Coordinating a Supply Chain with a Loss-Averse Retailer under Yield and Demand Uncertainties

This paper investigates the channel coordination of a supply chain (SC) consisting of a loss-averse retailer and a risk-neutral supplier under yield and demand uncertainties. Three existing contracts are analyzed. Our results demonstrate that the buyback (BB) and quantity flexibility (QF) contracts can not only coordinate the supply chain but also lead to Pareto improvement for each player, while the wholesale price (WP) contract fails to coordinate the chain due to the effects of double marginalization and risk preference. For comparison, a chain with a risk-neutral retailer is also analyzed. Furthermore, numerical examples are provided to demonstrate the effectiveness of the coordination contracts, and the impacts of loss aversion and random yield on the decision-making behaviors and system performance are then discussed

Daily Water Quality Forecast of the South-To-North Water Diversion Project of China Based on the Cuckoo Search-Back Propagation Neural Network

Water quality forecast is a critical part of water security management. Spatiotemporal and multifactorial variations make water quality very complex and changeable. In this article, a novel model, which was based on back propagation neural network that was optimized by the Cuckoo Search algorithm (hereafter CS-BP model), was applied to forecast daily water quality of the Middle Route of South-to-North Water Diversion Project of China. Nine water quality indicators, including conductivity, chlorophyll content, dissolved oxygen, dissolved organic matter, pH, permanganate index, turbidity, total nitrogen, and water temperature were the predictand. Seven external environmental factors, including air temperature, five particulate matter (PM2.5), rainfall, sunshine duration, water flow, wind velocity, and water vapor pressure were the default predictors. A data pre-processing method was applied to select pertinent predictors. The results show that the CS-BP model has the best forecast accuracy, with the Mean Absolute Percentage Errors (MAPE) of 0.004%–0.33%, and the lowest Root Mean Square Error (RMSE) of each water quality indicator in comparison with traditional Back Propagation (BP) model, General Regression Neural Network model and Particle Swarm Optimization-Back Propagation model under default data proportion, 150:38 (training data: testing data). When training data reduced from 150 to 140, and from 140 to 130, the CS-BP model still produced the best forecasts, with the MAPEs of 0.014%–0.057% and 0.004%–1.154%, respectively. The results show that the CS-BP model can be an effective tool in daily water quality forecast with limited observed data. The improvement of the Cuckoo Search algorithm such as calculation speed, the forecast errors reduction of the CS-BP model, and the large-scale impacts such as land management on different water quality indicators, will be the focus of future research

Regional Agroclimate Characteristic and Its Multiple Teleconnections: A Case Study in the Jianghan Plain (JHP) Region

Agricultural production depends on local agroclimatic conditions to a great extent, affected by ENSO and other ocean-atmospheric climate modes. This paper analyzed the spatio-temporal distributions of climate elements in the Jianghan Plain (JHP), Central China, and explored the impacts from teleconnection patterns, aimed at providing references for dealing with climate change and guiding agricultural activities. Both linear and multifactorial regression models were constructed based on the frequentist quantile regression and Bayesian quantile regression method, with the daily meteorological data sets of 17 national stations in the plain and teleconnection climate characteristic indices. The results showed that precipitation in JHP had stronger spatial variability than evapotranspiration. El Niño probably induced less precipitation in summer while the weakening Arctic Oscillation might lead to more summertime precipitation. The Nash-Sutcliffe efficiency (NSE) of the multifactorial and linear regression model at the median level were 0.42–0.56 and 0.12–0.18, respectively. The mean relative error (MRE) ranged −2.95–−0.26% and −7.83–0.94%, respectively, indicating the much better fitting accuracy of the multiple climatic factors model. Meanwhile it confirmed that the agricultural climate in JHP was under the influence from multiple teleconnection patterns

Prediction modelling framework comparative analysis of dissolved oxygen concentration variations using support vector regression coupled with multiple feature engineering and optimization methods: A case study in China

Dissolved oxygen (DO) is an essential indicator for assessing water quality and managing aquatic environments, but it is still a challenging topic to accurately understand and predict the spatiotemporal variation of DO concentrations under the complex effects of different environmental factors. In this study, a practical prediction framework was proposed for DO concentrations based on the support vector regression (SVR) model coupling multiple intelligence techniques (i.e., four data denoising techniques, three feature selection rules, and four hyperparameter optimization methods). The holistic framework was tested using a data matrix (17,532 observation data in total) of 12 indicators from three vital water quality monitoring stations of the longest inter-basin water diversion project in the world (i.e., the Middle-Route of the South-to-North Water Diversion Project of China), during the year 2017 to 2020 period. The results showed that the framework we advocated for could successfully and accurately predict DO concentration variations in different geographical locations. The model used the “wavelet analysis–LASSO regression–random search–SVR” combination of the Waihuanhe station has the best prediction performance, with the Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2) values of 0.251, 0.063, 0.190, and 0.911, respectively. The combined methods using feature selection and hyperparameter optimization techniques can significantly promote the robustness and accuracy of the prediction model and can provide a new universal and practical way of investigating and understanding the environmental drivers of DO concentration variations. For the water quality management department, this proposed comprehensive framework can also identify and reveal the key parameters that should be concerned and monitored under different environmental factors change. More studies in terms of assessing potential integrated water quality risk using multi-indicators in mega water diversion projects and/or similar water bodies are required in the future

Failure Analysis of a New Irrigation Water Allocation Mode Based on Copula Approaches in the Zhanghe Irrigation District, China

The risk analysis of an irrigation water allocation strategy based on physical mechanisms is critically important in practice. Conventional risk analysis only considers the role of the channel system and ignores the factors related to on-farm ponds. This paper proposes a channel-pond joint water supply mode (CPJM) based on copula approaches. Two copulas, the Plackett copula and No.16 copula, are chosen and two types of analyses are carried out with the proposed mode: (1) a risk assessment of CPJM with joint probability and conditional probability; and (2) determination of the water supply strategy given the pond water supply frequency. With a case study of the second channel in the Zhanghe Irrigation District (ZID), Southern China, nine combinations of channel water supply frequency (CWSF) and pond water supply frequency (PWSF) are studied. The results reveal that the failure probabilities of the joint distribution and the conditional distribution of the CPJM are 0.02%–16.54% and 0.45%–33.08%, respectively, with corresponding return period of 42–5000 and 10–222 years. Nevertheless, a previous study has shown that the real probability is 33.3%, which means that the return period is equals to three years. Therefore, the objective failure evaluation of the irrigation water-use strategy is useful for water saving in this channel system. Moreover, the irrigation water allocation strategy can be determined and the failure charts relating the CWSF and PWSF can be obtained for a predetermined PWSF. Thus, the channel-pond joint water supply mode provides a more reasonable estimate of the irrigation water allocation strategy reliability

Surface Water–Groundwater Transformation Patterns in the Jianghan Plain after the Impoundment of the Three Gorges Project and the Opening of the Yangtze-to-Hanjiang Water Transfer Project

Understanding the law of surface water–groundwater conversion in the face of high-intensity human activities is still a challenge. In this study, we employed statistical and system dynamics methods to investigate the surface water–groundwater conversion law in the Jianghan Plain following the impoundment of the Three Gorges Project (TGP) and the Yangtze-to-Hanjiang Water Transfer Project (YHWTP). The groundwater level’s long data set was used for the first time to study the water level change and water exchange in the research region after the impoundment of the TGP and the delivery of water from the YHWTP. The findings suggest a significant decrease in the interannual trend of the surface water level and groundwater level in the research region. It was observed that a 1m rise in the surface water level can lead to a 0.11–0.38 m rise in the groundwater level. The water level fluctuation coefficients of the surface water level and groundwater level are influenced by the impoundment of the TGP and the water delivery from the YHWTP, causing them to increase and decrease, respectively. In general, the surface water recharges the groundwater in the studied region. The water exchanges between the surface water and groundwater in the Yangtze River’s main stream, the middle region of the Hanjiang Plain, and the lower reaches of the Hanjiang River are, on average, 10−2 m3/(d·m), 10−5 m3/(d·m), and 10−3 m3/(d·m) orders of magnitude, respectively. The water exchange in the Yangtze River’s main stream was reduced after TGP impoundment, and it was enhanced following YHWTP water delivery

A new framework for water quality forecasting coupling causal inference, time-frequency analysis and uncertainty quantification

Accurate forecasting of water quality variables in river systems is crucial for relevant administrators to identify potential water quality degradation issues and take countermeasures promptly. However, pure data-driven forecasting models are often insufficient to deal with the highly varying periodicity of water quality in today’s more complex environment. This study presents a new holistic framework for time-series forecasting of water quality parameters by combining advanced deep learning algorithms (i.e., Long Short-Term Memory (LSTM) and Informer) with causal inference, time-frequency analysis, and uncertainty quantification. The framework was demonstrated for total nitrogen (TN) forecasting in the largest artificial lakes in Asia (i.e., the Danjiangkou Reservoir, China) with six-year monitoring data from January 2017 to June 2022. The results showed that the pre-processing techniques based on causal inference and wavelet decomposition can significantly improve the performance of deep learning algorithms. Compared to the individual LSTM and Informer models, wavelet-coupled approaches diminished well the apparent forecasting errors of TN concentrations, with 24.39%, 32.68%, and 41.26% reduction at most in the average, standard deviation, and maximum values of the errors, respectively. In addition, a post-processing algorithm based on the Copula function and Bayesian theory was designed to quantify the uncertainty of predictions. With the help of this algorithm, each deterministic prediction of our model can correspond to a range of possible outputs. The 95% forecast confidence interval covered almost all the observations, which proves a measure of the reliability and robustness of the predictions. This study provides rich scientific references for applying advanced data-driven methods in time-series forecasting tasks and a practical methodological framework for water resources management and similar projects

Table_1_v1_Impact of inter-basin water diversion project operation on water quality variations of Hanjiang River, China.DOCX

The impact of the operation of inter-basin water diversion projects on the integrity and stability of regional ecosystems cannot be ignored. In this study, water quality samplings were conducted monthly at 16 national monitoring sites in the mid-downstream of the Hanjiang River (HJR, the downstream of the water source of the South-to-North Water Diversion Project of China) over 3 years, covering seven physiochemical water quality indicators and six heavy metal elements. The water quality index (WQI) and multivariate statistical techniques were introduced to comprehensively evaluate water quality status and understand the corresponding driving factors of water quality variations. The heavy metal risks were evaluated using the Nemerow Pollution Index (Pn), the Heavy Metal Pollution Index (HPI), and the human health risk assessment model. The results showed that after the operation of the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC), water quality in the mid-downstream of the HJR was generally at a “good” status, with the average WQI of 86.37, showing no water quality deterioration trends. The operation of the MRSNWDPC did significantly decrease the monthly flow in the HJR by about 4.05–74.27%, and the flow variation processes also became more stable than before. Most water quality indicators and WQIs have no correlations with the flow and water level changes. The human health risks of all heavy metal elements caused by dermal exposure and ingestion pathways increased over time. The average individual health risk caused by carcinogenic heavy metal Cr was the highest. Chromium is the major carcinogenic factor and should be a critical indicator to pay special attention to for water risk management in the HJR. This study provides a scientific reference for the water quality safety management of HJR under the influence of a water diversion project.</p