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

Abstract

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.

Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test

“…It helps to decrease the complexity of the final classifier, hence raising prognostic precision by reducing overfitting into the dataset, which is the essential benefit of the REPTree method. Backward overfitting is the main responsibility of the pruning operation realized by applying the REPTree model from a computational perspective [ 77 ]. This is a fundamental technique of decision tree construction that uses condensed error trimming to construct a regression tree based on variance data, using the REPTree method [ 98 ].…”

“…Random Subspace is a particularly effective algorithm when there is a small number of training datasets compared to the amount of data to analyze [ 75 ]. This technique introduces randomness into the formulation of issues by selecting certain variables and substituting them at random in a random place [ 77 ]. As a robust algorithm, this algorithm combines various weak classifiers in order to produce a robust classifier [ 101 , 102 ].…”

“…The M5P model tree is a regression model for the continuous variables. It is an improved model of the M5 technique which can efficiently handle large datasets with high dimensionality [ 77 ]. M5P follows a multivariate linear regression model to create classification and regression trees through a rapid, simple, and precise procedure.…”

“…In light of the above-mentioned state-of-the-arts, the broader scientific literature, as well as the author's understanding, there have been no studies that have explored the hybridization of linear regression (LR) with other machine learning techniques i.e., linear regression-random subspace (LR-RSS), linear regression-reduced error pruning tree (LR-REPTree), linear regression-support vector machine (LR-SVM) as well as linear regression-M5 pruned models for forecasting stage-discharge relationships,. Many researchers have applied machine learning algorithms and compared the performances [ [74] , [75] , [76] , [77] , [78] , [79] , [80] ] but have not explored the hybrid algorithms for the study stations. Therefore, this study aims to develop the hybrid models of LR with other machine learning algorithms so that the performance of the LR algorithm may be enhanced for forecasting the rating curve and discharge prediction using hydrological data.…”

The Influence of Reservoirs on Water Temperature in the Downstream Part of an Open Watercourse: A Case Study at Botovo Station on the Drava River

“…Since the building of dams can significantly impact downstream water quality and quantity, it is obligatory to control the flow and minimize the water temperature concerning spillway operations. Advanced machine learning is suitable for such analysis [9] due to its suitability.…”