Pre- and post-dam river water temperature alteration prediction using advanced machine learning models

• Published in: Environmental science and pollution research international 2022-11, Vol.29 (55), p.83321-83346
• Main Authors: Vishwakarma, Dinesh Kumar, Ali, Rawshan, Bhat, Shakeel Ahmad, Elbeltagi, Ahmed, Kushwaha, Nand Lal, Kumar, Rohitashw, Rajput, Jitendra, Heddam, Salim, Kuriqi, Alban
• Format: Article
• Language: English
• Subjects: Algorithms; Aquatic habitats; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Construction; Correlation coefficient; Correlation coefficients; Criteria; Dam construction; Dams; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental science; Error reduction; Fluid flow; Goodness of fit; High flow; Hydrologic regime; Hydrology; Lag time; Learning algorithms; Machine Learning; Natural flow; Research Article; Rivers; Spillways; Temperature; Waste Water Technology; Water; Water levels; Water Management; Water Pollution Control; Water resources; Water temperature
• ISSN: 0944-1344; 1614-7499; 1614-7499
• DOI: 10.1007/s11356-022-21596-x

Dams significantly impact river hydrology by changing the timing, size, and frequency of low and high flows, resulting in a hydrologic regime that differs significantly from the natural flow regime before the impoundment. For precise planning and judicious use of available water resources for agricultural operations and aquatic habitats, it is critical to assess the dam water’s temperature accurately. The building of dams, particularly several dams in rivers, can significantly impact downstream water. In this study, we predict the daily water temperature of the Yangtze River at Cuntan. Thus, this work reveals the potential of machine learning models, namely, M5 Pruned (M5P), Random Forest (RF), Random Subspace (RSS), and Reduced Error Pruning Tree (REPTree). The best and effective input variables combinations were determined based on the correlation coefficient. The outputs of the various machine learning algorithm models were compared with recorded daily water temperature data using goodness-of-fit criteria and graphical analysis to arrive at a final comparison. Based on a number of criteria, numerical comparison between the models revealed that M5P model performed superior ( R 2 = 0.9920, 0.9708; PCC = 0.9960, 0.9853; MAE = 0.2387, 0.4285; RMSE = 0.3449, 0.4285; RAE = 6.2573, 11.5439; RRSE = 8.0288, 13.8282) in pre-impact and post-impact spam, respectively. These findings suggest that a huge wave of dam construction in the previous century altered the hydrologic regimes of large and minor rivers. This study will be helpful for the ecologists and river experts in planning new reservoirs to maintain the flows and minimize the water temperature concerning spillway operation. Finally, our findings revealed that these algorithms could reliably estimate water temperature using a day lag time input in water level. They are cost-effective techniques for forecasting purposes.