Floodwater Utilization Based on Reservoir Pre-Release Strategy Considering the Worst-Case Scenario

Hua, Lijuan; Wan, Xinyu; Wang, Xianhui; Zhao, Fangzheng; Zhong, Ping‐an; Liu, Moyang; Yang, Qun

doi:10.3390/w12030892

Cited by 16 publications

(3 citation statements)

References 31 publications

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“…In the total actual optimal water supplement, the proportions of flood resource supply under the three different scenarios were (27.49%, 29.05%), (33.85%, 36.87%), and (39.01%, 43.39%), respectively. In addition, the proportion of flood resource supply increased with the increase in flood flow, indicating that complete utilization of the flood resources under various scenarios could meet the water demand of MNNR and alleviate the water shortage to a certain extent [ 35 ]. Furthermore, the ecological water replenishment scheme after optimization could make full use of flood resources and improve the utilization rate of transit flood resources in MNNR, which is supported by conclusions reported by Liu [ 9 ] and Qiu et al [ 11 ].…”

Section: Resultsmentioning

confidence: 99%

Optimization of Ecological Water Supplement Scheme for Improved Suitable Habitat Area for Rare Migratory Birds in Nature Reserves Using Interval-Parameter Fuzzy Two-Stage Stochastic Programming Model

Liao

Meng

Ren

et al. 2020

IJERPH

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The optimization of ecological water supplement scheme in Momoge National Nature Reserve (MNNR), using an interval-parameter two-stage stochastic programming model (IPTSP), still experiences problems with fuzzy uncertainties and the wide scope of the obtained optimization schemes. These two limitations pose a high risk of system failure causing high decision risk for decision-makers and render it difficult to further undertake optimization schemes respectively. Therefore, an interval-parameter fuzzy two-stage stochastic programming (IPFTSP) model derived from an IPTSP model was constructed to address the random variable, the interval uncertainties and the fuzzy uncertainties in the water management system in the present study, to reduce decision risk and narrow down the scope of the optimization schemes. The constructed IPFTSP model was subsequently applied to the optimization of the ecological water supplement scheme of MNNR under different scenarios, to maximize the recovered habitat area and the carrying capacity for rare migratory water birds. As per the results of the IPFTSP model, the recovered habitat areas for rare migratory birds under low, medium and high flood flow scenarios were (14.06, 17.88) × 103, (14.92, 18.96) × 103 and (15.83, 19.43) × 103 ha, respectively, and the target value was (14.60, 18.47) × 103 ha with a fuzzy membership of (0.01, 0.83). Fuzzy membership reflects the possibility level that the model solutions satisfy the target value and the corresponding decision risk. We further observed that the habitat area recovered by the optimization schemes of the IPFTSP model was significantly increased compared to the recommended scheme, and the increases observed were (5.22%, 33.78%), (11.62%, 41.88%) and (18.44%, 45.39%). In addition, the interval widths of the recovered habitat areas in the IPFTSP model were reduced by 17.15%, 17.98% and 23.86%, in comparison to those from the IPTSP model. It was revealed that the IPFTSP model, besides generating the optimal decision schemes under different scenarios for decision-makers to select and providing decision space to adjust the decision schemes, also shortened the decision range, thereby reducing the decision risk and the difficulty of undertaking decision schemes. In addition, the fuzzy membership obtained from the IPFTSP model, reflecting the relationship among the possibility level, the target value, and the decision risk, assists the decision-makers in planning the ecological water supplement scheme with a preference for target value and decision risk.

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Section: Resultsmentioning

confidence: 99%

Optimization of Ecological Water Supplement Scheme for Improved Suitable Habitat Area for Rare Migratory Birds in Nature Reserves Using Interval-Parameter Fuzzy Two-Stage Stochastic Programming Model

Liao

Meng

Ren

et al. 2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…They proposed incorporating the depiction of the first flood and extending it to the user's current position. Hue et al [29] developed a flood forecasting model using elman neural networks for the Xianghongdian reservoir in East China. The model is trained with a real-time recurrent learning algorithm and meets the required precision.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Momentum-Backpropagation Algorithm for Flood Prediction and Management in the Internet of Things

Thankappan,

Mary,

Yoon

et al. 2023

CMC

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Flooding is a hazardous natural calamity that causes significant damage to lives and infrastructure in the real world. Therefore, timely and accurate decision-making is essential for mitigating flood-related damages. The traditional flood prediction techniques often encounter challenges in accuracy, timeliness, complexity in handling dynamic flood patterns and leading to substandard flood management strategies. To address these challenges, there is a need for advanced machine learning models that can effectively analyze Internet of Things (IoT)-generated flood data and provide timely and accurate flood predictions. This paper proposes a novel approach-the Adaptive Momentum and Backpropagation (AM-BP) algorithm-for flood prediction and management in IoT networks. The AM-BP model combines the advantages of an adaptive momentum technique with the backpropagation algorithm to enhance flood prediction accuracy and efficiency. Real-world flood data is used for validation, demonstrating the superior performance of the AM-BP algorithm compared to traditional methods. In addition, multilayer high-end computing architecture (MLCA) is used to handle weather data such as rainfall, river water level, soil moisture, etc. The AM-BP's real-time abilities enable proactive flood management, facilitating timely responses and effective disaster mitigation. Furthermore, the AM-BP algorithm can analyze large and complex datasets, integrating environmental and climatic factors for more accurate flood prediction. The evaluation result shows that the AM-BP algorithm outperforms traditional approaches with an accuracy rate of 96%, 96.4% F1-Measure, 97% Precision, and 95.9% Recall. The proposed AM-BP model presents a promising solution for flood prediction and management in IoT networks, contributing to more resilient and efficient flood control strategies, and ensuring the safety and well-being of communities at risk of flooding.

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“…Mobley, W [11] proposed using random forest classification to predict flood probability and using the national flood insurance program (NFIP) to predict flood disaster. At present, the utilization of flood resources mainly focuses on improving the limited water level of flood control during flood season by using the existing water conservancy projects combined with the rainfall runoff forecast and other non-engineering measures [12], and realizing the utilization of flood resources through the risk and benefit management of the reservoir [13].…”

Section: Introductionmentioning

confidence: 99%

Optimal Operation of Floodwater Resources Utilization of Lakes in South-to-North Water Transfer Eastern Route Project

et al. 2021

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In order to meet the demand of emergency water supply in the northern region without affecting normal water transfer, considering the use of the existing South-to-North Water Transfer eastern route project to explore the potential of floodwater resource utilization in the flood season of Hongze Lake and Luoma Lake in Jiangsu Province, this paper carried out relevant optimal operating research. First, the hydraulic linkages between the lakes were generalized, then the water resources allocation mode and the scale of existing projects were clarified. After that, the actual available amount of flood resources in the lakes was evaluated. The average annual available floodwater resources in 2003–2017 was 1.49 billion m3, and the maximum available capacity was 30.84 billion m3. Then, using the floodwater resource utilization method of multi period flood limited water levels, the research period was divided into the main flood season (15 July to 15 August) and the later flood season (16 August to 10 September, 11 September to 30 September) by the Systematic Clustering Analysis method. After the flood control calculation, the limited water level of Hongze Lake in the later flood season can be raised from 12.5 m to 13.0 m, and the capacity of reservoir storage can increase to 696 million m3. The limited water level of Luoma Lake can be raised from 22.5 m to 23.0 m (16 August to 10 September), 23.5 m (11 September to 30 September), and the capacity of reservoir storage can increase from 150 to 300 million m3. Finally, establishing the floodwater resource optimization model of the lake group with the goals of maximizing the floodwater transfer amount and minimizing the flood control risk rate, the optimal water allocation scheme is obtained through the optimization algorithm.

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Floodwater Utilization Based on Reservoir Pre-Release Strategy Considering the Worst-Case Scenario

Cited by 16 publications

References 31 publications

Optimization of Ecological Water Supplement Scheme for Improved Suitable Habitat Area for Rare Migratory Birds in Nature Reserves Using Interval-Parameter Fuzzy Two-Stage Stochastic Programming Model

Optimization of Ecological Water Supplement Scheme for Improved Suitable Habitat Area for Rare Migratory Birds in Nature Reserves Using Interval-Parameter Fuzzy Two-Stage Stochastic Programming Model

Adaptive Momentum-Backpropagation Algorithm for Flood Prediction and Management in the Internet of Things

Optimal Operation of Floodwater Resources Utilization of Lakes in South-to-North Water Transfer Eastern Route Project

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