ARTIFICIAL NEURAL NETWORKS

2018 ◽  
Vol 184 (1) ◽  
pp. 36-43 ◽  
Author(s):  
Gal Amit ◽  
Hanan Datz
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
Physical Parameters ◽  
Machine Method ◽  
Algorithm Performance ◽  
Artificial Neural ◽  
First Time ◽  
Automatic Algorithm

Abstract We present here for the first time a fast and reliable automatic algorithm based on artificial neural networks for the anomaly detection of a thermoluminescence dosemeter (TLD) glow curves (GCs), and compare its performance with formerly developed support vector machine method. The GC shape of TLD depends on numerous physical parameters, which may significantly affect it. When integrated into a dosimetry laboratory, this automatic algorithm can classify ‘anomalous’ (having any kind of anomaly) GCs for manual review, and ‘regular’ (acceptable) GCs for automatic analysis. The new algorithm performance is then compared with two kinds of formerly developed support vector machine classifiers—regular and weighted ones—using three different metrics. Results show an impressive accuracy rate of 97% for TLD GCs that are correctly classified to either of the classes.

scholarly journals High Three-Dimensional Detection Accuracy in Piezoelectric-Based Touch Panel in Interactive Displays by Optimized Artificial Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 753 ◽  
Author(s):  
Shuo Gao ◽  
Yanning Dai ◽  
Vasileios Kitsos ◽  
Bo Wan ◽  
Xiaolei Qu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Cost ◽  
Machine Learning Algorithms ◽  
Smart Device ◽  
Detection Accuracy ◽  
Force Level ◽  
High Detection ◽  
Artificial Neural ◽  
Interactive Displays

High detection accuracy in piezoelectric-based force sensing in interactive displays has gained global attention. To achieve this, artificial neural networks (ANN)—successful and widely used machine learning algorithms—have been demonstrated to be potentially powerful tools, providing acceptable location detection accuracy of 95.2% and force level recognition of 93.3% in a previous study. While these values might be acceptable for conventional operations, e.g., opening a folder, they must be boosted for applications where intensive operations are performed. Furthermore, the relatively high computational cost reported prevents the popularity of ANN-based techniques in conventional artificial intelligence (AI) chip-free end-terminals. In this article, an ANN is designed and optimized for piezoelectric-based touch panels in interactive displays for the first time. The presented technique experimentally allows a conventional smart device to work smoothly with a high detection accuracy of above 97% for both location and force level detection with a low computational cost, thereby advancing the user experience, and serviced by piezoelectric-based touch interfaces in displays.

Artificial Neural Networks for Structural Health Monitoring of Helicopter Harsh Landings

2013 ◽  
Vol 390 ◽  
pp. 192-197
Author(s):  
Giorgio Vallone ◽  
Claudio Sbarufatti ◽  
Andrea Manes ◽  
Marco Giglio
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Numerical Models ◽  
Damage State ◽  
Operational Conditions ◽  
Structural Health ◽  
Artificial Neural

The aim of the current paper is to explore fuselage monitoring possibilities trough the usage of Artificial Neural Networks (ANNs), trained by the use of numerical models, during harsh landing events. A harsh landing condition is delimited between the usual operational conditions and a crash event. Helicopter structural damage due to harsh landings is generally less severe than damage caused by a crash but may lead to unscheduled maintenance events, involving costs and idle times. Structural Health Monitoring technologies, currently used in many application fields, aim at the continuous detection of damage that may arise, thereby improving safety and reducing maintenance idle times by the disposal of a ready diagnosis. A landing damage database can be obtained with relatively little effort by the usage of a numerical model. Simulated data are used to train various ANNs considering the landing parameter values as input. The influence of both the input and output noise on the system performances were taken into account. Obtained outputs are a general classification between damaged and undamaged conditions, based on a critical damage threshold, and the reconstruction of the fuselage damage state.

scholarly journals Modeling Semiarid River–Aquifer Systems with Bayesian Networks and Artificial Neural Networks

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 107
Author(s):  
Ana D. Maldonado ◽  
María Morales ◽  
Francisco Navarro ◽  
Francisco Sánchez-Martos ◽  
Pedro A. Aguilera
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Bayesian Networks ◽  
Structure Learning ◽  
Computational Cost ◽  
Predictive Performance ◽  
Water Infiltration ◽  
Groundwater Temperature ◽  
Aquifer Systems ◽  
Artificial Neural

In semiarid areas, precipitations usually appear in the form of big and brief floods, which affect the aquifer through water infiltration, causing groundwater temperature changes. These changes may have an impact on the physical, chemical and biological processes of the aquifer and, thus, modeling the groundwater temperature variations associated with stormy precipitation episodes is essential, especially since this kind of precipitation is becoming increasingly frequent in semiarid regions. In this paper, we compare the predictive performance of two popular tools in statistics and machine learning, namely Bayesian networks (BNs) and artificial neural networks (ANNs), in modeling groundwater temperature variation associated with precipitation events. More specifically, we trained a total of 2145 ANNs with different node configurations, from one to five layers. On the other hand, we trained three different BNs using different structure learning algorithms. We conclude that, while both tools are equivalent in terms of accuracy for predicting groundwater temperature drops, the computational cost associated with the estimation of Bayesian networks is significantly lower, and the resulting BN models are more versatile and allow a more detailed analysis.

Quality assessment and artificial neural networks modeling for characterization of chemical and physical parameters of potable water

2018 ◽  
Vol 118 ◽  
pp. 212-219 ◽  
Author(s):  
Marjan Salari ◽  
Esmaeel Salami Shahid ◽  
Seied Hosein Afzali ◽  
Majid Ehteshami ◽  
Gea Oliveri Conti ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Quality Assessment ◽  
Potable Water ◽  
Physical Parameters ◽  
Artificial Neural

scholarly journals A Comparison of Performance of Artificial Neural Networks for Prediction of Heavy Metals Concentration in Groundwater Resources of Toyserkan Plain

2017 ◽  
Vol 4 (1) ◽  
pp. 11792-11792 ◽  
Author(s):  
Meysam Alizamir ◽  
Soheil Sobhanardakani
Keyword(s):  
Heavy Metals ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Squared Error ◽  
Groundwater Resources ◽  
Coefficient Of Determination ◽  
Multi Layer Perceptron ◽  
Squared Error ◽  
Artificial Neural ◽  
Statistical Indicators

Nowadays, about 50% the world’s population is living in dry and semi dry regions and has utilized groundwater as a source of drinking water. Therefore, forecasting of pollutant content in these regions is vital. This study was conducted to compare the performance of artificial neural networks (ANNs) for prediction of As, Zn, and Pb content in groundwater resources of Toyserkan Plain. In this study, two types of artificial neural networks (ANNs), namely multi-layer perceptron (MLP) and Radial Basis Function (RBF) approaches, were examined using the observations of As, Zn, and Pb concentrations in groundwater resources of Toyserkan plain, Western Iran. Two statistical indicators, the coefficient of determination (R2) and root mean squared error (RMSE) were employed to evaluate the performances of various models. The results indicated that the best performance could be obtained by MLP, in terms of different statistical indicators during training and validation periods.

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