The use of feed-forward back propagation and cascade correlation for the neural network prediction of surface water quality parameters

Elbisy, Moussa S.; Ali, Hatem M.; AbdElall, M. A.; Alaboud, Turki M.

doi:10.1134/s0097807814060153

Cited by 11 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A constructive neural network that aims to solve the problems of the determination of potential neurons which are not relevant to the output layer [49] MNNs A special feedforward network Choosing the neural network which have the maximum similarity between the inputs and centroids of the cluster Solving the problem of low prediction accuracy [30,50] RNNs The RNNs are developed with the development of deep learning Solving the problems of long-term dependence which are not captured by the feedforward network [12,31,38,51,52] LSTMs Its structure is similar to RNNs Memory cell state is added to hidden layer Addressing the well-known vanishing gradient problem of RNNs [15,26,45,53,54] TLRN Its structure is similar to MLPs It has the local recurrent connections in the hidden layer…”

Section: Mlpsmentioning

confidence: 99%

See 1 more Smart Citation

A Review of the Artificial Neural Network Models for Water Quality Prediction

et al. 2020

View full text Add to dashboard Cite

Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.

show abstract

Section: Mlpsmentioning

confidence: 99%

“…Results demonstrated the effectiveness of the proposed methods. Researchers tended to divide the training set data into 70% to 90% of the total data [39,42,49,52,72,[120][121][122][123][124][125][126][127]. Iglesias et al [35] divided the data into training (90%) and testing sets (10%).…”

Section: Artificial Neural Network Models For Water Quality Predictionmentioning

confidence: 99%

A Review of the Artificial Neural Network Models for Water Quality Prediction

et al. 2020

View full text Add to dashboard Cite

show abstract

“…ANN's have demonstrated the ability to derive highly nonlinear relationships, and they can be continually updated as more data is collected. Furthermore, this process modeling approach has been successfully implemented by a number of researchers to develop accurate model predictions in a number of application areas, including fuel cells, batteries, heat exchangers, chemical reactions, and surface water quality parameters (Yu and Gomm, 2003;Singh et al, 2009;Vasickaninova et al, 2011;Shen et al, 2013;Chen et al, 2014;Elbisy et al, 2014).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A Feasibility Study for the Automated Monitoring and Control of Mine Water Discharges

Vass¹

View full text Add to dashboard Cite

The chemical treatment of mine-influenced waters is a longstanding environmental challenge for many coal operators, particularly in Central Appalachia. Mining conditions in this region present several unique obstacles to meeting NPDES effluent limits. Outlets that discharge effluent are often located in remote areas with challenging terrain where conditions do not facilitate the implementation of large-scale commercial treatment systems. Furthermore, maintenance of these systems is often laborious, expensive, and time consuming. Many large mining complexes discharge water from numerous outlets, while using environmental technicians to assess the water quality and treatment process multiple times per day. Unfortunately, this treatment method when combined with the lower limits associated with increased regulatory scrutiny can lead to the discharge of non-compliant water off of the mine permit. As an alternative solution, this thesis describes the ongoing research and development of automated protocols for the treatment and monitoring of mine water discharges. In particular, the current work highlights machine learning algorithms as a potential solution for pH control. I would first like to express my sincere gratitude to my committee chair, Dr. Aaron Noble for his expert guidance, understanding, support, and encouragement. While Dr. Noble is technically my advisor, his charisma, wisdom, and astute analytical reasoning have placed his role much closer to being a mentor and friend. Given his spirit of adventure in regard to research and genuine excitement in the betterment of others, Dr. Noble has established himself as a role model to to both his students and peers. Next, I would like to thank my committee members Dr. Keith Heasley and Dr. Mark Sindelar for their time, advice, and insightful comments. They have provided a wealth of knowledge, which I will never forget. Both Fernando Campos and Lucas Santos assisted with the generation of much of the data presented in this thesis. Their hard work and painstaking effort helped to make this project successful. Fernando and Lucas are both owed a debt of gratitude for their help and support. This work would not have been accomplished if not for my lovely wife Rachel. Her encouragement, quiet patience, and unwavering love were undeniably the bedrock upon which the last eight years of my life have been built. Rachel's tolerance for my occasional crude temperament is a testament in itself of her unyielding devotion and love. Finally, I would like to thank my father Casey. Dad has taught me the value of a solid work ethic and provided an example of a high ethical standard which has been the foundation of the many accomplishments I have achieved. Without his guidance and support I would undoubtedly be far less of a man than I am today.

show abstract