scholarly journals Simulation-Optimization for Conjunctive Water Resources Management and Optimal Crop Planning in Kushabhadra-Bhargavi River Delta of Eastern India

2020 ◽  
Vol 17 (10) ◽  
pp. 3521
Author(s):  
Madan K. Jha ◽  
Richard C. Peralta ◽  
Sasmita Sahoo
Keyword(s):  
Water Resources ◽  
Simulation Model ◽  
Seawater Intrusion ◽  
Optimization Model ◽  
Crop Yields ◽  
Simulation Optimization ◽  
Optimization Models ◽  
Water Model ◽  
Cropping Pattern ◽  
Groundwater Exploitation

Water resources sustainability is a worldwide concern because of climate variability, growing population, and excessive groundwater exploitation in order to meet freshwater demand. Addressing these conflicting challenges sometimes can be aided by using both simulation and mathematical optimization tools. This study combines a groundwater-flow simulation model and two optimization models to develop optimal reconnaissance-level water management strategies. For a given set of hydrologic and management constraints, both of the optimization models are applied to part of the Mahanadi River basin groundwater system, which is an important source of water supply in Odisha State, India. The first optimization model employs a calibrated groundwater simulation model (MODFLOW-2005, the U.S. Geological Survey modular ground-water model) within the Simulation-Optimization MOdeling System (SOMOS) module number 1 (SOMO1) to estimate maximum permissible groundwater extraction, subject to suitable constraints that protect the aquifer from seawater intrusion. The second optimization model uses linear programming optimization to: (a) optimize conjunctive allocation of surface water and groundwater and (b) to determine a cropping pattern that maximizes net annual returns from crop yields, without causing seawater intrusion. Together, the optimization models consider the weather seasons, and the suitability and variability of existing cultivable land, crops, and the hydrogeologic system better than the models that do not employ the distributed maximum groundwater pumping rates that will not induce seawater intrusion. The optimization outcomes suggest that minimizing agricultural rice cultivation (especially during the non-monsoon season) and increasing crop diversification would improve farmers’ livelihoods and aid sustainable use of water resources.

Experiences in Modeling, Simulation, and Optimization of Chemical Industry Supply Chains

Manufacturing ◽  
2002 ◽  
Author(s):  
Charles R. Standridge ◽  
David R. Heltne
Keyword(s):  
Supply Chain ◽  
Simulation Model ◽  
Production Planning ◽  
Simulation Optimization ◽  
Process Industry ◽  
Plant Production ◽  
Optimization Models ◽  
Individual Plant ◽  
Simulation And Optimization ◽  
Combined Simulation

We have developed and applied simulation as well as combined simulation – optimization models to represent process industry plant logistics and supply chain operations. The simulation model represents plant production, inventory, and shipping operations as well as inter-plant shipments. When a combined simulation-optimization approach is used, the simulation periodically invokes a classical production planning optimization model to set production and shipping levels. These levels are retrieved by and used in the simulation model. Process industry supply chain operations include stochastic elements such as customer demands whose expected values may vary in time as well as transportation lead times. The complexity of individual plant operations and logistics must be considered. Simulation provides the methods needed to integrate these elements in a single model. Periodically during a simulation run, production planning decisions that require optimization models may be made. Simulation experimental results are used to determine service levels to end customers as well as to set rail fleet sizes, inventory capacities, and capital equipment requirements for logistics as well as to assess alternative shipping schedules.

scholarly journals Optimization of Irrigation Scheduling for Maize in an Arid Oasis Based on Simulation–Optimization Model

Agronomy ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 935 ◽  
Author(s):  
Jiang Li ◽  
Xiyun Jiao ◽  
Hongzhe Jiang ◽  
Jian Song ◽  
Lina Chen
Keyword(s):  
Simulation Model ◽  
Optimization Model ◽  
Simulation Optimization ◽  
Water Productivity ◽  
Irrigation Schedule ◽  
Irrigation Scheduling ◽  
Water Saving ◽  
Scheduling Optimization ◽  
Agricultural Water Resources ◽  
Crop Water Consumption

In arid regions, irrigation scheduling optimization is efficient in coping with the shortage of agricultural water resources. This paper developed a simulation–optimization model for irrigation scheduling optimization for the main crop in an arid oasis, aiming to maximize crop yield and minimize crop water consumption. The model integrated the soil water balance simulation model and the optimization model for crop irrigation scheduling. The simulation model was firstly calibrated and validated based on field experiment data for maize in 2012 and 2013, respectively. Then, considering the distribution of soil types and irrigation districts in the study area, the model was used to solve the optimal irrigation schedules for the scenarios of status quo and typical climate years. The results indicated that the model is applicable for reflecting the complexities of simulation–optimization for maize irrigation scheduling. The optimization results showed that the irrigation water-saving potential of the study area was between 97 mm and 240 mm, and the average annual optimal yield of maize was over 7.3 t/ha. The simulation–optimization model of irrigation schedule established in this paper can provide a technical means for the formulation of irrigation schedules to ensure yield optimization and water productivity or water saving.

scholarly journals Uncertainty Analysis in the Optimization Process of Groundwater Exploitation Scheme Based on SVR Method—A Case Study of Hetao Plain

2018 ◽  
Author(s):  
Yongkai An ◽  
Wenxi Lu ◽  
Xueman Yan
Keyword(s):  
Uncertainty Analysis ◽  
Simulation Model ◽  
Optimization Model ◽  
Surrogate Model ◽  
Surrogate Models ◽  
Groundwater Table ◽  
Pumping Rate ◽  
Groundwater Exploitation ◽  
Computational Load ◽  
Hetao Plain

This paper introduces a surrogate model to reduce the huge computational load in the process of simulation-optimization and uncertainty analysis. First, the groundwater numerical simulation model was established, calibrated and verified in the northeast of Hetao Plain. Second, two surrogate models of simulation model were established using support vector regression (SVR) method, one (surrogate model A, SMA) was used to describe the corresponding relationship between the pumping rate and average groundwater table drawdown, and another (surrogate model B, SMB) was used to express the corresponding relationship between the hydrogeological parameter values and average groundwater table drawdown. Third, an optimization model was established to search an optimal groundwater exploitation scheme using the maximum total pumping rate as objective function and the limitative average groundwater table drawdown as constraint condition, the SMA was invoked by the optimization model for obtaining the optimal groundwater exploitation scheme. Finally, the SMB was invoked in the process of uncertainty analysis for assessing the reliability of optimal groundwater exploitation scheme. Results show that the relative error and root mean square error between simulation model and the two surrogate models are both less than 5%, which is a high approximation accuracy. The SVR surrogate model developed in this study could not only considerably reduce the computational load, but also maintain high computational accuracy. The optimal total pumping rate is 7947 m3/d and the reliability of optimal scheme is 40.21%. This can thus provide an effective method for identifying an optimal groundwater exploitation scheme and assessing the reliability of scheme quickly and accurately.

scholarly journals Simulation and optimization of groundwater exploitation for the water supply of Tarnów agglomeration (southern Poland)

2015 ◽  
Vol 9 (1) ◽  
pp. 21-29 ◽  
Author(s):  
Wiktor Treichel ◽  
Andrzej Haładus ◽  
Robert Zdechlik
Keyword(s):  
Water Supply ◽  
Simulation Model ◽  
Simulation Optimization ◽  
Single Layer ◽  
Optimization Procedure ◽  
Optimization Approach ◽  
Objective Functions ◽  
Groundwater Exploitation ◽  
Simulation And Optimization ◽  
Southern Poland

Abstract This paper presents the application of the simulation-optimization approach to optimize groundwater intake for the water supply of Tarnów agglomeration (southern Poland). Tarnów agglomeration is supplied with water from extensively exploited surface and groundwater intakes located at the confluence of the rivers Dunajec and Biała. Groundwater intakes capture water from the Quaternary aquifer, which is recharged by rainfall and direct infiltration from the rivers and irrigation ditches. Hydrogeological conditions occurring within the area under consideration were mapped by using a single-layer numerical model in Processing Modflow software. After calibrating the simulation model, a simulation-optimization approach was developed with the aim of aiding the process of searching for the best scheme of exploitation of groundwater intake. The unit response matrix method was used to connect the simulation model with the optimization procedure. In the optimization task the objective functions concerning the total volume of groundwater intake discharge and infiltration amount from the River Dunajec or irrigation ditches were applied. Several constraints concerned both the maximum and minimum capacity of individual wells and the entire intake, as well as desirable or undesirable drawdown values in selected control points and control areas. Finally, twelve variants of optimization tasks, for different boundary conditions and different objective functions, were solved and optimal schemes of well discharge distribution within the intake were calculated. The results of optimization show that, depending on the intended objective and the constraints imposed, the best exploitation scenario under the given conditions may be found.

scholarly journals Hybrid Optimization Model for Conjunctive Use of Surface and Groundwater Resources in Water Deficit Irrigation System

2021 ◽  
Author(s):  
Karthikeyan MoothampalayamSampathkumar ◽  
Saravanan Ramasamy ◽  
Balamurugan Ramasubbu ◽  
Hamid Reza Pourghasemi ◽  
Saravanan Karuppanan ◽  
...  
Keyword(s):  
Water Resources ◽  
Water Deficit ◽  
Optimization Model ◽  
Conjunctive Use ◽  
Hybrid Optimization ◽  
Cropping Pattern ◽  
Net Benefit ◽  
Hybrid Optimization Model ◽  
Optimal Cropping ◽  
Surface And Groundwater

Increasing demand for food production with limited available water resources pose the threat to agricultural activities. The conjunctive allocation of water resources maximizes the net benefit of farmers efficiently. In this study, a novel hybrid optimization model was developed based on a genetic algorithm (GA), bacterial foraging optimization (BFO) and ant colony optimization (ACO) to maximize the net benefit of water deficit Sathanur reservoir command. The GA-based opti-mization model considered crop-related physical and economical parameters to derive optimal cropping patterns for three different conjunctive use policies and further allocation of surface and groundwater for different crops are enhanced with the BFO. The allocation of surface and groundwater for the head, middle and tail reach obtained from BFO is considered as input to ACO as a guiding mechanism to attain an optimal cropping pattern. Comparing the average produc-tivity values Policy 3 (3.665 Rs/m3) has better values relating to Policy 1 (3.662 Rs/m3) and Policy 2 (3.440 Rs/m3). Thus, the developed novel hybrid optimization model (GA-BFO-ACO) is very promising to enhance the farmer's net income as well as for the command area water conservation and can be replicated in other irrigated regions of the globe to overcome chronic land and water problems.

scholarly journals Optimization of Groundwater Pumping and River-Aquifer Exchanges for Management of Water Resources

2021 ◽  
Author(s):  
Mayank Bajpai ◽  
Shishir Gaur ◽  
Anurag Ohri ◽  
Shreyansh Mishra ◽  
Hervé Piégay ◽  
...  
Keyword(s):  
Water Resources ◽  
Boundary Conditions ◽  
Simulation Optimization ◽  
Optimization Models ◽  
Multi Objective Optimization ◽  
Groundwater Pumping ◽  
Multi Objective ◽  
Model Domain ◽  
Pumping Rates ◽  
Pareto Fronts

Groundwater pumping influences the rate of River-Aquifer (R-A) exchanges and alters the water budget of the aquifer. Therefore, fulfilling the total water demand of the area, with an optimal pumping rate of wells and optimal R-A exchanges rate, is important for the sustainable management of water resources and aquatic ecosystems. Meanwhile, comparison of the output of different simulation-optimization techniques, which is used for the solution of water resource management problems, is a very challenging task where different Pareto fronts are compared to identify the best results. In the present work, mathematical models were developed to simulate the R-A exchanges for the lower part of the River Ain, France. The developed models were coupled with optimization models in MATLAB environment and were executed to solve the multi-objective optimization problem based on the maximization of pumping rates of wells and maximization of groundwater input into the river Ain through R-A exchanges. The Pareto front developed by different simulation-optimization models was compared and analyzed. The Pareto fronts were juxtaposed based on the convergence, total diversity, and uniformity with the help of different performance metrics like hypervolume, generational distance, inverted generational distance, etc. The impact of different groundwater models based on domain size and boundary conditions was also examined. Results show the dominance of MOPSO over other optimization algorithms and concluded that the maximization of pumping rates significantly changes after considering the R-A exchanges-based objective function. It is observed that the model domain also alters the output of simulation-optimization, therefore the model domain and corresponding boundary conditions should be selected carefully for the field application of management models. ANN models were also developed to deal with the computationally expensive simulation model by reducing the processing time and were found efficient. Keywords: Simulation-Optimization, Multi-Objective optimization, Artificial Neural Network, River-Aquifer exchanges.

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