Water Pollution Prevention and Control Project, 2023, 4(1); doi: 10.38007/WPPCP.2023.040103.
Baotou Teachers College, Baotou, China
Water resources are an important material resource for living and production. China is a country with poor water resources, and at the same time, the existing water resources in China are all polluted to varying degrees, especially the quality of surface water is closely related to the quality of people's production and life. With the boom in artificial neural network (ANN) research, neural networks have now been used in a number of fields such as graphics processing, expert decision making systems, sound processing, etc. due to the advantages of ANNs themselves, which have achieved amazing results. The theory has turned into a new multifaceted avant-garde discipline associated with multiple fields. In recent years, ANN research has been gradually applied to environmental science, some of which have applied ANN research to areas such as water eutrophication prediction and water quality prediction. The application of ANN technology to surface water quality prediction is at an early stage, and its characteristics make it a great advantage in this field. This paper investigates the use of BP ANNs to predict surface water quality, to make rapid and accurate predictions of surface water pollution, and to provide decisions for the protection of water resources and pollution prevention.
Artificial Neural Network, Water Pollution, Pollution Prevention, Raman Spectroscopy
Huimin Yang. Raman Spectroscopy System for Water Pollution Control based on Artificial Neural Network. Water Pollution Prevention and Control Project (2023), Vol. 4, Issue 1: 19-28. https://doi.org/10.38007/WPPCP.2023.040103.
 K. A. Tapia Espinoza, Marcos Leonardo Fuentes Ávila, V. F. Pichardo Oropeza, J. M. Falcón González, J. C. Martínez-Espinosa. Qualitative Analysis of Ciprofloxacin using RS and K-Means: Preliminary Results. Res. Comput. Sci. (2020) 149(2): 23-26.
 Imane Zouaneb, Mostefa Belarbi, Abdellah Chouarfia. Converging image processing and data mining for RS analysis. Int. J. Commun. Networks Distributed Syst. (2022) 28(3): 287-311. https://doi.org/10.1504/IJCNDS.2022.122176
 Cristian Ciobanu, Katherine J. I. Ember, Balázs J. Nyíri, Sreeraman Rajan, Vinita Chauhan, Frederic Leblond, Sangeeta Murugkar. Potential of RS for Blood-Based Biopsy. IEEE Instrum. Meas. Mag. (2022) 25(1): 62-68. https://doi.org/10.1109/MIM.2022.9693451
 Anurag Gupta, Syed Moosa Ali, Aswathy Vijaya Krishna, Arvind Sahay, Mini Raman. Role of Visible Spectroscopy in Bio-Optical Characterization of Coastal Waters. IEEE Geosci. Remote. Sens. Lett. (2021) 18(8): 1327-1331. https://doi.org/10.1109/LGRS.2020.3003663
 David Chen. Analysis of Machine Learning Methods for COVID-19 Detection Using Serum RS. Appl. Artif. Intell. (2021) 35(14): 1147-1168. https://doi.org/10.1080/08839514.2021.1975379
 Giuseppe Sciortino, Andrea Ragni, Alejandro De la Cadena, Marco Sampietro, Giulio Cerullo, Dario Polli, Giorgio Ferrari. Four-Channel Differential Lock-in Amplifiers with Autobalancing Network for Stimulated RS. IEEE J. Solid State Circuits. (2021) 56(6): 1859-1870. https://doi.org/10.1109/JSSC.2020.3046484
 Swati Chopade, Hari Prabhat Gupta, Rahul Mishra, Preti Kumari, Tanima Dutta. An Energy-Efficient River Water Pollution Monitoring System in Internet of Things. IEEE Trans. Green Commun. Netw. (2021) 5(2): 693- 702. https://doi.org/10.1109/TGCN.2021.3062470
 Amal Agarwal, Lingzhou Xue. Model-Based Clustering of Nonparametric Weighted Networks With Application to Water Pollution Analysis. Technometrics. (2020) 62(2): 161-172. https://doi.org/10.1080/00401706.2019.1623076
 K. Kamaraj, B. Lanitha, S. Karthic, P. N. Senthil Prakash, R. Mahaveerakannan. A Hybridized ANN for Automated Software Test Oracle. Comput. Syst. Sci. Eng. (2023) 45(2): 1837-1850. https://doi.org/10.32604/csse.2023.029703
 Junaid Rashid, Sumera Kanwal, Muhammad Wasif Nisar, Jungeun Kim, Amir Hussain. An ANN-Based Model for Effective Software Development Effort Estimation. Comput. Syst. Sci. Eng. (2023) 44(2): 1309-1324. https://doi.org/10.32604/csse.2023.026018
 Roman Englert, Jörg Muschiol. Numerical Evidence That the Power of ANNs Limits Strong AI. Adv. Artif. Intell. Mach. Learn. (2022) 2(2): 338-346. https://doi.org/10.54364/AAIML.2022.1122
 Pablo Negro, Claudia Pons. Artificial Intelligence techniques based on the integration of symbolic logic and deep neural networks: A systematic review of the literature. Inteligencia Artif. (2022) 25(69): 13-41. https://doi.org/10.4114/intartif.vol25iss69pp13-41
 Motoaki Hiraga, Kazuhiro Ohkura. Topology and weight evolving ANNs in cooperative transport by a robotic swarm. Artif. Life Robotics. (2022) 27(2): 324-332. https://doi.org/10.1007/s10015-021-00716-9
 Riya Aggarwal, Hassan Ugail, Ravi Kumar Jha. A deep ANN architecture for mesh free solutions of nonlinear boundary value problems. Appl. Intell. (2022) 52(1): 916-926. https://doi.org/10.1007/s10489-021-02474-4
 Faiyaz Ahmad. Deep image retrieval using ANN interpolation and indexing based on similarity measurement. CAAI Trans. Intell. Technol. (2022) 7(2): 200-218. https://doi.org/10.1049/cit2.12083
 G. Gokulkumari. Metaheuristic-Enabled ANN Framework for Multimodal Biometric Recognition with Local Fusion Visual Features. Comput. J. (2022) 65(6): 1586-1597. https://doi.org/10.1093/comjnl/bxab001
 Eddy Kwessi, Lloyd J. Edwards. ANNs with a signed-rank objective function and applications. Commun. Stat. Simul. Comput. (2022) 51(6): 3363-3388. https://doi.org/10.1080/03610918.2020.1714659
 M. Mohamed Asan Basiri. Versatile Architectures of ANN with Variable Capacity. Circuits Syst. Signal Process. (2022) 41(11): 6333-6353. https://doi.org/10.1007/s00034-022-02087-3