scholarly journals Parameter Identification by High-Resolution Inverse Numerical Model Based on LBM/CMA-ES: Application to Chalk Aquifer (North of France)

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1574
Author(s):  
Lahcen Zouhri ◽  
Sami Kaidi ◽  
Hassan Smaoui
Keyword(s):  
High Resolution ◽  
Optimization Algorithm ◽  
Decision Support Tool ◽  
Integrated Water Resources Management ◽  
Pumping Test ◽  
Heterogeneous Distribution ◽  
Support Tool ◽  
Integrated Optimization ◽  
Hydrogeological Parameters ◽  
Magnetic Resonance Sounding

The present paper proposes the numerical solution of an inverse problem in groundwater flow (Darcy’s equation). This solution was achieved by combining a high-resolution new code HYSFLO-LBM (Hydrodynamic of Subsurface Flow by Lattice Boltzmann Method), based on LBM, to solve the direct problem, and the metaheuristic optimization algorithm CMA-ES ES (Covariance Matrix Adaptation-Evolution Strategy) to solve the optimization step. The integrated optimization algorithm which resulted from this combination, HYSFLO-LBM/CMA-ES, was applied to the hydrogeological experimental site of Beauvais (Northern France), instrumented by a set of sensors distributed over 20 hydrogeological wells. Hydrogeological parameters measured by the sensors are necessary to understand the aquifer functioning and to serve as input data for the identification of the transmissivity field by the HYSFLO-LBM/CMA-ES code. Results demonstrated an excellent concordance between the integrated optimization algorithm and hydrogeological applied methods (pumping test and magnetic resonance sounding). The spatial distribution of the transmissivity and hydraulic conductivity are related to the heterogeneous distribution of aquifer formations. The LBM and CMA-ES were chosen for their proven excellent performance and lesser cost, in terms of both money and time, unlike the geophysical survey and pumping test. The model can be used and developed as a decision support tool for integrated water resources management in the region.

scholarly journals A Hierarchical Route Guidance Framework for Off-Road Connected Vehicles

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Judhajit Roy ◽  
Nianfeng Wan ◽  
Angshuman Goswami ◽  
Ardalan Vahidi ◽  
Paramsothy Jayakumar ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Optimal Path ◽  
Computational Cost ◽  
Grid Cell ◽  
Decision Support Tool ◽  
Route Guidance ◽  
Low Resolution ◽  
Support Tool ◽  
Optimal Cost

A new framework for route guidance, as part of a path decision support tool, for off-road driving scenarios is presented in this paper. The algorithm accesses information gathered prior to and during a mission which are stored as layers of a central map. The algorithm incorporates a priori knowledge of the low resolution soil and elevation information and real-time high-resolution information from on-board sensors. The challenge of high computational cost to find the optimal path over a large-scale high-resolution map is mitigated by the proposed hierarchical path planning algorithm. A dynamic programming (DP) method generates the globally optimal path approximation based on low-resolution information. The optimal cost-to-go from each grid cell to the destination is calculated by back-stepping from the target and stored. A model predictive control algorithm (MPC) operates locally on the vehicle to find the optimal path over a moving radial horizon. The MPC algorithm uses the stored global optimal cost-to-go map in addition to high resolution and locally available information. Efficacy of the developed algorithm is demonstrated in scenarios simulating static and moving obstacles avoidance, path finding in condition-time-variant environments, eluding adversarial line of sight detection, and connected fleet cooperation.

A protocol for sampling pastures in hill country

2016 ◽  
Vol 78 ◽  
pp. 203-209 ◽  
Author(s):  
K.J. Hutchinson ◽  
D.R. Scobie ◽  
J. Beautrais ◽  
A.D. Mackay ◽  
G.M. Rennie ◽  
...  
Keyword(s):  
Random Sampling ◽  
Geographical Information Systems ◽  
Field Measurements ◽  
Decision Support Tool ◽  
Accurate Estimate ◽  
Ease Of Use ◽  
Geographical Information ◽  
Support Tool ◽  
Topographic Complexity ◽  
Hill Country

To develop a protocol to guide pasture sampling for estimation of paddock pasture mass in hill country, a range of pasture sampling strategies, including random sampling, transects and stratification based on slope and aspect, were evaluated using simulations in a Geographical Information Systems computer environment. The accuracy and efficiency of each strategy was tested by sampling data obtained from intensive field measurements across several farms, regions and seasons. The number of measurements required to obtain an accurate estimate was related to the overall pasture mass and the topographic complexity of a paddock, with more variable paddocks requiring more samples. Random sampling from average slopes provided the best balance between simplicity and reliability. A draft protocol was developed from the simulations, in the form of a decision support tool, where visual determination of the topographic complexity of the paddock, along with the required accuracy, were used to guide the number of measurements recommended. The protocol was field tested and evaluated by groups of users for efficacy and ease of use. This sampling protocol will offer farmers, consultants and researchers an efficient, reliable and simple way to determine pasture mass in New Zealand hill country settings. Keywords: hill country, feed budgeting, protocol pasture mass, slope

Neural Network Ensemble Approach to Pushed Convoys Dispatching Problems

2020 ◽  
Vol 27 (1) ◽  
pp. 70-82 ◽  
Author(s):  
Aleksandar Radonjić ◽  
Danijela Pjevčević ◽  
Vladislav Maraš
Keyword(s):  
Neural Network ◽  
Decision Support Tool ◽  
Primary Objective ◽  
Danube River ◽  
Decision Making Process ◽  
Neural Network Ensemble ◽  
Support Tool ◽  
Shipping Company ◽  
The Republic ◽  
Power Measurements

AbstractThis paper investigates the use of neural networks (NNs) for the problem of assigning push boats to barge convoys in inland waterway transportation (IWT). Push boat–barge convoy assignmentsare part of the daily decision-making process done by dispatchers in IWT companiesforwhich a decision support tool does not exist. The aim of this paper is to develop a Neural Network Ensemble (NNE) model that will be able to assist in push boat–barge convoy assignments based on the push boat power.The primary objective of this paper is to derive an NNE model for calculation of push boat Shaft Powers (SHPs) by using less than 100% of the experimental data available. The NNE model is applied to a real-world case of more than one shipping company from the Republic of Serbia, which is encountered on the Danube River. The solution obtained from the NNE model is compared toreal-world full-scale speed/power measurements carried out on Serbian push boats, as well as with the results obtained from the previous NNE model. It is found that the model is highly accurate, with scope for further improvements.

A Decision Support Tool for Evaluating Big Data Investment in Transport: Empirical evidence from European use cases of NOESIS project

2020 ◽  
Author(s):  
Christos Katrakazas ◽  
Natalia Sobrino ◽  
Ilias Trochidis ◽  
Jose Manuel Vassallo ◽  
Stratos Arampatzis ◽  
...  
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Empirical Evidence ◽  
Decision Support Tool ◽  
Use Cases ◽  
Support Tool
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