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Volume 13 | Issue 7 | Year 2026 | Article Id. IJCE-V13I7P119 | DOI : https://doi.org/10.14445/23488352/IJCE-V13I7P119

Spatio-Temporal Reservoir Water Quality Assessment Using PSO-Adam Optimized Vision Transformers

Sachchidanand Bhagat, L. B. Roy

Received Revised Accepted Published
27 Dec 2025 01 Jun 2026 04 Jul 2026 29 Jul 2026

Citation :

Sachchidanand Bhagat, L. B. Roy, "Spatio-Temporal Reservoir Water Quality Assessment Using PSO-Adam Optimized Vision Transformers," International Journal of Civil Engineering, vol. 13, no. 7, pp. 297-322, 2026. Crossref, https://doi.org/10.14445/23488352/IJCE-V13I7P119

Abstract

Continuous Water Quality (WQ) monitoring and measurement in dam reservoirs at different locations in the same dam water regions are the major problem. Spatial heterogeneity led to variations in WQ metrics such as pH, DO, TDS in the dam reservoir based on different depths due to sedimentation and nutrient cycling. Manual sampling methods is not suitable for continues WQ parameter measuring in different locations of dam water regions. To solve the above problem, Landsat image of dam water regions is processed with the Transverse Dyadic Wavelet Transform (TDyWT) and the Adam Optimized Vision Transformer (AdamViT) algorithms and obtained the statistical features of water regions for the temporal and spatial regions. Nagi Dam (NGD) and Nakti Dam (NKD) reservoirs in Bihar, India is the study area for the proposed study. The statistical values of water regions measured are the mean, entropy, PSNR, band values. The obtain statistical water feature values are correlated with laboratory-based sample water measured four WQ metrics. AdamViT- Bayesian Optimised-(BO)- (Support Vector Regression) SVR predicts the above four Key WQ metrics. AdamViT- BO-SVR provide the R2 value 0.97, RMSE is 0.15, and MAPE is 3.4% compared to the ground truth.

Keywords

Water Quality metrics measurement, Wavelet Transform, Vision Transformer, Support Vector Regression.

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