Water Pollution Prevention and Control Project, 2023, 4(2); doi: 10.38007/WPPCP.2023.040201.
Anhui Xinhua University, Hefei, China
Traditional water pollution(WP) detection is mainly based on the manual use of water quality testing equipment to identify samples at fixed points, this method of detection is a huge amount of work, and to pay the high cost of testing, in addition, in some unsuitable for operators to enter the WP testing environment, the use of traditional testing equipment is difficult to complete the sampling, so that the testing work can not be carried out as scheduled, and, the current water resources collection every six months or once a quarter, in this way to collect water quality data on a long time line, can not do water quality status updated. In order to solve these problems and build a stable water environment ecosystem, this paper studies WP detection equipment, builds a WP detection system based on artificial intelligence and sensors, and integrates intelligent means to detect pollutants and prevent WP. The system can complete the abnormal detection and recording of water pollutants through different interfaces and software systems, and its advantages of miniaturization and intelligence will be applied to actual production and life.
Artificial Intelligence, Water Pollution Prevention, Anomaly Detection, Water Resource Data
Jian Zhang. Anomaly Detection of Water Pollution Prevention Ecosystem Based on Artificial Intelligence. Water Pollution Prevention and Control Project (2023), Vol. 4, Issue 2: 1-9. https://doi.org/10.38007/WPPCP.2023.040201.
 C. Aswini, M. L. Valarmathi. Artificial Intelligence Based Smart Routing in Software Defined Networks. Comput. Syst. Sci. Eng. (2023) 44(2): 1279-1293. https://doi.org/10.32604/csse.2023.022023
 Pietro Fusco, Salvatore Venticinque, Rocco Aversa. An Application of Artificial Intelligence to Support the Discovering of Roman Centuriation Remains. IEEE Access. (2022) 10: 79192-79200 . https://doi.org/10.1109/ACCESS.2022.3194147
 Jose Mekha, V. Parthasarathy. An Automated Pest ldentification and Classification in Crops Using Artificial Intelligence - A State-of-Art-Review. Autom. Control. Comput. Sci. (2022) 56(3): 283-290. https://doi.org/10.3103/S0146411622030038
 Julia Arribas Anta, Ivan Martinez- Ballestero, Daniel Eiroa, Javier Garcia, Julia Rodriguez-Comas. Artificial intelligence for the detection of pancreatic lesions. Int. J Comput. Assist. Radiol. Surg. (2022) 17(10): 1855-1865 . https://doi.org/10.1007/s11548-022-02706-z
 Manikam Babu, Thangaraju Jesudas. An artificial intelligence-based smart health system for biological cognitive detection based on wireless telecommunication. Comput. Intell. (2022) 38(4): 1365-1378. https://doi.org/10.1111/coin.12513
 Rehab Alanazi, Ahamed Aljuhani. Anomaly Detection for Industrial Internet of Things Cyberattacks. Comput. Syst. Sci. Eng. (2023) 44(3): 2361-2378. https://doi.org/10.32604/csse.2023.026712
 Samuel A. Ajila, Chung-Horng Lung, Anurag Das. Analysis of error-based machine learning algorithms in network anomaly detection and categorization. Ann. des Telecommunications. (2022) 77(5-6): 359-370 . https://doi.org/10.1007/s12243-021-00836-0
 Taku Wakui, Takao Kondo, Fumio Teraoka. GAMPAL: an anomaly detection mechanism for Internet backbone traffic by flow size prediction with LSTM-RNN. Ann. des Telecommunications. (2022) 77(5-6): 437-454. https://doi.org/10.1007/s12243-021-00874-8
 Seonho Park, George Adosoglou, Panos M. Pardalos. Interpreting rate-distortion of variational autoencoder and using model uncertainty for anomaly detection. Ann. Math. Artif. Intell. (2022) 90(7-9): 735-752.
 Mohammad Kazim Hooshmand, Doreswamy Hosahalli. Network anomaly detection using deep learning techniques. CAAI Trans. Intell. Technol. (2022) 7(2): 228-243 . https://doi.org/10.1049/cit2.12078
 Felix M. Philip, Jayakrishnan V, Ajesh F, Haseena P. Video Anomaly Detection Using the Optimization-Enabled Deep Convolutional Neural Network. Comput. J. (2022) 65(5): 1272-1292. https://doi.org/10.1093/comjnl/bxaa177
 Lucas Bondan, Tim Wauters, Bruno Volckaert, Filip De Turck, Lisandro Zambenedetti Granville. NFV Anomaly Detection: Case Study through a Security Module. IEEE Commun. Mag. (2022) 60(2): 18-24. https://doi.org/10.1109/MCOM.001.2100408
 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
 Malik Kaya. Fiber optic chemical sensors for water testing by using fiber loop ringdown spectroscopy technique. Turkish J. Electr. Eng. Comput. Sci. (2020) 28(5): 2375-2384. https://doi.org/10.3906/elk-2005-39
 Mohammad Sadegh Sadeghi Garmaroodi, Faezeh Farivar, Mohammad Sayad Haghighi, Mahdi Aliyari Shoorehdeli, Alireza Jolfaei. Detection of Anomalies in Industrial IoT Systems by Data Mining: Study of CHRIST Osmotron Water Purification System. IEEE Internet Things J. (2021) 8(13): 10280-10287. https://doi.org/10.1109/JIOT.2020.3034311
 Adam Niewiadomski, Marcin Kacprowicz. Type-2 Fuzzy Logic Systems in Applications: Managing Data in Selective Catalytic Reduction for Air Pollution Prevention. J. Artif. Intell. Soft Comput. Res. (2021) 11(2): 85-97. https://doi.org/10.2478/jaiscr-2021-0006
 Victor Manuel Zezatti, Alberto Ochoa, Gustavo Urquiza, Miguel Basurto, Laura Castro, Juan Garcia. The Implementation of a Nickel-Electroless Coating in Heat Exchanger Pipes Considering the Problem of the Environmental Conditions ofthe Cooling Water Without Recirculation to Increase the Effectiveness Under Uncertainty. Int. J. Comb. Optim. Probl. Informatics. (2022) 13(4): 73-82.