scholarly journals Modeling moisture fluxes using artificial neural networks: can information extraction overcome data loss?

2010 ◽  
Vol 7 (5) ◽  
pp. 6525-6551
Author(s):  
A. L. Neal ◽  
H. V. Gupta ◽  
S. A. Kurc ◽  
P. D. Brooks
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Eddy Covariance ◽  
Land Surface ◽  
Data Sets ◽  
Data Loss ◽  
Gap Filling ◽  
Multiple Data ◽  
Moisture Fluxes ◽  
Artificial Neural

Abstract. Eddy covariance sites can experience data losses as high as 30 to 45% on an annual basis. Artificial neural networks (ANNs) have been identified as powerful tools for gap filling, but their performance depends on the representativeness of data used to train the model. In this paper, we develop a normalization method, which has similar performance compared to conventional training approaches, but exhibits differences in the timing of fluxes, indicating different and previously unused information in the data record. Specifically, the differences between half-hourly model fluxes, especially during summer months, indicate that the structure of the information content in the data changes seasonally, diurnally and with the rate of data loss. This variation between gap-filling models complicates the application of their output as consistent data sets for land surface modeling, and points to the need for improved data and models to address flux behavior at critical times. We advise several approaches to address these concerns, including use of separate models for day and nighttime processes and the use of multiple data streams at dawn, when eddy covariance may be particularly ineffective due to the timing of the onset of turbulent mixing.

scholarly journals Modeling moisture fluxes using artificial neural networks: can information extraction overcome data loss?

2011 ◽  
Vol 15 (1) ◽  
pp. 359-368 ◽  
Author(s):  
A. L. Neal ◽  
H. V. Gupta ◽  
S. A. Kurc ◽  
P. D. Brooks
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Eddy Covariance ◽  
Model Performance ◽  
Data Loss ◽  
Improve Model ◽  
Data Record ◽  
Moisture Fluxes ◽  
Artificial Neural ◽  
Improve Model Performance

Abstract. Eddy covariance sites can experience data losses as high as 30 to 45% on an annual basis. Artificial neural networks (ANNs) have been identified as powerful tools for gap filling, but their performance depends on the representativeness of data used to train the model. In this paper, we develop a normalization method, which has similar performance compared to conventional training approaches, but exhibits differences in the timing of fluxes, indicating different and previously unused information in the data record. Specifically, the differences between half-hourly model fluxes, especially during summer months, indicate that the structure of the information content in the data changes seasonally, diurnally and with the rate of data loss. Extracting more information from data may not improve model performance and indicates the need for improved data and models to address flux behavior at critical times. We advise several approaches to address these concerns, including use of separate models for day and nighttime processes and the use of alternate data streams at dawn, when eddy covariance may be particularly ineffective due to the timing of the onset of turbulent mixing.

Developing a Predictive Model for Plasmodium knowlesi–Susceptible Areas in Malaysia Using Geospatial Data and Artificial Neural Networks

2021 ◽  
pp. 101053952110486
Author(s):  
Rozita Hod ◽  
Siti Aisah Mokhtar ◽  
Farrah Melissa Muharam ◽  
Ummi Kalthom Shamsudin ◽  
Jamal Hisham Hashim
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Environmental Factors ◽  
Predictive Model ◽  
Land Surface ◽  
Plasmodium Knowlesi ◽  
Geospatial Data ◽  
Susceptibility Index ◽  
Malaria Susceptibility ◽  
Artificial Neural

Plasmodium knowlesi is an emerging species for malaria in Malaysia, particularly in East Malaysia. This infection contributes to almost half of all malaria cases and deaths in Malaysia and poses a challenge in eradicating malaria. The aim of this study was to develop a predictive model for P. knowlesi susceptibility areas in Sabah, Malaysia, using geospatial data and artificial neural networks (ANNs). Weekly malaria cases from 2013 to 2014 were used to identify the malaria hotspot areas. The association of malaria cases with environmental factors (elevation, water bodies, and population density, and satellite images providing rainfall, land surface temperature, and normalized difference vegetation indices) were statistically determined. The significant environmental factors were used as input for the ANN analysis to predict malaria cases. Finally, the malaria susceptibility index and zones were mapped out. The results suggested integrating geospatial data and ANNs to predict malaria cases, with overall correlation coefficient of 0.70 and overall accuracy of 91.04%. From the malaria susceptibility index and zoning analyses, it was found that areas located along the Crocker Range of Sabah and the East part of Sabah were highly susceptible to P. knowlesi infections. Following this analysis, targetted entomological mapping and malaria control programs can be initiated.

scholarly journals Simulation of flood flow in a river system using artificial neural networks

2005 ◽  
Vol 9 (4) ◽  
pp. 313-321 ◽  
Author(s):  
R. R. Shrestha ◽  
S. Theobald ◽  
F. Nestmann
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Flow Simulation ◽  
Simulation Models ◽  
River System ◽  
Training Data ◽  
Data Sets ◽  
Network Training ◽  
Artificial Neural ◽  
Flood Flow

Abstract. Artificial neural networks (ANNs) provide a quick and flexible means of developing flood flow simulation models. An important criterion for the wider applicability of the ANNs is the ability to generalise the events outside the range of training data sets. With respect to flood flow simulation, the ability to extrapolate beyond the range of calibrated data sets is of crucial importance. This study explores methods for improving generalisation of the ANNs using three different flood events data sets from the Neckar River in Germany. An ANN-based model is formulated to simulate flows at certain locations in the river reach, based on the flows at upstream locations. Network training data sets consist of time series of flows from observation stations. Simulated flows from a one-dimensional hydrodynamic numerical model are integrated for network training and validation, at a river section where no measurements are available. Network structures with different activation functions are considered for improving generalisation. The training algorithm involved backpropagation with the Levenberg-Marquardt approximation. The ability of the trained networks to extrapolate is assessed using flow data beyond the range of the training data sets. The results of this study indicate that the ANN in a suitable configuration can extend forecasting capability to a certain extent beyond the range of calibrated data sets.

scholarly journals Artificial Neural Networks Application in Modal Analysis of Tires

2013 ◽  
Vol 13 (5) ◽  
pp. 273-278 ◽  
Author(s):  
P. Koštial ◽  
Z. Jančíková ◽  
D. Bakošová ◽  
J. Valíček ◽  
M. Harničárová ◽  
...  
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Modal Analysis ◽  
Speckle Pattern ◽  
Point Of View ◽  
Electronic Speckle Pattern Interferometry ◽  
Data Sets ◽  
Artificial Neural

Abstract The paper deals with the application of artificial neural networks (ANN) to tires’ own frequency (OF) prediction depending on a tire construction. Experimental data of OF were obtained by electronic speckle pattern interferometry (ESPI). A very good conformity of both experimental and predicted data sets is presented here. The presented ANN method applied to ESPI experimental data can effectively help designers to optimize dimensions of tires from the point of view of their noise.

scholarly journals Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

2017 ◽  
Vol 43 (4) ◽  
pp. 26-32 ◽  
Author(s):  
Sinan Mehmet Turp
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Data Sets ◽  
Adsorption Efficiency ◽  
Back Propagation Algorithm ◽  
Artificial Neural

AbstractThis study investigates the estimated adsorption efficiency of artificial Nickel (II) ions with perlite in an aqueous solution using artificial neural networks, based on 140 experimental data sets. Prediction using artificial neural networks is performed by enhancing the adsorption efficiency with the use of Nickel (II) ions, with the initial concentrations ranging from 0.1 mg/L to 10 mg/L, the adsorbent dosage ranging from 0.1 mg to 2 mg, and the varying time of effect ranging from 5 to 30 mins. This study presents an artificial neural network that predicts the adsorption efficiency of Nickel (II) ions with perlite. The best algorithm is determined as a quasi-Newton back-propagation algorithm. The performance of the artificial neural network is determined by coefficient determination (R2), and its architecture is 3-12-1. The prediction shows that there is an outstanding relationship between the experimental data and the predicted values.

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