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.

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

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.

Diagnostic Cost Reduction Using Artificial Neural Networks

2011 ◽  
pp. 1812-1830
Author(s):  
Steven Walczak ◽  
Bradley B. Brimhall ◽  
Jerry B. Lefkowitz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Clinical Laboratory ◽  
Predictive Performance ◽  
Diagnostic Model ◽  
Medical Problems ◽  
Ann Models ◽  
Artificial Neural ◽  
Diagnostic Capabilities ◽  
Performance Results

Patients face a multitude of diseases, trauma, and related medical problems that are difficult and costly to diagnose with respect to direct costs, including pulmonary embolism (PE). Advanced decision-making tools such as artificial neural networks (ANNs) improve diagnostic capabilities for these problematic medical conditions. The research in this chapter develops a backpropagation trained ANN diagnostic model to predict the occurrence of PE. Laboratory database values for 292 patients who were determined to be at risk for a PE, with 15% suffering a confirmed PE, are collected and used to evaluate various ANN models’ performance. Results indicate that using ANN diagnostic models enables the leveraging of knowledge gained from standard clinical laboratory tests, significantly improving both overall positive predictive and negative predictive performance.

Predictive performance of “highly complex” artificial neural networks

2007 ◽  
Vol 324 ◽  
pp. 90-93 ◽  
Author(s):  
András Tompos ◽  
József L. Margitfalvi ◽  
Ernő Tfirst ◽  
Károly Héberger
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Performance ◽  
Artificial Neural

Artificial neural networks applied for solidified soils data prediction: a bibliometric and systematic review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vinicius Luiz Pacheco ◽  
Lucimara Bragagnolo ◽  
Antonio Thomé
Keyword(s):  
Systematic Review ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
State Of The Art ◽  
Computational Cost ◽  
Soil Improvement ◽  
The State ◽  
Data Set ◽  
Content Type ◽  
Artificial Neural

Purpose The purpose of this article is to analyze the state-of-the art in a systematic way, identifying the main research groups and their related topics. The types of studies found are fundamental for understanding the application of artificial neural networks (ANNs) in cemented soils and the potential for using the technique, as well as the feasibility of extrapolation to new geotechnical or civil and environmental engineering segments. Design/methodology/approach This work is characterized as being bibliometric and systematic research of an exploratory perspective of state-of-the-art. It also persuades the qualitative and quantitative data analysis of cemented soil improvement, biocemented or microbially induced calcite precipitation (MICP) soil improvement by prediction/modeling by ANN. This study sought to compile and study the state of the art of the topic which possibilities to have a critical view about the theme. To do so, two main databases were analyzed: Scopus and Web of Science. Systematic review techniques, as well as bibliometric indicators, were implemented. Findings This paper connected the network between the achievements of the researches and illustrated the main application of ANNs in soil improvement prediction, specifically on cemented-based soils and biocemented soils (e.g. MICP technique). Also, as a bibliometric and systematic review, this work could achieve the key points in the absence of researches involving soil-ANN, and it provided the understanding of the lack of exploratory studies to be approached in the near future. Research limitations/implications Because of the research topic the article suggested other applications of ANNs in geotechnical engineering, such as other tests not related to geomechanical resistance such as unconfined compression test test and triaxial test. Practical implications This article systematically and critically presents some interesting points in the direction of future research, such as the non-approach to the use of ANNs in biocementation processes, such as MICP. Social implications Regarding the social environment, the paper brings approaches on methods that somehow mitigate the computational use, or elements necessary for geotechnical improvement of the soil, thereby optimizing the same consequently. Originality/value Neural networks have been studied for a long time in engineering, but the current computational power has increased the implementation for several engineering applications. Besides that, soil cementation is a widespread technique and its prediction modes often require high computational strength, such parameters can be mitigated with the use of ANNs, because artificial intelligence seeks learning from the implementation of the data set, reducing computational cost and increasing accuracy.

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.

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