Abstract
River water plays a crucial role in the development of urban civilizations, necessitating the need for its continuous monitoring, despite the challenges involved. This paper focuses on the application of deep learning techniques to monitor the water quality in rivers. Such monitoring is essential to determine both the quantity and quality of available water, enabling sustainable management practices and informed decision-making. To accomplish this, various water testing stations have been established along the Kaveri River, collecting data from each location. By employing a hybrid approach that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), the level of contamination introduced into the water supply can be assessed. The data is collected using field cameras placed at the test sites to measure the water levels and a range of sensors to quantify the degree of contamination. A cloud-based web interface facilitates real-time monitoring, data storage, and data transfer functions. The proposed CNN-BiLSTM model was evaluated, resulting in an improved detection accuracy of 4.62%. This approach proves valuable in precisely assessing the quality and quantity of water in rivers, making it a useful tool for water resource management.
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The data used in the experiment is available with the corresponding author. It will be provided on request.
References
Ahmed W, Mohammed S, El-Shazly A, Morsy S (2023) Egypt J Remote Sens Space Sci 26(3):816–825. https://doi.org/10.1016/j.ejrs.2023.09.001. Tigris River water surface quality monitoring using remote sensing data and GIS techniques
Ampe EM, Raymaekers D, Hestir EL, Jansen M, Knaeps E, Batelaanc O (2015) IEEE Trans Geosci Remote Sens 53:869–882. https://doi.org/10.1109/TGRS.2014.2330251. A Wavelet-Enhanced Inversion Method for Water Quality Retrieval From High Spectral Resolution Data for Complex Waters
Belavadi SV, Rajagopal S, Ranjani R, Rajasekar M (2020) Air quality forecasting using LSTM RNN and wireless sensor networks. Procedia Comput Sci 170:241–248
Cao H, Guo Z, Wang S, Cheng H, Zhan C (2020) Intelligent wide-Area Water Quality Monitoring and Analysis System exploiting Unmanned Surface vehicles and Ensemble Learning. Water 12(3):1–15
Changchun L, Haihua Z, Dunbing T, Qingwei N, Shipei L, Yi Z, Xuan L (2022) A transfer learning CNN-BILSTM network-based production progress prediction approach in IIoT-enabled manufacturing. https://doi.org/10.1080/00207543.2022.2056860
Chao Niu K, Tan X, Jia X, Wang (2021) Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery. Environ Pollut 286:117534. https://doi.org/10.1016/j.envpol.2021.117534
Chellaswamy C, Saravanan M, Kanchana E, Shalini J Deep learning based pothole detection and reporting system. 7th International Conference on Smart, Structures (2020) and Systems (ICSSS) 1–6
Chellaswamy C, Geetha TS, Ramasubramanian B, Dhelipan Raj A, Dhilipkumar S, Koushikkaran K (2023) Smart River Water Quality and Level Monitoring: a Hybrid Neural Network Approach. 2023 International Conference on Advances in Intelligent Computing and Applications (AICAPS). 1–6, 2023. https://doi.org/10.1109/AICAPS57044.2023.10074495
Elbaz K, Shaban WM, Zhou A, Shen SL (2023) Real time image-based air quality forecasts using a 3D-CNN approach with an attention mechanism. Chemosphere 333:1–18. https://doi.org/10.1016/j.chemosphere.2023.138867
Escoto JE, Blanco AC, Argamosa RJ, Medina JM (2021) Pasig River Water Quality Estimation using an empirical ordinary least squares regression model of Sentinel-2 Satellite images. Int Arch Photogramm Rem Sens Spat Inf Sci 46:161–168
Ganesh Babu R, Chellaswamy C (2022) Different stages of disease detection in squash plant based on machine learning. J Biosci 47(9):1–14. https://doi.org/10.1007/s12038-021-00241-8
Hafeez S, Wong MS, Ho HC, Nazeer M, Nichol J, Abbas S, Tang D, Lee KH, Pun L (2019) Comparison of machine learning algorithms for retrieval of water quality indicators in case-II waters: a case study of Hong Kong, Remote Sens. 11(6):1–15. https://doi.org/10.3390/rs11060617
Hemdan EED, Essa YM, Shouman M et al (2023) An efficient IoT based smart water quality monitoring system. Multimed Tools Appl 82:28827–28851. https://doi.org/10.1007/s11042-023-14504-z
Hiroshi K, Koji O, Takuya N, Kazuyuki W, Kinshi K, Junichi H (2022) Grip Strength as a screening index for severe degenerative cervical myelopathy in primary care. Development of Cutoff Values Using Receiver Operating Curve Analysis, pp 9863–9872
Jiang F, Sha K, Lin C et al (2023) Node layout optimization strategy based on Aquaculture Water Quality Monitoring System. Wirel Pers Commun 132:2839–2856. https://doi.org/10.1007/s11277-023-10745-1
Junhao Q, Yuhang D, Xinqing X (2023) Solar powered wireless water quality monitoring system for ornamental fish. Results Eng 17:101016. https://doi.org/10.1016/j.rineng.2023.101016
Kumar JM, Kumari SR, Rashmitha MS, Sinha R, Sujatha B, Suma KV (2018) Smart Water Monitoring System for Real-Time Water Quality and Usage Monitoring, 2018 International Conference on Inventive Research in Computing Applications (ICIRCA) 617–621. https://doi.org/10.1109/ICIRCA.2018.8597179
Luo S, Wei B, Chen L (2024) Multi-point deformation monitoring model of concrete arch dam based on MVMD and 3D-CNN. Appl Math Model 125:812–826. https://doi.org/10.1016/j.apm.2023.10.030
Madeo D, Pozzebon A, Mocenni C, Bertoni D (2020) A low-cost unmanned surface vehicle for pervasive water quality monitoring. IEEE Trans Instrum Meas 69:1433–1444. https://doi.org/10.1109/TIM.2019.2963515
Matlab (2023) https://in.mathworks.com/help/deeplearning/ug/monitor-deep-learning-training-progress.html?searchHighlight=training%20progress%20plot&s_tid=srchtitle_training%20progress%20plot_5
Matta G, Kumar A, Nayak A et al (2022) Pollution complexity quantification using NPI and HPI of River Ganga system in Himalayan Region, Proc.Indian Natl. Sci. Acad. 88:651–663. https://doi.org/10.1007/s43538-022-00111-3
Matta G, Kumar A, Nayak A, Kumar P, Kumar A, Naik PK, Singh SK (2023) Assessing heavy metal index referencing health risk in Ganga River System. Int J River Basin Manage 21:759–769. https://doi.org/10.1080/15715124.2022.2098756
McDowell RW, Noble A, Kittridge M et al (2024) Monitoring to detect changes in water quality to meet policy objectives. Sci Rep 14:1–19. https://doi.org/10.1038/s41598-024-52512-7
Mei P, Li M, Zhang Q, Li G, Song L (2022) Prediction model of drinking water source quality with potential industrial-agricultural pollution based on CNN-GRU-Attention. J Hydrol 610:1–18. https://doi.org/10.1016/j.jhydrol.2022.127934
Moeinzadeh H, Jegakumaran P, Yong KT, Withana A (2023) Efficient water quality prediction by synthesizing seven heavy metal parameters using deep neural network. J Water Process Eng 56:1–18. https://doi.org/10.1016/j.jwpe.2023.104349
Mohammed NI, Bamarni KA (2019) Water quality monitoring of Duhok Dam (Kurdistan Region of Iraq). Zanco J Pure Appl Sci 31(1):7–16. https://doi.org/10.21271/ZJPAS.31.1.2
Nayak A, Matta G, Uniyal DP, Kumar A, Kumar P, Pant G (2023) Assessment of potentially toxic elements in groundwater through interpolation, pollution indices, and chemometric techniques in Dehradun in Uttarakhand State. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-023-27419-x
Nila Rekha P, Nishan Raja R, Albin Sunny S, Sarkar, Nedun R (2023) Optimizing Brackishwater Shrimp Farming with IoT-Enabled Water Quality Monitoring and Decision Support System, Thalassas: An International Journal of Marine Sciences. https://doi.org/10.1007/s41208-023-00630-w
Pany R, Rath A, Chandra Swain PC (2023) Water quality assessment for River Mahanadi of Odisha, India using statistical techniques and Artificial neural networks. J Clean Prod 417:1–16. https://doi.org/10.1016/j.jclepro.2023.137713
Pujar PM, Kenchannavar HH, Kulkarni RM, Umakant PK (2020) Real-time water quality monitoring through internet of things and ANOVA-based analysis: a case study on river Krishna. Appl Water Sci 10(22):1–16. https://doi.org/10.1007/s13201-019-1111-9
Singh S, Rai S, Singh P, Vijay Kumar Mishra (2022) Real-time water quality monitoring of River Ganga (India) using internet of things. Ecol Inf 71:1–15. https://doi.org/10.1016/j.ecoinf.2022.101770
Aires URV, da Silva DD, Fernandes Filho EI, Rodrigues LN, Uliana EM, Amorim RSS, de Melo Ribeiro CB, Campos JA (2023) Machine learning-based modeling of surface sediment concentration in Doce river basin. J Hydrol 619:1–14. https://doi.org/10.1016/j.jhydrol.2023.129320
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Geetha, T.S., Chellaswamy, C., Raja, E. et al. Deep learning for river water quality monitoring: a CNN-BiLSTM approach along the Kaveri River. Sustain. Water Resour. Manag. 10, 125 (2024). https://doi.org/10.1007/s40899-024-01102-6
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DOI: https://doi.org/10.1007/s40899-024-01102-6