Water Resources Allocation Problems Research Articles

A distributed simulation-optimization framework for many-objective water resources allocation in canal-well combined irrigation district under diverse supply and demand scenarios

To address the issues of both water resources allocation and sustainable management in agriculture areas with rising food demand, a simulation-optimization framework based on Flopy and Pymoo was proposed and developed for canal-well combined irrigation districts. The proposed framework first solved the many-objective water resources allocation problem which integrates groundwater simulation, crop production, and farmer income modules to quantitatively reveal the various trade-offs and synergies by using NSGA-III algorithm. The Entropy-TOPSIS method was then applied to recommend proper water allocation schemes. The proposed framework was further tested in Baojixia irrigation district considering various water supply and crop demand scenarios based on Copula-based uncertainty analysis. The Key findings are as follows: (1) the proposed framework could effectively optimize conjunctive water resources allocation problems of both surface water and groundwater; (2) low supply combined with high demand (p=0.17) is more likely to occur than high supply with high demand (p=0.02); (3) increased crop demand and restricted surface water negatively impact both water productivity and groundwater sustainability; and (4) the cumulative groundwater drawdown of recommend schemes is 36.9 % and 6.5 % higher under low to medium supply scenarios, while water productivity of recommend schemes decreases 28.2 % and 9.7 % with high and medium demand. This framework could provide useful insights for sustainable agricultural water management in canal-well combined irrigation district with various uncertainties in supply and demand scenarios.

Accurate preference-based method to obtain the deterministically optimal and satisfactory fairness-efficiency trade-off

The resource allocation problem is a classic multi-objective challenge, particularly when balancing the fairness-efficiency trade-off. To achieve a deterministically optimal and satisfactory solution, researchers frequently employ preference-based methods, including selecting among Pareto solutions based on the decision-maker's a posteriori preference and using deterministic models incorporating a priori preferences. In this study, we address two main challenges—specifically, (1) the limitations in measuring the abstract concepts of fairness and efficiency and (2) finding a deterministically optimal and satisfactory balance between fairness and efficiency. We apply a Gini impurity index derived from the classification and regression tree to calculate fairness, ensuring the Gini index function's differentiability. Additionally, we unify the scales of fairness and efficiency to facilitate calculation. Using accurate preference information, we employ the extended interval goal programming method to solve the model and achieve a deterministically optimal and satisfactory solution. The comparative analysis results demonstrate that our model (1) efficiently addresses the real-world water resource allocation problem concerning the fairness-efficiency trade-off; and (2) generates fewer penalties, with an average improvement ratio of 8% in the case study, using more refined penalty functions that align closer to the decision-maker's real and nonlinear preferences.

Development of the Multi-Objective Invasive Weed Optimization Algorithm in the Integrated Water Resources Allocation Problem

Development of the Multi-Objective Invasive Weed Optimization Algorithm in the Integrated Water Resources Allocation Problem

Water resource management based on wireless virtualization in wastewater reclamation and utilization in dryland botanical garden

Abstract The research on the management and distribution of renewable water resources mostly adopts the method of combining the intelligent optimization algorithm and the target planning model. In this paper, four water distribution users are used as categories, and the problem of renewable water resources allocation is transformed into a classification problem, which is included in the category of wireless virtualization processing problems, and wireless virtualization is selected to support the allocation of renewable water resources. Experiments show that the water shortage of the water distribution scheme obtained by the method in this paper is lower than that of the optimal water distribution scheme of the traditional intelligent optimization algorithm. It can also be seen from the water distribution cost and water shortage of a single user that the water distribution effect of the method in this paper is better than that of the traditional intelligent optimization algorithm (P<0.05). It shows that the research on the management and distribution method of renewable water resources based on wireless virtualization not only provides new ideas for dealing with configuration problems but also has important significance for alleviating the shortage of water resources in our country and the management and utilization of renewable water resources.

A MOEA/D with global and local cooperative optimization for complicated bi-objective optimization problems

Multi-objective evolutionary algorithm based on decomposition has been well-recognized in addressing bi-objective optimization problems. However, it is a serious challenge for MOEA/D to optimize real-world complicated bi-objective optimization problems which have irregular optimal objective spaces, such as long-tailed, peaked, and disconnected types. Hence, an improved MOEA/D with global and local cooperative mechanisms is proposed in this paper for complicated bi-objective optimization problems. The overall optimization is carried out by the coordination of global and local search phases. Specifically, a bidirectional global search strategy based on reference points is first used to broaden the search regions. Aiming at the irregularity of the Pareto fronts, an adaptive neighborhood search strategy is proposed according to the individual sparsity, which can further explore near the obtained global optimal solutions. The cooperation of these two strategies is designed to improve the distribution and uniformity of the population on complicated bi-objective optimization problems. The performance of the proposed algorithm is investigated on a representative set of benchmark test functions and a real-world water resource allocation problem. The simulation results show that the proposed algorithm is very promising for complicated bi-objective optimization problems.

Multi-objective optimal water resources allocation in the middle and upper reaches of the Huaihe River Basin (China) based on equilibrium theory

In the river basin water resources allocation (WRA) problem, an unbalanced WRA poses challenges to water resources management departments. Many studies focus on achieving a lower water shortage rate while ignoring the equilibrium relationship among the socio-economic system, water resources system and eco-environmental system, as well as the equilibrium relationship among different regions. In this study, a water resources allocation model(WRAM) based on equilibrium theory is constructed to achieve the balance between different systems and different spaces in a basin. First, the relationship among the water resources system, socio-economic system and eco-environmental system is described. Then, the regional equilibrium index and system equilibrium index are constructed. Finally, the first model based on equilibrium theory is constructed. The results show that: (1) the Pareto Front reflects the contradictory relationship between economic development and environmental sustainability; (2) with the restructuring of industry and cropping, both economic efficiency and water shortage rates improve; (3) the equilibrium of the basin could also be further improved if water resources utilisation is further improved. Therefore, this study improves the existing WRAM, which can be applied to guide the water resources management of river basin.

An integrated approach for reducing spatially coupled water-shortage risks of Beijing-Tianjin-Hebei urban agglomeration in China

Growing agricultural and economic activities have brought tremendous pressure to regional water resources management. Considering the interactions of water resources allocation among adjacent regions with connected water systems, it is necessary to pay attention to the unification of socio-economic development and environmental protection between the regions. This is of significance to many agglomeration areas that are composed of multiple large cities such as the Beijing-Tianjin-Hebei urban agglomeration (BTH) in China. In this research, an ensemble approach was proposed to tackle water resources allocation problems under the constraints of water resources to reflect interdependence of the composed three areas, i.e., Beijing, Tianjin and Hebei. The approach included ARIMA (Autoregressive Integrated Moving Average) method, ANN (Artificial Neural Network) modelling, copula functions, and multi-objective chance-constrained programming model. It can 1) reflect the variations characteristic of regional water supply and demands, 2) solve the interactions between multi-regional water resources, and 3) obtain desired water resources allocation strategies under multi- scenario and scale conditions. Then, the approach was applied in typical regions of China, i.e., Beijing-Tianjin-Hebei urban agglomeration. Considering 27 scenarios of the nexus water shortage risk, multi-objective joint opportunity constraint programming model aimed at maximizing economic benefits and ecological benefits, in combination with economic, agricultural, water resources and environmental constraints. As the optimal strategies indicated, the maximum GDP in BTH would remain at 8514 billion yuan. The minimum amount of water allocation and wastewater discharge would be 24–26 billion m3 and 2100–2101 million m3 respectively.

Multiobjective land–water allocation model for sustainable agriculture with predictive stochastic yield response

AbstractConsider an agricultural land–water resource allocation problem in which yields are spatial dependent and stochastically correlated. To achieve sustainability, we formulate a multiobjective (MO) optimization problem, in which the decision maker determines the cultivation areas and the supplemental irrigation water levels at different locations, with social, economic, and environmental goals in mind. For the social goal, we minimize the root mean squared difference of incomes among locations. For the economic goal, we minimize the production risk. We show that minimizing production risk is equivalent to maximizing the service level, when demand is normally distributed. For the environmental goal, we minimize the resource utilization. Assume that the yield vector at different locations follows a multivariate normal distribution. We formulate the MO optimization problem using a weight global criterion method, and we provide a sufficient condition for convex quadratic programming. We demonstrate the applicability of our proposed framework in the case study of sugarcane production in Thailand. To capture yield response to water, we propose several models including linear and nonlinear regressions, and we obtain the closed‐form expression for the linear and probit yield response models. The numerical experiment reveals that our solution significantly improves the social and economic goals, compared to the current policy. Finally, we illustrate how to apply our model to quantify the monetary value from reducing yield variability, which could be resulted from smart irrigation or precision agriculture.

A priority-based multi-objective framework for water resources diversion and allocation in the middle route of the South-to-North Water Diversion Project

Because of the degradation of the social-ecological-economic-environmental (SEEE) system, water scarcity has been a growing source of conflicts over the globe. Further, the uncertainty arising from complex water resource scenarios increases the conflicts between the different water users and destabilizes water allocation systems. In this study, a priority-based multi-objective programming (MOP) model (quantitative path) with fuzzy random variables (FRVs) is established for a water resource diversion and allocation (WRDA) problem. To determine the priorities of the multiple objectives, a priority-determination approach (qualitative path) is designed, comprising of a pressure-state-response (PSR) multiple attribute assessment system and a technique for order preference by similarity to an ideal solution (TOPSIS)-based evaluation method. Then the MOP model is transformed into a solvable goal programming (GP)-based model. Because of the inclusion of FRVs, the obtained results can be adjusted to local conditions in view of social, economic, environmental and ecological objective priorities. Therefore, they are more applicable than traditional weight sum or Pareto multi-objective WRDA methodologies. A case study from the middle route of the South-to-North Water Diversion Project (SNWDP-MRP) in China is given to demonstrate the practicability and rationality of the proposed methodology in obtaining scientific WRDA plans.

Transboundary Water Conflicts as Postcolonial Legacy (the Case of Nile Basin)

It is not shortage or lack of water that leads to conflict but the way how water is governed and managed. It is said that water will be, much more than oil, the major geopolitical issue of the 21st century. Although it is difficult to demonstrate this, it is clear that the increasing scarcity of the resource, on the one hand, and the configuration of its availability, on the other, are conflict-generating. In the particular case of the African continent, the large catchment basins of the Nile, Niger and Chad, shared by many states of unequal power, are the scene of inefficient hydro-diplomacy. Indeed, north to south, the Nile Delta is 161 km long and covers the coastline of Egypt from Alexandria in the west to Port Said in the east. Egypt with 100 mln population is de facto the principal hydro-hegemon state in the Nile basin. Nevertheless, a couple of riparian states, as Ethiopia (105 mln population), have taken measures in order to challenge this status quo: the signature and launching of the Nile Basin Initiative (NBI), the signature of Cooperative Framework Agreement (CFA), the construction of the Grand Ethiopian Renaissance Dam (GERD) and the signing of the Declaration of Principles Agreement. The article attempts to analyse the urgency of the problem of water resources allocation in Africa with particular focus to the Nile basin and the complexity of agreements regulating the issue dating back to the colonial era. The study also emphasizes the difficulties bilateral and multilateral aids faced while trying to solve a conflict. As Nile for many states is not just a source of water, it is the host of a fragile ecosystem, essential for maintaining the environmental and ecological balance of North-East Africa.

Many-objective optimization with improved shuffled frog leaping algorithm for inter-basin water transfers

Inter-basin water transfers (IBWT) are implemented to re-allocate unevenly distributed water resources. However, many conflicting objectives associated with society, economy, and environment have made the water resources allocation problem in IBWT more complicated than ever before. Thus, there is a continuous need for in-depth research with the latest optimization techniques to secure many-objective allocation of water resources for IBWT. In addition, being troubled of easily falling into local minima and premature convergence in some multi-objective optimization algorithms, it is necessary to explore new alternatives to improve their search quality. Here we propose a many-objective optimization methodology for IBWT, which includes three modules: (1) formulating a many-objective optimization problem based on realistic controls; (2) developing a new multi-objective real-coded quantum inspired shuffled frog leaping algorithm (r-MQSFLA) to solve the optimization problem; (3) utilizing the Analytic Hierarchy Process (AHP)-Entropy method to filter the Pareto solutions. In r-MQSFLA, the real-coded quantum computer and the external archive with dynamic updating mechanism are applied to SFLA. The performance of r-MQSFLA is first compared to that of other multi-objective evolutionary algorithms (MOEAs) in solving mathematical benchmark problems. A case study of the Eastern Route of South-to-North Water Transfer Project in Jiangsu Province, China varying from a normal to an extremely dry year, demonstrates that r-MQSFLA displays approximate performance on some compared algorithms and is improved significantly than MOSFLA in terms of convergence, diversity and reasonable solutions. This study can update the understanding of quantum theory to MOEAs and will provide a reference for better water resources allocation in IBWT under uncertainty.

Multi-Objective Optimal Allocation of Urban Water Resources While Considering Conflict Resolution Based on the PSO Algorithm: A Case Study of Kunming, China

With the rapid increase of water demand in urban life, ecology and production sectors, the problem of water resources allocation has become increasingly prominent. It has hindered the sustainable development of urban areas. Based on the supply of various water sources and the water demand of different water users, a multi-objective optimal allocation model for urban water resources was proposed. The model was solved using the algorithm of particle swarm optimization (PSO). The algorithm has a fast convergence and is both simple and efficient. In this paper, the conflict over Kunming’s water resources allocation was taken as an example. The PSO algorithm was used to obtain optimized water resources allocation plans in the year 2020 and 2030, under the circumstances of a dry year (inflow guarantee rate p = 0.825) and an unusually dry year (inflow guarantee rate p = 0.885), respectively. The results showed that those allocation plans can lower the future potential water shortage rates of Kunming. At the same time, the interests of different sectors can all be satisfied. Therefore, conflicts over urban water use can be effectively alleviated.

Decision preference-based artificial bee colony algorithm for many-objective optimal allocation of water resources

Freshwater becomes a scarce resource because of the rapid growth of population and serious water pollution. Optimal allocation of water resources is an effective method to tackle this issue. In this paper, we consider a many objective water resources allocation problem with four objectives and several constraints. Unlike other many-objective optimisation problems, water resources allocation prefers the current situation (economy, resident life, or ecological environment). A decision preference-based algorithm may be suitable for the problem. So, a decision preference-based ABC is proposed for many-objective optimal allocation of water resources. Firstly, each objective is assigned a weight factor based on decision preferences. By weighted sum of four objectives, the many-objective water resources allocation problem is converted into a single objective optimisation problem. Then, an improved artificial bee colony (ABC) algorithm is used to solve the problem. Finally, different decision preferences are tested. Simulation experiments show our approach achieves promising results.

A bio-inspired algorithm for a classical water resources allocation problem based on Adleman–Lipton model

A bio-inspired algorithm for a classical water resources allocation problem based on Adleman–Lipton model

A new approach for solving fuzzy multi-objective quadratic programming of water resource allocation problem

This paper describes an application of fuzzy multi-objective quadratic model with flexible constraints for optimal allocation of limited available water resources among different water-user sectors. Due to the fact that, water resource allocation problem is one of the practical and essential subjects in real world and many of the parameters may be faced by uncertainty. In this paper, we present α - cut approach for transforming fuzzy multi-objective quadratic programming model with flexible constraints into a crisp form. By using this approach a multi-parametric multi-objective programming model corresponding to α and parameters of flexible constraints is obtained. One of the advantages of this model is that the α - cut level is not determined by the decision makers. Actually, this model itself can calculate the α - cut level. In order to achieve a desired Pareto optimal value of multi-parametric multi-objective model, we use goal programming method for illustration of water resource allocation with sensitivity analysis of lower bound of parameters in flexible constraints. To illustrate the efficiency of the proposed approach, we apply it for a real case problem of water resource allocation.

Optimal Allocation Model of Water Resources Based on the Prospect Theory

The rational allocation of water resources in the basin/region can be better assisted and performed using a suitable water resources allocation model. Rule-based and optimization-based simulation methods are utilized to solve medium- and long-term water resources allocation problems. Since rule-based allocation methods requires more experience from expert practice than optimization-based allocation methods, it may not be utilized by users that lack experience. Although the optimal solution can be obtained via the optimization-based allocation method, the highly skilled expert experience is not taken into account. To overcome this deficiency and employ the advantages of both rule-based and optimization-based simulation methods, this paper proposes the optimal allocation model of water resources where the highly skilled expert experience has been considered therein. The “prospect theory” is employed to analyze highly skilled expert behavior when decision-making events occur. The cumulative prospect theory value is employed to express the highly skilled expert experience. Then, the various elements of the cumulative prospect theory value can be taken as the variables or parameters in the allocation model. Moreover, the optimal water allocation model developed by the general algebraic modeling system (GAMS) has been improved by adding the decision reversal control point and defining the inverse objective function and other constraints. The case study was carried out in the Wuyur River Basin, northeast of China, and shows that the expert experience considered as the decision maker’s preference can be expressed in the improved optimal allocation model. Accordingly, the improved allocation model will contribute to improving the rationality of decision-making results and helping decision-makers better address the problem of water shortage.

Multiobjective Irrigation Model: Alqueva River Basin Application

AbstractThis paper aims to solve an optimal irrigation water resources allocation problem applied to Alqueva River Basin irrigation system in Portugal. A multiobjective model was developed for this...

An Interval Quadratic Fuzzy Dependent-Chance Programming Model for Optimal Irrigation Water Allocation under Uncertainty

In this study, an interval quadratic fuzzy dependent-chance programming (IQFDCP) model is proposed for irrigation water management under uncertainty. This model can deal with the interval and fuzzy uncertainties encountered in optimal irrigation water resources allocation problems. It takes optimal credibility level of system revenue as the objective function for addressing the conflict between maximum system revenue and the credibility level of the system revenue. By providing three scenarios of precipitation, the optimal solutions are obtained. The developed model is applied to a case study for irrigation water allocation in Minqin Oasis, Wuwei city, northwest China. This study can help determine how much irrigation water should be allocated to different crops under the maximum credibility level of the system revenue. The optimal solutions can provide a certain credibility level of system revenue corresponding to different scenarios and mitigate the system-failure risk level of water shortages in arid areas. Moreover, because the IQFDCP can objectively reflect the interrelationship among crop irrigation water amount, system revenue, and system-failure risk level, the results are able to provide advices to decision makers for efficiently managing water resources in different scenarios.

Water Resources Allocation in Transboundary River Based on Asymmetric Nash–Harsanyi Leader–Follower Game Model

Transboundary river water resources allocation is important in water resources management. Conflicts often arise when different water users compete for a limited water supply. This study proposes a two-level asymmetric Nash–Harsanyi Leader–Follower game model to resolve conflicts of interest in transboundary river water resources allocation problems. In the proposed model, we use bankruptcy theory to derive disagreement points and determine the bargaining weights considering the principles of equity and efficiency. For comparison, a model that does not consider disagreement points and bargaining weights are also used to demonstrate the importance of disagreement points and bargaining weights. The proposed model is applied to a real case of the Huaihe River basin in China, which is facing water shortages. In the case study, the watershed management agency serves as the leader, three provinces (Henan, Anhui and Jiangsu) serve as followers, and successive linear programming is used to solve the model for followers. The results show that the proposed disagreement points can guarantee basic water demand, and the bargaining weights can better balance the economic development levels among followers.

Performance Assessment of a Coupled Particle Swarm Optimization and Network Flow Programming Model for Optimum Water Allocation

Water resources allocation problems are mainly categorized in two classes of simulation and optimization. In most cases, optimization problems due to the number of variables, constraints and nonlinear feasible search space are known as a challenging subject in the literature. In this research, by coupling particle swarm optimization (PSO) algorithm and a network flow programming (NFP) based river basin simulation model, a PSO-NFP hybrid structure is constructed for optimum water allocation planning. In the PSO-NFP model, the NFP core roles as the fast inner simulation engine for finding optimum values for a large number of water discharges in the network links (rivers and canals) and nodes (reservoirs and demands) while the heuristic PSO algorithm forms the outer optimization cover to search for the optimum values of reservoirs capacities and their storage priorities. In order to assess the performance of the PSO-NFP model, three hypothetical test problems are defined, and their equivalent nonlinear mathematical programs are developed in LINGO and the results are compared. Finally, the PSO-NFP model is applied in solving a real river basin water allocation problem. Results indicate that the applied method of coupling PSO and NFP has an efficient ability for handling river basin-scale water resources optimization problems.