In large-scale emergencies,appropriate and efficient dispatch of limited resources is essentialConsidering human's bounded rationality,the paper applies the value function in Prospect Theory to measure people's satisfaction perception with regard to emergency responsivenessWe present a multi-objective nonlinear integer model for planning of emergency resource allocation in the initial period of unconventional emergencies,which quantifies the satisfaction of timeliness and demand as its main objectives,and take into consideration the economic utilityA scatter search method is designed to solve the model with a numerical experiment performed to testify its validity

Multiple-area Post-disaster Resource Distribution Model Considering Perception Satisfaction

The present work aims to find the optimal solution of Nash Equilibrium (NE) in the traditional Game Theory (GT) applied to water resources allocation. Innovatively, this paper introduces Particle Swarm Optimization (PSO) into GT to propose a cooperative game model to solve the NE problem. Firstly, the basic theory of the PSO algorithm and cooperative game model is described. Secondly, the PSO-based cooperative game model is explained. Finally, the PSO-based cooperative game model is compared with the Genetic Algorithm (GA) to test the performance. Besides taking the countries in Lancang Mekong River Basin as the research object, this paper discusses each country’s water consumption and economic benefits under different cooperation patterns. Then, a series of improvement measures and suggestions are put forward accordingly. The results show that the average server occupancy time of the PSO-based cooperative game model is 78.46% lower than that of GA, and the average waiting time is 79.24% lower than that of the GA. Thus, the model reported here has higher computational efficiency and excellent performance than the GA and is more suitable for the current study. In addition, the multi-country cooperation mode can obtain more economic benefits than the independent water resource development mode. This model can quickly find the optimal combination of 16 cooperation modes and has guiding significance for maximizing the benefits of cross-border water Resource Utilization. This research can provide necessary technical support to solve the possible contradictions and conflicts between cross-border river basin countries and build harmonious international relations.

Economic benefit evaluation of water resources allocation in transboundary basins based on particle swarm optimization algorithm and cooperative game model—A case study of Lancang-Mekong River Basin

Renewable energy has been rapidly developed and transmitted across provinces to accelerate carbon neutrality in China. Water has been saved in power-receiving regions due to interprovincial renewable energy substitution for water-intensive thermal power (RST). The re-utilization of the water saved by RST through various ways is an urgent issue to be addressed. To establish the correlation between the saved water and related source reservoir and further utilize the saved water to maximize its incremental benefits, a general model framework is proposed to quantify the saved water and reallocate it to cope with emergency events and promote hydropower-based reservoir operation. The model is applied to an energy-water system centered around Henan Province. The incremental benefits and optimal reallocation mode of the saved water have been revealed. The results indicate that RST produces an annual water savings of 175.160 million m3 in Henan Province, which contributes to the increase in hydropower generation and emergency water supply ranging from 14.584 GWh to 32.267 GWh and 0 to 1.724 × 108 m3, respectively, under different hydrological scenarios. To boost the incremental benefits of water reallocation under RST, the source reservoir should concentrate on the emergency water supply in dry years and enhance hydropower generation in wet years. Conserving the saved water for future use would produce additional 12.771 GWh to 22.324 GWh of hydropower generation in the following year. As RST capacity increases from 40 to 320 TWh, the marginal revenues of hydropower generation and emergency water supply decrease by 75.4% and 40.7%, respectively, in wet years. This indicates that water supply from different sources should be rearranged to maximize total benefits according to the volume of water saved by RST and the marginal revenue characteristics. The general framework can be extended to other energy-water systems to assess incremental benefits and propose adaptive reservoir operation rules, which provides new insights into water management from the perspective of renewable energy substitution. 

Utilization of Water Conserved by Interprovincial Renewable Energy Substitution for Thermal Power: Incremental Benefits and Advanced Reservoir Operation Rules

In recent years, lakes pollution has become increasingly serious, so water quality monitoring is becoming increasingly important. The concentration of total organic carbon (TOC) in lakes is an important indicator for monitoring the emission of organic pollutants. Therefore, it is of great significance to determine the TOC concentration in lakes. In this paper, the water quality dataset of the middle and lower reaches of the Yangtze River is obtained, and then the temperature, transparency, pH value, dissolved oxygen, conductivity, chlorophyll and ammonia nitrogen content are taken as the impact factors, and the stacking of different epochs’ deep neural networks (SDE-DNN) model is constructed to predict the TOC concentration in water. Five deep neural networks and linear regression are integrated into a strong prediction model by the stacking ensemble method. The experimental results show the prediction performance, the Nash-Sutcliffe efficiency coefficient (NSE) is 0.5312, the mean absolute error (MAE) is 0.2108 mg/L, the symmetric mean absolute percentage error (SMAPE) is 43.92%, and the root mean squared error (RMSE) is 0.3064 mg/L. The model has good prediction performance for the TOC concentration in water. Compared with the common machine learning models, traditional ensemble learning models and existing TOC prediction methods, the prediction error of this model is lower, and it is more suitable for predicting the TOC concentration. The model can use a wireless sensor network to obtain water quality data, thus predicting the TOC concentration of lakes in real time, reducing the cost of manual testing, and improving the detection efficiency.

An Ensemble Deep Neural Network Approach for Predicting TOC Concentration in Lakes along the Middle-Lower Reaches of Yangtze River

ABSTRACT In the face of diverse and conflicting factors affecting emergency water supply (EWS) decisions, ensuring sufficient drinking water provision for communities emerges as a critical and intricate challenge. Compounding this complexity is the presence of vague and imprecise data, complicating the evaluation and selection process. To tackle these challenges, this study introduces an extended fuzzy multi-criteria group decision-making (FMCGDM) model, leveraging compromise solutions and relative preference relations, specifically tailored for EWS situations. The proposed model employs linguistic variables with multiple experts’ judgements to rate the alternatives versus conflicting criteria and assign weight to these criteria. By utilizing triangular fuzzy numbers, the model effectively handles information imprecision, enabling nuanced evaluations and distinguishing among potential alternatives. Employing a relative preference relation, the model computes distance values between alternatives and ideal or anti-ideal solutions, aiding in decision-making. Finally, the practical application and computational effectiveness of the model are demonstrated through a real-life case study on EWS in Iran. The results show that the mobile water treatment units and packaged water alternatives received the highest score, placing first and second in the order ranking, respectively, while the existing distribution systems were deemed most inappropriate for the EWS situation in the case study. 

Emergency water supply during disasters in Iran: a multi-criteria group decision analysis

The emergency supply of transboundary water resources is a prominent problem affecting the social and economic development of basin countries. However, current water supply decisions on transboundary water resources may ignore the psychological perception of multi-stakeholders, and the evolution of emergencies increases the uncertainty of decision making. Both factors would lead to the low acceptance of water-related decisions. Utility satisfaction, perceived losses, and quantity satisfaction were selected in this paper to identify the perceived satisfaction of upstream governments, downstream governments, and the public, respectively, over multiple decision-making stages. A modeling framework combining prospect theory and the multi-stage multi-objective programming methodology was then developed to measure the perceived satisfaction of different stakeholders in a watershed under emergency. A two-stage NSGA-II and TOPSIS based approach was adopted to find the optimal compromise solution to solve the model. The framework was applied in the Lancang–Mekong River basin to provide suggestions to decision makers. Upstream decision makers must choose a moderate proportional fairness degree when making emergency decisions to maximize the perceived satisfaction of all stakeholders. Meanwhile, the perceived loss of downstream countries with low water demand should be considered first in the formulation of emergency water supply plans. Furthermore, although water supply from upstream countries can improve perceived water quantity satisfaction of downstream publics, additional actions must still be taken to change the traditional concepts of the public.

Emergency water supply decision-making of transboundary river basin considering government–public perceived satisfaction

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Emergency water supply decision-making of transboundary river basin considering government–public perceived satisfaction