scholarly journals Irrigation Water Allocation at Farm Level Based on Temporal Cultivation-Related Data Using Meta-Heuristic Optimisation Algorithms

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2611
Author(s):  
Bahram Saeidian ◽  
Mohammad Saadi Mesgari ◽  
Biswajeet Pradhan ◽  
Abdullah M. Alamri
Keyword(s):  
Water Management ◽  
Objective Function ◽  
Water Allocation ◽  
Optimal Allocation ◽  
Heuristic Algorithms ◽  
Imperialist Competitive Algorithm ◽  
Complex Problem ◽  
Bees Algorithm ◽  
Farm Level ◽  
Related Data

The present water crisis necessitates a frugal water management strategy. Deficit irrigation can be regarded as an efficient strategy for agricultural water management. Optimal allocation of water to agricultural farms is a computationally complex problem because of many factors, including limitations and constraints related to irrigation, numerous allocation states, and non-linearity and complexity of the objective function. Meta-heuristic algorithms are typically used to solve complex problems. The main objective of this study is to represent water allocation at farm level using temporal cultivation data as an optimisation problem, solve this problem using various meta-heuristic algorithms, and compare the results. The objective of the optimisation is to maximise the total income of all considered lands. The criteria of objective function value, convergence trend, robustness, runtime, and complexity of use and modelling are used to compare the algorithms. Finally, the algorithms are ranked using the technique for order of preference by similarity to ideal solution (TOPSIS). The income resulting from the allocation of water by the imperialist competitive algorithm (ICA) was 1.006, 1.084, and 1.098 times that of particle swarm optimisation (PSO), bees algorithm (BA), and genetic algorithm (GA), respectively. The ICA and PSO were superior to the other algorithms in most evaluations. According to the results of TOPSIS, the algorithms, by order of priority, are ICA PSO, BA, and GA. In addition, the experience showed that using meta-heuristic algorithms, such as ICA, results in higher income (4.747 times) and improved management of water deficit than the commonly used area-based water allocation method.

Optimal Water Allocation Using Data Mining Techniques- A Survey

2018 ◽  
Vol 6 (8) ◽  
pp. 225
Author(s):  
Sara Kutty T K ◽  
Hanumanthappa M
Keyword(s):  
Water Management ◽  
Data Mining ◽  
Water Resources ◽  
Water Allocation ◽  
Optimal Allocation ◽  
Weather Forecasting ◽  
Theoretical Background ◽  
Survey Paper ◽  
Using Data ◽  
Optimal Water Allocation

Water is one of the most precious resources on earth. All living beings depend on water and it is used for agriculture, environment, household, power generation, industries, navigation, recreation etc. The volume of water resources data in the world is increasing day by day and various studies are carried-out on these data for decision making process. To handle this enormous volume of water data, many methods are available, but the most adequate and suitable method for optimal allocation of water data is data mining. It can be used to predict the results for future action related to weather forecasting, climate change, water management, flood controlling, optimal water allocation etc. This survey paper elaborates the theoretical background of data-mining models and highlights the applications in knowledge data discovery from a water resources database, in particularly on optimal water allocation. Application of data-mining to water management is at a developmental stage and very few research works have been carried out on this domain.

Optimal Allocation of Water Resources Based on Multi-Objective Particle Swarm Algorithm and Information Entropy

2014 ◽  
Vol 641-642 ◽  
pp. 75-79 ◽  
Author(s):  
Wei Lin Liu ◽  
Li Na Liu
Keyword(s):  
Information Entropy ◽  
Water Allocation ◽  
Optimal Allocation ◽  
Supply And Demand ◽  
Particle Swarm ◽  
Complex Problem ◽  
Particle Swarm Algorithm ◽  
Multi Objective ◽  
Decision Making Problem ◽  
North Water

Water allocation is a very complex problem, involving social, economic, environmental, and political factors. Consequently, it is a multi-objective decision-making problem. This paper presents a multi-objective model for the optimal allocation on multisource water for multiuser under sufficiently considering the harmonious development among economy, society and environment. A multi-objective particle swarm optimization (MOPSO) algorithm is employed to generate a set of Pareto-optimal solutions. At the same time, to facilitate easy implementation for the water allocation operator, information entropy theory is adopted to sort the decision results according to the magnitude of the superiority degrees. As a case study the proposed approach has been applied to the reasonable allocation of water supply and demand in the water-receiving areas of the South-to-North Water Transfer Project in China, in which the maximal benefit of economy, society and environment was regarded as the multi-objectives. The results show that the proposed approach is able to offer the quantifiable benefits or costs among different objectives for the water managers, and is highly professional in making decisions for allocating water among use sectors and different areas.

scholarly journals Optimal Allocation of Surface Water Resources at the Provincial Level in the Uzbekistan Region of the Amudarya River Basin

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1446
Author(s):  
Min Wang ◽  
Xi Chen ◽  
Ayetiguli Sidike ◽  
Liangzhong Cao ◽  
Philippe DeMaeyer ◽  
...  
Keyword(s):  
River Basin ◽  
Water Allocation ◽  
Water Distribution ◽  
Optimal Allocation ◽  
Probability Distributions ◽  
Economic Benefits ◽  
Aral Sea ◽  
Incoming Flow ◽  
Provincial Level ◽  
Water Users

Water users in the Amudarya River Basin in Uzbekistan are suffering severe water use competition and uneven water allocation, which seriously threatens ecosystems, as shown, for example, in the well-known Aral Sea catastrophe. This study explores the optimized water allocation schemes in the study area at the provincial level under different incoming flow levels, based on the current water distribution quotas among riparian nations, which are usually ignored in related research. The optimization model of the inexact two-stage stochastic programming method is used, which is characterized by probability distributions and interval values. Results show that (1) water allocation is redistributed among five different sectors. Livestock, industrial, and municipality have the highest water allocation priority, and water competition mainly exists in the other two sectors of irrigation and ecology; (2) water allocation is redistributed among six different provinces, and allocated water only in Bukhara and Khorezm can satisfy the upper bound of water demand; (3) the ecological sector can receive a guaranteed water allocation of 8.237–12.354 km3; (4) under high incoming flow level, compared with the actual water distribution, the total allocated water of four sectors (except for ecology) is reduced by 3.706 km3 and total economic benefits are increased by USD 3.885B.

scholarly journals Ground station scheduling optimization for a model of a real-world problem instance

2021 ◽  
Author(s):  
Bryce Wildish
Keyword(s):  
Real World ◽  
Heuristic Algorithms ◽  
Complex Problem ◽  
Initial Guess ◽  
Fine Tuning ◽  
Problem Instance ◽  
Ground Station ◽  
Real World Problem ◽  
Crucial Component ◽  
Orbiting Spacecraft

Effective scheduling of communication windows between orbiting spacecraft and ground stations is a crucial component of efficiently using spacecraft resources. In all but the most trivial cases, this forces the operator to choose a subset of the potentially available access windows such that they can achieve the best possible usage of their hardware and other resources. This is a complex problem not normally solvable analytically, and as a result the standard approach is to apply heuristic algorithms which take an initial guess at a solution and improve upon it in order to increase its quality. Various such algorithms exist, with some being in common practice for this particular problem. This thesis covers the application of several of the most commonly-used algorithms on a problem instance. Additionally, a real-world problem instance is used, and the resultant practical constraints are addressed when applying the heuristics and fine-tuning them for this application.

Optimization of the geometrical parameters for elevated temperature hydro-mechanical deep drawing process of 2024 aluminum alloy

2020 ◽  
pp. 095440892094936
Author(s):  
S Yaghoubi ◽  
F Fereshteh-Saniee
Keyword(s):  
Aluminum Alloy ◽  
Elevated Temperature ◽  
Objective Function ◽  
Deep Drawing ◽  
Sheet Thickness ◽  
Bees Algorithm ◽  
Group Method ◽  
Geometrical Parameters ◽  
2024 Aluminum Alloy ◽  
Experimental Findings

This research is concerned with the effects of the geometrical parameters of the die in elevated temperature Hydro-Mechanical Deep Drawing (HMDD) process of 2024 aluminum alloy. A Group Method of Data Handling (GMDH) process was used to train a neural network in order to study the process behavior. Based on the maximum reduction in sheet thickness and the uniformity of the final product, an objective function was constructed. The Bees Algorithm (BA) was used to achieve the optimal values for process variables. To verify the simulation results, they were compared with the experimental findings gained via this research and an appropriate correlation was observed between these results. This comparison showed that, by optimization of the geometrical parameters of the process, the value of the combined objective function was the best one compared with all of the cases tried in the present investigation.

Spatial and Temporal Water Allocation in the Kissimmee River Basin

1977 ◽  
Vol 9 (1) ◽  
pp. 107-113 ◽  
Author(s):  
John E. Reynolds ◽  
J. Richard Conner
Keyword(s):  
Water Management ◽  
Water Supply ◽  
River Basin ◽  
Water Allocation ◽  
Cooperative Effort ◽  
The Public ◽  
Available Water ◽  
Kissimmee River ◽  
Strong Competition ◽  
Southern Florida

In many areas of the country, there is strong competition among agricultural, municipal, industrial and other users of water. Water managers are faced with the problem of allocating available water among alternative uses.The study [11] upon which this paper is based was a cooperative effort with the Central and Southern Florida Control District which is typical of many water management districts making decisions regarding allocation of a limited amount of water among uses and users. When the District was formed, it was developed with emphasis on facilities to provide relief from flooding. Water management responsibilities such as water supply, recreation and the preservation and enhancement of fish and wildlife have become important to the public and consequently have received recognition by those responsible for managing the water.

Robust scheduling in two-stage assembly flow shop problem with random machine breakdowns: integrated meta-heuristic algorithms and simulation approach

2019 ◽  
Vol 39 (5) ◽  
pp. 944-962 ◽  
Author(s):  
Sahar Tadayonirad ◽  
Hany Seidgar ◽  
Hamed Fazlollahtabar ◽  
Rasoul Shafaei
Keyword(s):  
Manufacturing Systems ◽  
Heuristic Algorithms ◽  
Flow Shop ◽  
Imperialist Competitive Algorithm ◽  
Flow Shop Scheduling ◽  
Process Time ◽  
Two Stage ◽  
Content Type ◽  
Robust Scheduling ◽  
Machine Breakdowns

Purpose In real manufacturing systems, schedules are often disrupted with uncertainty factors such as random machine breakdown, random process time, random job arrivals or job cancellations. This paper aims to investigate robust scheduling for a two-stage assembly flow shop scheduling with random machine breakdowns and considers two objectives makespan and robustness simultaneously. Design/methodology/approach Owing to its structural and algorithmic complexity, the authors proposed imperialist competitive algorithm (ICA), genetic algorithm (GA) and hybridized with simulation techniques for handling these complexities. For better efficiency of the proposed algorithms, the authors used artificial neural network (ANN) to predict the parameters of the proposed algorithms in uncertain condition. Also Taguchi method is applied for analyzing the effect of the parameters of the problem on each other and quality of solutions. Findings Finally, experimental study and analysis of variance (ANOVA) is done to investigate the effect of different proposed measures on the performance of the obtained results. ANOVA's results indicate the job and weight of makespan factors have a significant impact on the robustness of the proposed meta-heuristics algorithms. Also, it is obvious that the most effective parameter on the robustness for GA and ICA is job. Originality/value Robustness is calculated by the expected value of the relative difference between the deterministic and actual makespan.

scholarly journals A Stochastic Optimization Model for Agricultural Irrigation Water Allocation Based on the Field Water Cycle

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1031 ◽  
Author(s):  
Zehao Yan ◽  
Mo Li
Keyword(s):  
Irrigation Water ◽  
Water Allocation ◽  
Optimal Allocation ◽  
Water Cycle ◽  
Decision Makers ◽  
Irrigation District ◽  
Agricultural Irrigation ◽  
Dynamic Changes ◽  
Irrigation Water Allocation ◽  
Allocation Strategies

Agricultural water scarcity is a global problem and this reinforces the need for optimal allocation of irrigation water resources. However, decision makers are challenged by the complexity of fluctuating stream condition and irrigation quota as well as the dynamic changes of the field water cycle process, which make optimal allocation more complex. A two-stage chance-constrained programming model with random parameters in the left- and right-hand sides of constraints considering field water cycle process has been developed for agricultural irrigation water allocation. The model is capable of generating reasonable irrigation allocation strategies considering water transformation among crop evapotranspiration, precipitation, irrigation, soil water content, and deep percolation. Moreover, it can deal with randomness in both the right-hand side and the left-hand side of constraints to generate schemes under different flow levels and constraint-violation risk levels, which are informative for decision makers. The Yingke irrigation district in the middle reaches of the Heihe River basin, northwest China, was used to test the developed model. Tradeoffs among different crops in different time periods under different flow levels, and dynamic changes of soil moisture and deep percolation were analyzed. Scenarios with different violating probabilities were conducted to gain insight into the sensitivity of irrigation water allocation strategies on water supply and irrigation quota. The performed analysis indicated that the proposed model can efficiently optimize agricultural irrigation water for an irrigation district with water scarcity in a stochastic environment.

NEW DEVELOPMENT OF ANALYSIS TOOL FOR OPTIMIZING GENERATION COST WITH GAS EMISSION VIA AN ELECTROMAGNETISM LIKE ALGORITHM

2016 ◽  
Vol 78 (6-3) ◽  
Author(s):  
F.Y.C. Albert ◽  
S.P. Koh ◽  
C. P. Chen ◽  
S. K. Tiong
Keyword(s):  
Power Generation ◽  
Objective Function ◽  
Environmental Concern ◽  
Economic Dispatch ◽  
Complex Problem ◽  
Analysis Tool ◽  
New Development ◽  
Generation Cost ◽  
Electric Power Generation Systems ◽  
Power Generation Systems

This paper addresses the preliminary new development results of the evolutionary algorithm technique to optimize the formulated problems incorporating the generation cost with emission gas as objective function or constraints. The power generation cost with emission gas are a complex problem which also the major concerns in electric power generation systems in the Environmental or Economic Dispatch Problems (EDP). Thus, due to environmental concern the electrical utilities required to minimize the emission level while optimizing the thermal generating units at a minimum generating cost and hence, satisfying the load demand and the emissions. In this work the electromagnetism-Like algorithm (EML) has been employed for optimizing generation cost and emission constraints economic dispatch problem. The proposed decision analysis tool software in this work will optimize the generation cost with emission gas objective function. The best generation cost with emission gas solution are obtained from different fuel technology via the developed software. 

scholarly journals Role and relevance of three enabling conditions to resolve inter-provincial water conflicts in the Indus basin within Pakistan

Water Policy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 811-824
Author(s):  
Shahmir Janjua ◽  
Ishtiaq Hassan ◽  
Shafiqul Islam
Keyword(s):  
Water Allocation ◽  
Complex Problem ◽  
Situational Factors ◽  
Indus Basin ◽  
Illustrative Case ◽  
Water Conflicts ◽  
Transboundary Water ◽  
Competing Demands ◽  
Transboundary Water Management ◽  
Necessary And Sufficient

Abstract Addressing water access, allocation, and use becomes a complex problem when it crosses multiple boundaries: political, jurisdictional, and societal, as well as ecological, biogeochemical, and physical. This paper focuses on transboundary water management (TWM) problems among the riparians with conflicting needs and competing demands. The complexity of TWM problems arises because of interdependencies among variables, processes, actors, and institutions operating at various scales. For such situations, the traditional notion of necessary and sufficient causal conditions is not adequate to resolve TWM problems. In essence, the resolution of many TWM issues becomes contingent upon the changes that occur within the context of the problem. A key for initiating and sustaining the resolution of complex TWM issues appears to be a set of enabling conditions, not any easily identifiable and replicable causal conditions or mechanisms. Thus, before analyzing and addressing contingent and situational factors important for any TWM issues, this paper argues for a reframing of these issues and examining the role and relevance of three enabling conditions. Using the inter-provincial water conflicts for the Indus basin within Pakistan as an illustrative case, it shows why over 30 years of dialog and discourse could not create any formal water allocation agreement. Then, it discusses how the Water Apportionment Accord of 1991 created the enabling conditions to address inter-provincial water conflicts within Pakistan in an adaptive way.

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