Integration of deep learning and improved multi-objective algorithm to optimize reservoir operation for balancing human and downstream ecological needs.

Dam (reservoir)-induced alterations of flow and water temperature regimes can threaten downstream fish habitats and native aquatic ecosystems. Alleviating the negative environmental impacts of dam-reservoir and balancing the multiple purposes of reservoir operation have attracted wide attention. While previous studies have incorporated ecological flow requirements in reservoir operation strategies, a comprehensive analysis of trade-offs among hydropower benefits, ecological flow, and ecological water temperature demands is lacking. Hence, this study develops a multi-objective ecological scheduling model, considering total power generation, ecological flow guarantee index, and ecological water temperature guarantee index simultaneously. The model is based on an integrated multi-objective simulation-optimization (MOSO) framework which is applied to Three Gorges Reservoir. To that end, first, a hybrid long short-term memory and one-dimensional convolutional neural network (LSTM_1DCNN) model is utilized to simulate the dam discharge temperature. Then, an improved epsilon multi-objective ant colony optimization for continuous domain algorithm (ε-MOACOR) is proposed to investigate the trade-offs among the competing objectives. Results show that LSTM _1DCNN outperforms other competing models in predicting dam discharge temperature. The conflicts among economic and ecological objectives are often prominent. The proposed ε-MOACOR has potential in resolving such conflicts and has high efficiency in solving multi-objective benchmark tests as well as reservoir optimization problem. More realistic and pragmatic Pareto-optimal solutions for typical dry, normal and wet years can be generated by the MOSO framework. The ecological water temperature guarantee index objective, which should be considered in reservoir operation, can be improved as inflow discharge increases or the temporal distribution of dam discharge volume becomes more uneven.

Influence of the impoundment of the Three Gorges Reservoir on hydrothermal conditions for fish habitat in the Yangtze River.

The thermal regimes of rivers play an important role in the overall health of aquatic ecosystems. Modifications to water temperature regimes resulting from dams and reservoirs have important consequences for river ecosystems. This study investigates the impacts of the impoundment of the Three Gorges Reservoir (TGR) on the water temperature regime of fish spawning habitats in the middle reach of the Yangtze River, China. Mike 11 model is used to analyze the temporal and spatial variation of water temperatures of the expanse of 400 km along the river, from Yichang to Chenglingji. The water temperature alterations caused by the operation of the TGR are assessed with river temperature metrics. The impact on spawning habitats due to water temperature variation was also discussed in different impoundments of the TGR. The results show that the TGR has significantly altered the downstream water temperature regime, affecting the baseline deviation and phase shift of the water temperature. Such impacts on the thermal regime of the river varied with the impoundment level. The effects of the TGR on the water temperature regime decreased as the distance from the structure to the sample site increased. The water temperature regime alterations have led to the delay of the spawning times of the four famous major carp (FFMC) species. The results could be used to identify the magnitudes of water temperature alterations induced by reservoirs in the Yangtze River and provide useful information to design ecological operations for the protection of river ecosystem integrity in regulated rivers.

Water temperature forecasting based on modified artificial neural network methods: Two cases of the Yangtze River.

Water temperature is a controlling indicator of river habitat since many physical, chemical and biological processes in rivers are temperature dependent. Highly precise and reliable predictions of water temperature are important for river ecological management. In this study, a hybrid model named BP_PSO3, based on the BPNN (back propagation neural network) optimized by the PSO (particle swarm optimization) algorithm, is proposed for water temperature prediction using air temperature (Ta), discharge (Q) and day of year (DOY) as input variables. The performance of the BP_PSO3 model was compared with that of the BP_PSO1 (with Ta as the input) and BP_PSO2 (with Ta and Q as the inputs) models to evaluate the importance of the inputs. In addition, a comparison among the BPNN, RBFNN (radial basis function neural network), WNN (wavelet neural network), GRNN (general regression neural network), ELMNN (Elman neural network), and BP_PSO-based models was carried out based on the MAE, RMSE, NSE and R2. The eight artificial intelligence models were examined to predict the water temperature at the Cuntan and Datong stations in the Yangtze River. The results indicated that the hybrid BPNN-PSO3 model had a stronger ability to forecast water temperature under both normal and extreme drought conditions. Optimization by the PSO algorithm and the inclusion of Q and DOY could help capture river thermal dynamics more accurately. The findings of this study could provide scientific references for river water temperature forecasting and river ecosystem protection.