Restricted accessResearch articleFirst published online 2025-3
An effective illustrative approach for water quality prediction using spatio temporal features with cross attention-based adaptive long short-term memory
Water quality prediction is significant for handling water resources for ensuring the quality of drinking water. Forecasting the chemical parameters present in water at a specific point is necessary for predicting the quality of water. Uneven observation results in missing information, which makes it challenging to make exact forecasts. Conventional techniques for predicting water quality are quite straightforward and linear models, which are frequently employed. Nevertheless, these models have restrictions in terms of forecasting the nonlinear features of water quality. Due to the intricate water environment, the water quality time series is significant in this work. In this article, effective water quality prediction using a spatio-temporal features model is developed and implemented. At first, the requisite data will be fetched from the standard online resources. After that, the attained data are given to the Spatio-Temporal Features with Cross Attention-based Adaptive Long Short Term Memory (SRF-CAALSTM) technique, where the quality of the water is predicted. In order to enrich the system functionality the parameters of the LSTM model is tuned with the assistance of the Improved Osprey Optimization Algorithm (IOOA). Thus, effective predicted outcomes are attained for the water quality. In the end, extensive analyses are carried out to validate and contrast the suggested prediction model with the conventional mechanisms. This assured the effectiveness of the recommended water quality prediction approach.
GuoLLuoWTianQ. Water quality prediction in the Yellow River source area based on the DeepTCN-GRU model. J Water Proc Eng2024; 59: 105052.
2.
ZhangSOmarAHHashimAS, et al.Enhancing waste management and prediction of water quality in the sustainable urban environment using optimized algorithm of least square support vector machine and deep learning techniques. Urban Climate2023; 49: 101487.
3.
WanHXuRZhangM, et al.A novel model for water quality prediction caused by non-point sources pollution based on deep learning and feature extraction methods. J Hydrol2022; 612: 128081.
4.
LiuWLiuTLiuZ, et al.A novel deep learning ensemble model based on two-stage feature selection and intelligent optimization for water quality prediction. Environ Res2023; 224: 115560.
5.
IslamNIrshadK. Artificial ecosystem optimization with deep learning enabled water quality prediction and classification model. Chemosphere2022; 309: 136615.
6.
ShimJHongSLeeJ, et al.Deep learning with data preprocessing methods for water quality prediction in ultrafiltration. J Clean Prod2023; 428: 139217.
7.
ZhouXWangJLiuY,et al.Deep learning with PID residual elimination network for time-series prediction of water quality in aquaculture industry. Comput Electron Agric2023; 212: 108125.
8.
ZhengHLiuYWanW, et al.Large-scale prediction of stream water quality using an interpretable deep learning approach. J Environ Manag2023; 331: 117309.
9.
WangJXueBWangY, et al.Identification of pollution source and prediction of water quality based on deep learning techniques. J Contam Hydrol2024; 261: 104287.
10.
MaierHRJainADandyGC,et al.Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environ Modell Soft2010; 5: 891–909.
11.
SlivaLWilliamsDD. Buffer zone versus whole catchment approaches to studying land use impact on river water quality. Water Res2001; 35: 3462–3472.
12.
AbbaspourKCYangJMaximovI, et al.Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. J Hydrol2007; 333: 413–430.
13.
GholizadehMHMelesseAMReddiL. A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors2016; 16: 1298.
14.
MoriasiDNGitauMWPaiN,et al.Hydrologic and water quality models: performance measures and evaluation criteria. Transact ASABE2015; 58: 1763–1785.
15.
DelplaIJungA-VBauresE, et al.Impacts of climate change on surface water quality in relation to drinking water production. Environ Int2009; 35: 1225–1233.
16.
AlbertoWDdel PilarDMValeriaAM, et al.Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. a case study: Suquı´a river basin (Córdoba–Argentina). Water Res2001; 35: 2881–2894.
17.
TanBNgCNshimyimanaJP, et al.Next-generation sequencing (NGS) for assessment of microbial water quality current progress, challenges, and future opportunities. Front Microbiol2015; 6: 1027.
18.
LiuJYuCHuZ, et al.Accurate prediction scheme of water quality in smart mariculture with deep Bi-S-SRU learning network. IEEE Access2020; 8: 24784–24798.
19.
NooriNKalinLIsikS. Water quality prediction using SWAT-ANN coupled approach. J Hydrol2020; 590: 125220.
20.
El BilaliATalebABrouziyneY. Groundwater quality forecasting using machine learning algorithms for irrigation purposes. Agric Water Manag2021; 245: 106625.
21.
HaqKRAHarigovindanVP. Water quality prediction for smart aquaculture using hybrid deep learning models. Ieee Access2022; 10: 60078–60098.
22.
ZhangQWangRQiY,et al.A watershed water quality prediction model based on attention mechanism and Bi-LSTM. Environ Sci Pollut Res2022; 29: 75664–75680.
23.
GeorgescuP-LMoldovanuSIticescuC, et al.Assessing and forecasting water quality in the Danube river by using neural network approaches. Sci Total Environ2023; 879: 162998.
24.
PetersonKTSaganVSloanJJ. Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing. GIScience & Remote Sensing2020; 57: 510–525.
25.
BarzegarRAalamiMTAdamowskiJ. Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model. Stoch Environ Res Risk Assess2020; 34: 415–433.
26.
ZhengYWeiJZhangW, et al.An ensemble model for accurate prediction of key water quality parameters in river based on deep learning methods. J Environ Manag2024; 366: 121932.
27.
NongXLaiCChenL,et al.A novel coupling interpretable machine learning framework for water quality prediction and environmental effect understanding in different flow discharge regulations of hydro-projects. Sci Total Environ2024; 950: 175281.
28.
ZhengZDingHWengZ,et al.Research on out-of-sample prediction method of water quality parameters based on dual-attention mechanism. Environ Modell Soft2024; 176: 106020.
29.
MaheshNJagan BabuJNithyaK,et al.Water quality prediction using LSTM with combined normalizer for efficient water management”. Desalin water Treat2024; 317: 100183.
30.
ShabanWMXieDElbazK,et al.Real-time water quality prediction of wastewater treatment plants using advanced deep learning networks. J water process Eng2024; 65: 105775.
31.
MellalNEHTaharWBoumaazaM, et al.Prediction of purified water quality in industrial hydrocarbon wastewater treatment using an artificial neural network and response surface methodology. J Water process Eng2024; 58: 104757.
32.
DehghaniMTrojovskýP. Osprey optimization algorithm: A new bio-inspired metaheuristic algorithm for solving engineering optimization problems. Front Mech Eng2023; 8.
33.
PalchevskyEAntonovVFilimonovN, et al.Development of an intelligent system for early forecasting and modelling of flood situation on the example of the Republic of Bashkortostan using a proprietary machine and deep learning library. J Hydrol2024; 633: 130978.
34.
Al-BaikOAlomariSAlssayedO, et al.Pufferfish Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems. Biomimetics2024; 9: 65.
ChanJChanBHoHL, et al.The neonatal resuscitation algorithm organized cart is more efficient than the airway– breathing–circulation organized drawer: a crossover randomized control trial. Eur J Emerg Med2016; 23: 258–262.
37.
LiangL. Water pollution prediction based on deep belief network in big data of water environment monitoring. Sci Program2021; 1: 8271950.
38.
MuruganVEmilynJJ. Monitoring and forecasting of water quality and fish population using stacked LSTM-GRU in IoT environment,” pp. 1529–1536, 2021.
