Short-range reservoir inflow forecasting using hydrological and large-scale atmospheric circulation information

Maddu, Rajesh; Pradhan, Indranil; Ahmadisharaf, Ebrahim; Singh, Shailesh Kumar; Rehana, S.

doi:10.1016/j.jhydrol.2022.128153

Cited by 13 publications

(3 citation statements)

References 34 publications

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“…By integrating forecast information into operational strategies, reservoir operators can optimize water releases, storage levels, and hydropower generation, leading to improved water resource management and enhanced resilience to changing hydrological conditions. This approach enables proactive and adaptive reservoir operations that take into account anticipated future conditions, contributing to efficient water allocation, flood control, and ecosystem preservation [58][59][60].…”

Section: Figurementioning

confidence: 99%

“…Figures 6 and 7 delineate the appraisal interims of water shortage characteristics for the simulation approach utilizing genetic algorithms (GAs) with and without the smooth- The rule curves were subjected to an appraisal to assess the events of water shortage and excess discharge by comparing important characteristics, such as recurrence, magnitude, and term with those of the optimal curves. The assessment was performed employing a Monte Carlo simulation, producing 500 tests of month-to-month streams from stations P.12 and SK [60]. The results of the appraisal provide the interval of the insights (cruel ± standard deviation).…”

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confidence: 99%

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Concern Condition for Applying Optimization Techniques with Reservoir Simulation Model for Searching Optimal Rule Curves

Sriworamas,

Prasanchum,

Ashrafi

et al. 2023

Water

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This paper presents a comprehensive review of optimization algorithms utilized in reservoir simulation-optimization models, specifically focusing on determining optimal rule curves. The study explores critical conditions essential for the optimization process, including inflow data, objective and smoothing functions, downstream water demand, initial reservoir characteristics, evaluation scenarios, and stop criteria. By examining these factors, the paper provides valuable insights into the effective application of optimization algorithms in reservoir operations. Furthermore, the paper discusses the application of popular optimization algorithms, namely the genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search (CS), and tabu search (TS), highlighting how researchers can utilize them in their studies. The findings of this review indicate that identifying optimal conditions and considering future scenarios contribute to the derivation of optimal rule curves for anticipated situations. The implementation of these curves can significantly enhance reservoir management practices and facilitate the resolution of water resource challenges, such as floods and droughts.

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Section: Figurementioning

confidence: 99%

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confidence: 99%

Concern Condition for Applying Optimization Techniques with Reservoir Simulation Model for Searching Optimal Rule Curves

Sriworamas,

Prasanchum,

Ashrafi

et al. 2023

Water

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“…Accurate and reliable runoff forecasting provides data support for reservoir scheduling decisions, enhances the benefits of reservoirs under the condition of ensuring flood safety, and ultimately achieves the goal of optimizing water resources allocation, flood control, and disaster reduction [1]. However, with the increasing frequency of climate change and human activities the runoff series exhibit more obvious nonlinearity and nonstationarity, which has enhanced the complexity of runoff forecasting [2].…”

Section: Introductionmentioning

confidence: 99%

Investigation of the EWT–PSO–SVM Model for Runoff Forecasting in the Karst Area

Yan

et al. 2023

Applied Sciences

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As the runoff series exhibit nonlinear and nonstationary characteristics, capturing the embedded periodicity and regularity in the runoff series using a single model is challenging. To account for these runoff characteristics and enhance the forecasting precision, this research proposed a new empirical wavelet transform–particle swarm optimization–support vector machine (EWT–PSO–SVM) hybrid model based on “decomposition-forecasting-reconstruction” for runoff forecasting and investigated its effectiveness in the karst area. First, empirical wavelet transform (EWT) was employed to decompose the original runoff series into multiple subseries. Second, the support vector machine (SVM) optimized by particle swarm optimization (PSO) was applied to forecast every signal subseries. Finally, this study summarized the predictions of the subseries to reconstruct the ultimate runoff forecasting. The developed forecasting model was assessed by applying the monthly runoff series of the Chengbi River Karst Basin, and the composite rating index combined with five metrics was adopted as the performance evaluation tool. From the results of this research, it is clear that the EWT–PSO–SVM model outperforms both the PSO–SVM model and the SVM model in terms of the composite rating index, reaching 0.68. Furthermore, verifying the performance stability, the developed model was also compared with PSO–SVM and SVM models under different input data structures. The comparison demonstrated that the hybrid EWT–PSO–SVM model had a robust performance superiority and was an effective model that can be applied to karst area runoff forecasting.

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AI-Based Rainfall-Runoff Modelling for Sustainable Water Management in Potteruvagu Watershed, India

Raja Shekar,

Mathew

2024

Geospatial Technology to Support Communities and Policy

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Short-range reservoir inflow forecasting using hydrological and large-scale atmospheric circulation information

Cited by 13 publications

References 34 publications

Concern Condition for Applying Optimization Techniques with Reservoir Simulation Model for Searching Optimal Rule Curves

Concern Condition for Applying Optimization Techniques with Reservoir Simulation Model for Searching Optimal Rule Curves

Investigation of the EWT–PSO–SVM Model for Runoff Forecasting in the Karst Area

AI-Based Rainfall-Runoff Modelling for Sustainable Water Management in Potteruvagu Watershed, India

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