Optimal Network Research Articles

Solution of the Steiner problem in the search for optimal network structure using population optimization methods

Subject of research: This paper considers an example of solving the Steiner problem for the design of utilities, in particular, power lines. Purpose of research: The study is electrical networks. The study is to assess the suitability of using genetic algorithms to find the network connection scheme with the lowest total active power losses. Methods and objects of research: Heuristic methods of population optimization are applied as a research method. Main results of research: As a result of the work, the suitability of applying genetic algorithms to solve the Steiner problem on the example of an electrical network to minimize total power losses has been evaluated.

Hydrogen for net-zero emissions in ASEAN by 2050

Hydrogen is believed to be a promising decarbonization vector. An optimal hydrogen supply-chain network achieving net-zero emissions in ASEAN by 2050 is determined. A logistic profile for decarbonization and declining costs with technological advances are assumed. It is found that the region has the potential to fully decarbonize its power sector with renewable energy if a regional power grid is installed. A mix of green (from surplus renewable power), blue (from natural gas) or orange (from biomass) hydrogen, the latter two with carbon capture and sequestration, meets industry, transport and power (without a regional power grid) sector demands. Results show that available biomass is insufficient to meet decarbonization targets beyond 2045 without green hydrogen. Prioritizing energy security and sustainability has minimal incremental costs compared to importing liquefied natural gas for blue hydrogen production. Optimal production locations are Myanmar, Cambodia for green hydrogen, and Indonesia, Vietnam for orange and blue hydrogen.

Real-time robust liver and gallbladder segmentation during laparoscopic cholecystectomy using convolutional neural networks: an analysis

Aim: Images in different laparoscopic cholecystectomy datasets are acquired using various camera models, parameters, and settings, with the annotation methods varying by institution. These factors result in inconsistent inference performance of the network model. This study aims to identify the optimal network model architecture for liver and gallbladder segmentation from several options. Then, the performance and robustness of the optimal network model are evaluated using an independent dataset that is not included in the training. Methods: The public dataset, CholecSeg8k, was utilized as the input for the network model training, validation, and testing. A local private dataset from KPJ Damansara Hospital, Selangor, Malaysia, was used for testing purposes only. For the implementation of liver and gallbladder segmentation, segmentation models, a public Python library was employed. Results: Among the experiments, highly accurate liver and gallbladder segmentation results were achieved using the feature pyramid network (FPN) architecture as the network model, with the Inception-ResNet-v2 architecture as the network backbone. The best-trained network model resulted in a loss of 0.070955, a mean intersection over union (IoU) score of 0.95896, and a mean F1-score of 0.9773 on the test set. However, visualized results for the private dataset contained considerable false-negative areas. Conclusion: The proposed automated technique has the potential to serve as an alternative to the conventional indocyanine green injection along with near-infrared fluorescence imaging (ICG-NIRF)-based method for liver and gallbladder segmentation during laparoscopic cholecystectomy. Future work will focus on enhancing the results of the private dataset. Additionally, a surgeon-assistant robotic arm that will use the liver and gallbladder segmentation results for camera steering will be analyzed.

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network

Cyberattacks are increasingly becoming more complex, which makes intrusion detection extremely difficult. Several intrusion detection approaches have been developed in the literature and utilized to tackle computer security intrusions. Implementing machine learning and deep learning models for network intrusion detection has been a topic of active research in cybersecurity. In this study, artificial neural networks (ANNs), a type of machine learning algorithm, are employed to determine optimal network weight sets during the training phase. Conventional training algorithms, such as back-propagation, may encounter challenges in optimization due to being entrapped within local minima during the iterative optimization process; global search strategies can be slow at locating global minima, and they may suffer from a low detection rate. In the ANN training, the Artificial Bee Colony (ABC) algorithm enables the avoidance of local minimum solutions by conducting a high-performance search in the solution space but it needs some modifications. To address these challenges, this work suggests a Deep Autoencoder (DAE)-based, vectorized, and parallelized ABC algorithm for training feed-forward artificial neural networks, which is tested on the UNSW-NB15 and NF-UNSW-NB15-v2 datasets. Our experimental results demonstrate that the proposed DAE-based parallel ABC-ANN outperforms existing metaheuristics, showing notable improvements in network intrusion detection. The experimental results reveal a notable improvement in network intrusion detection through this proposed approach, exhibiting an increase in detection rate (DR) by 0.76 to 0.81 and a reduction in false alarm rate (FAR) by 0.016 to 0.005 compared to the ANN-BP algorithm on the UNSW-NB15 dataset. Furthermore, there is a reduction in FAR by 0.006 to 0.0003 compared to the ANN-BP algorithm on the NF-UNSW-NB15-v2 dataset. These findings underscore the effectiveness of our proposed approach in enhancing network security against network intrusions.

Power distribution system restoration based on soft open points and islanding by distributed generations

Abstract A powerful and efficient program for restoring the electrical distribution system by effectively utilizing the maximum capabilities within the system, including soft open points, distributed generation resources, and network configuration changes, can significantly reduce both the quantity and duration of lost loads caused by permanent faults. In this research study, we address the issue of distribution network restoration with a focus on soft open points (SOPs) and intentional islanding using distributed generations. This problem is formulated as a constrained optimization problem in order to determine the optimal distribution network configuration, quality of intentional islanding through DGs, control function of SOPs, and amount of load shedding. The objective is to minimize lost load while minimizing switching operations and preferably minimal islanding. Given that this problem involves multiple complexities such as combinatorial nature, mixed-integer variables, non-linearity, non-convexity along with numerous variables and constraints; we employ an evolutionary method known as simulated annealing (SA) algorithm to solve it without any simplifications or assumptions about convexity. To enhance efficiency during implementation of SA algorithm, Kruskal’s algorithm is utilized for generating radial solutions which restricts search space to feasible solutions resulting in quicker attainment of high-quality optimal solutions. Finally, the outcomes of implementing the suggested approach on the 69-bus IEEE distribution system are presented and examined. It is demonstrated that by leveraging the potential of soft open points in load transfer control and network voltage regulation, along with utilizing intentional islanding capability provided by distributed generation resources, the restoration capability of the distribution system can be greatly enhanced.

Thermal management of sodium-oxygen-hydrogen (Na-O-H) thermochemical water splitting for sustainable hydrogen production

Owing to environmental problems caused by the consumption of fossil fuels and the growing global demand for energy, interests in usage of hydrogen as a green energy carrier has grown. In particular, water is considered a prospect source for green hydrogen production. In this paper, a three-step sodium-oxygen-hydrogen (Na–O–H) thermochemical cycle, which is a newly developed cycle that can operate between 400 and 500 °C is implemented. Also, low number of steps, which results in less system complexity and easier system design is another advantage of the Na–O–H thermochemical cycle. In the first step of this study, a basic model of the three-step pure Na–O–H cycle is modeled in order to produce 1 kg/s of hydrogen. Moreover, output data from basic modeling is used for cycle energy management. Then, heat recovery is performed within the cycle streams to reduce the external energy demand and make the Na–O–H cycle more feasible. To achieve this objective, pinch method, which is an effective method for designing and optimizing plants is used. Several heat exchanger network designs have been developed and compared from different aspects. Finally, a design with the least heat requirement among others is selected and optimized based on total annualized cost objective function to introduce the optimum final design exchanger network. As a result of this work, it is found that using the final optimized design, external heating loads accounted for just 5.8% of the total load, while external cooling loads accounted for 19.3% of the total load. This implies that the Na–O–H thermochemical cycle recovered 74.7% of the energy required for heating and cooling of the streams. The implementation of the proposed heat exchanger network design boosts the overall energy efficiency of the Na–O–H thermochemical cycle. Using final heat recover scheme the overall efficiency of the cycle increased from 45.36% to 52.86%, representing an impressive 7.50% improvement. This significant achievement underscores the pivotal role of this study in developing an effective and optimized model for the Na–O–H thermochemical cycle, which holds great promise for future applications.

Automated fault diagnosis of rotating machinery using sub domain greedy Network Architecture search

Network Architecture Search (NAS) automates hyperparameter adjustments in deep learning models, offering a potent solution for building intelligent fault diagnosis models. Despite its potential, NAS faces challenges in this application. The primary issues include the excessive time required when searching across the full data domain and the inefficacy of the common random search strategy in yielding high-performance sub networks. This study addresses these challenges through several key initiatives: It defines a specific library of hyperparameters tailored for intelligent fault diagnosis tasks; constructs sub data domain model clusters to reduce the computational costs associated with NAS; and introduces a hyperparameter search strategy leveraging a greedy algorithm, which enhances the search efficiency for optimal diagnostic sub network and minimizes its prediction errors. When applied to datasets involving gear and bearing faults, the proposed method demonstrates a reduction in both the time cost of searches and the prediction errors of sub networks, compared to traditional Network Architecture Search. Moreover, it outperforms manually tuned deep learning models by simultaneously optimizing multiple sets of hyperparameter and automatically generating higher accuracy diagnostic sub networks.

Application of Visual Transformer in Low-resolution Thermal Infrared Image Recognition

Abstract Addressing the challenges of inadequate accuracy and limited robustness exhibited by current lightweight object detection networks specifically tailored for low-resolution thermal infrared face detection scenarios, this paper delves into developing an ultra-lightweight thermal infrared face detection algorithm that leverages visual attention mechanisms. To ascertain the optimal neural network complexity, a series of comparative experiments are meticulously conducted. With Yolo-FastestDet serving as the benchmark, this study endeavors to compress the backbone network, striking a delicate balance between network depth and detection speed. Additionally, to bolster the network’s capacity for profound feature extraction and precise discrimination of target edges and small objects, a Transformer-Encoder-based visual attention module is seamlessly integrated. Consequently, a lightweight face detection algorithm, enriched with attention mechanisms, is formulated. Furthermore, to mitigate the scarcity of low-resolution infrared face image data, a self-constructed visible-infrared face dataset is employed for training and evaluation purposes. The experimental outcomes reveal that the proposed algorithm attains an impressive mAP@0.5 score of 0.953 on the test dataset while satisfying the stringent real-time detection criterion of 30 frames per second (FPS) when deployed on an embedded Raspberry Pi CPU.

Reliable Prediction of Solar Photovoltaic Power and Module Efficiency using Bayesian Surrogate Assisted Explainable Data-Driven Model

This study proposes a Bayesian surrogate-driven explainable deep neural network model to predict and interpret the module efficiency and maximum output power of three commercially available photovoltaic modules: monocrystalline silicon, polycrystalline silicon, and amorphous silicon during the winter season. In addition, the influence of the photovoltaic material and ambient conditions on the predicted power and module efficiency is investigated. The model inputs include solar irradiance, module material (monocrystalline, polycrystalline, and amorphous silicon), season of the year (months), and time of day (morning to evening). Experiments were conducted on these three modules during the winter using data from Rawalpindi, Pakistan. These data were then fed into the deep neural network model to train and yield predictions. Several preprocessing techniques such as logarithmic transformation, square root, cube root, reciprocal, and exponential transformations are employed to improve the linearity of distributed data. For hyper-parameters optimization, Gaussian process, gradient boost regression trees, and random forest are used. The results show that the optimal deep neural network using random forest surrogate model along with square root and exponential transformation is able to predict the maximum power output and module efficiency of the considered photovoltaic modules for the entire investigated period with a correlation coefficient, R2 = 0.998. The least accurate model (R2=0.991) is a Gaussian process using simple data distribution. These results hold valuable insights for predicting and optimizing the performance of other solar photovoltaic modules in various climates and different seasons of the year.

Towards Forming Optimal Communication Network for Effective Power System Restoration

Towards Forming Optimal Communication Network for Effective Power System Restoration

Comprehensive techno-economic and environmental assessment for 2,3-butanediol production from bread waste

Bread waste (BW) is a common food waste in Europe and North America and has enormous potential as a biorefinery substrate for the sustainable synthesis of various platform chemicals. Our previous work made use of BW for the fermentative production of 2,3–butanediol (BDO). The present work evaluated the economic prospects and environmental consequences associated with the overall processes, handling 100 metric tons BW per day. The comprehensive process design using Aspen Plus and integrated techno-economic and environmental assessment was carried out for two different BW hydrolysis scenarios: acid and enzyme hydrolysis, followed by fermentation and extraction-based downstream BDO separation. The optimal heat exchanger network was designed using pinch analysis, which improved the energy efficiency of the process significantly, with about 10 % savings of BDO production costs. Despite this improvement, the BDO derived from BW was exorbitant (4.2–6.9 $/kg) compared to the market price (3.23 $/kg) due to relatively higher capital investment for the current plant capacity. Further, the process inventory was modelled in SimaPro v9.1.0 to estimate the environmental consequences of these production processes for impact categories, such as global warming (2.63 – 3.19 kg CO2 eq.), marine eutrophication (3.55 × 10-4 – 4.01 × 10-4 kg N eq.), terrestrial ecotoxicity (6.44 – 7.88 kg 1,4 − DCB), etc. Sensitivity and uncertainty analyses were also conducted to establish the reliability of the results. It was found that the enzyme hydrolysis was associated with lower environmental impacts than acid hydrolysis. This comprehensive study can be used as a guideline for developing sustainable BW-based biorefinery in the future.

Numerical Simulation and Artificial Neural Network Modeling of Cavitation on 2D Tandem Hydrofoils Arrangement

Abstract In the current research, we used a parametric approach to investigate cavitation with tandem-arranged Clark Y-11.7% hydrofoils. We simulated the tandem-arranged hydrofoil system using ANSYS FLUENT with the Zwart cavitation model combined with the Transition SST turbulence model. To substantiate the accuracy and reliability of the numerical model, computations were performed for an individual Clark Y-11.7% hydrofoil and compared to experimental results. The following parameters were studied in tandem arrangement: cavitation number(σ), horizontal direction spacing ratio between two hydrofoils(x), vertical direction spacing ratio between two hydrofoils(y), angle of attack (AOA) of the upstream hydrofoil (α 1), and angle of the downstream hydrofoil relative to the upstream hydrofoil (α 2). The lift coefficient (C L) was used to estimate due to the acceptable error. An optimal artificial neural network (ANN) model was trained based on the five-parameter combination from numerical simulations. The model’s weights and biases were presented, providing a prediction method for hydrofoils in tandem arrangement with cavitation. A global sensitivity analysis based on the trained model is implemented, which reveals the AOA of the downstream hydrofoil (α 2), the horizontal spacing ratio (x), and the AOA of the upstream hydrofoil (α 1) have a main effect on the performance of the tandem arrangement.

Removal of zinc from wastewaters using Turkish bentonite and artificial neural network [ANN] modeling

In this study, Ordu-Unye bentonite was used as an adsorbent in the removal of zinc from aqueous solutions. The aim of the experimental part of the study was to ascertain how zinc removal was affected by variables such as pH, adsorbent amount, contact time, and initial zinc concentration. In the second part of the experiments, bentonite was modified with two different acids and the adsorption performance of modified bentonite was also investigated. Characterization of raw and modified bentonites was also carried out using FTIR and XRD. It was observed that acid modification of bentonite negatively affected the zinc removal process from aqueous solutions. In this study, higher zinc removal (95 %) was obtained with raw bentonite compared to acid modified bentonites (58.4 % in HNO3 activated, 43.8 % for H2SO4 activated). Equilibrium isotherms were obtained and modelled to explain the adsorption mechanism. Adsorption isotherm studies showed that zinc adsorption fits well with Langmuir (R2: 0.99) and Temkin (R2: 0.97) models. Besides from these experimental investigations, various artificial neural network (ANN) training techniques were used to optimize the zinc adsorption process. By trial and error, the optimal performance was obtained by changing the number of hidden neurons in each layer of the neural network architecture. These models under study were analyzed to determine their R2 and mean square error (MSE) values, and the optimal outcomes were identified. Among the various training models of ANN, it was determined that the Bayesian Regularization method exhibited the optimum network architecture with the highest R2 (R2:0.995) and lowest MSE (MSE:0.0008) ratio.

An artificial neural network visible mathematical model for predicting slug liquid holdup in low to high viscosity multiphase flow for horizontal to vertical pipes

AbstractSlug liquid holdup (SLH) is a critical requirement for accurate pressure drop prediction during multiphase pipe flows and by extension optimal gas lift design and production optimization in wellbores. Existing empirical correlations provide inaccurate predictions because they were developed with regression analysis and data measured for limited ranges of flow conditions. Existing SLH machine learning models provide accurate predictions but are published without any equations making their use by other researchers difficult. The only existing ML model published with actual equations cannot be considered optimum because it was selected by considering artificial neural network (ANN) structures with only one hidden layer. In this study, an ANN-based model for SLH prediction with actual equations is presented. A total of 2699 data points randomly divided into 70%, 15%, and 15% for training, validation, and testing was used in constructing 71 different network structures with 1, 2, and 3 hidden layers respectively. Sensitivity analysis revealed that the optimum network structure has 3 hidden layers with 20, 5, and 15 neurons in the first, second, and third hidden layers, respectively. The optimum network structure was translated into actual equations with the aid of the weights, biases, and activation functions. Trend analysis revealed that this study’s model reproduced the expected effects of inputs on SLH. Test against measured data revealed that this study’s model is in agreement with measured data with coefficient of determinations of 0.9791, 0.9727, 0.9756, and 0.9776 for training, testing, validation, and entire datasets, respectively. Comparative study revealed that this study’s model outperformed existing models with a relative performance factor of 0.002. The present model is presented with visible mathematical equations making its implementation by any user easy and without the need for any ML framework. Unlike existing ANN-based models developed with one hidden layered ANN structures, the present model was developed by considering ANN structures with one, two, and three hidden layers, respectively.

Accelerating one-shot neural architecture search via constructing a sparse search space

Neural Architecture Search (NAS) has garnered significant attention for its ability to automatically design high-quality deep neural networks (DNNs) tailored to various hardware platforms. The major challenge for NAS is the time-consuming network estimation process required to select optimal networks from a large pool of candidates. Rather than training each candidate from scratch, recent one-shot NAS methods accelerate the estimation process by only training a supernet and sampling sub-networks from it, inheriting partial network architectures and weights. Despite significant acceleration, the supernet training with a large search space (i.e., the number of candidate sub-networks) still requires thousands of GPU hours to support high-quality sub-network sampling. In this work, we propose SparseNAS, an approach for one-shot NAS acceleration by reducing the redundancy of the search space. We observe that many sub-networks in the space are underperforming, with significant performance disparity to high-performance sub-networks. Crucially, this disparity can be observed early in the beginning of the supernet training. Therefore, we train an early predictor to learn this disparity and filter out high-quality networks in advance. Then, the supernet training will be conducted in this space sub-space. Compared to the state-of-the-art one-shot NAS, our SparseNAS reports a 3.1× training speedup with comparable network performance on the ImageNet dataset. Compared to the state-of-the-art acceleration method, SparseNAS reports a maximum of 1.5% higher Top-1 accuracy and 28% training cost reduction with a 7× bigger search space. Extensive experiment results demonstrated that SparseNAS achieves better trade-offs between efficiency and performance than state-of-the-art one-shot NAS.

Data-Driven Decision-Making for Flexible Natural Gas Allocation Under Uncertainties: An Agent-Based Modelling Approach

Despite the anticipated growth in the global demand for energy commodities, the frequently changing market dynamics imposed by environmental regulations and political sanctions create end-user demand uncertainties. This imposes the need for prompt quantitative decision-making approaches to understand how various market structures affect the planning of current natural gas projects. Agent-based modelling (ABM) emerges as a powerful approach to facilitate expedited and well-informed decisions amidst limited timeframes. This study deploys agent-based modelling to investigate natural gas allocation across various utilisation routes under diverse economic and environmental scenarios. Results from four main cases and two sub-scenarios imply that the allocation strategy is driven by utilisation routes considered in each case, followed by the allocation target (i.e., economic or environmental) and the operational bounds. The results reveal that cases prioritising natural gas monetisation for export outperform those meeting power requirements in average annual profitability. In case 4, considering a full network with power, the average annual profitability in the economic scenario reduces by approximately 47% compared to case 3, representing the optimal network configuration with $5.22 billion in average annual profitability. However, the economic scenario of case 3 demonstrates the second-highest rate of emissions (0.66 CO2-eq t/y), following the hydrogen-rich process routes in case 2. Overall, this study presents an innovative data-driven framework for enhancing strategic resource allocation in dynamic business environments. By integrating empirical evidence and technical data with an advanced technical tool (i.e., ABM), the framework provides decision-makers and policymakers with valuable insights for managing uncertainties and shifts in market structures, particularly in existing natural gas projects.

Optimizing target control in complex networks using edge-addition cost

This paper investigates optimal target control of complex networks through modifications in network topology. A novel edge-addition algorithm is proposed to ensure structural target controllability in directed networks with a single input. An edge-addition cost is introduced to measure the efficiency of achieving target control objectives. The relationships between the target node selection and the average node degree, respectively, and the edge-addition cost are examined by numerical simulations. It concludes that both the choice of target nodes and the average node degree significantly influence the cost-effectiveness of achieving target control. These findings offer valuable insights for the design of optimal networks in practical applications.

Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data

BackgroundRNA-sequencing technology provides an effective tool for understanding miRNA regulation in complex human diseases, including cancers. A large number of computational methods have been developed to make use of bulk and single-cell RNA-sequencing data to identify miRNA regulations at the resolution of multiple samples (i.e. group of cells or tissues). However, due to the heterogeneity of individual samples, there is a strong need to infer miRNA regulation specific to individual samples to uncover miRNA regulation at the single-sample resolution level.ResultsHere, we develop a framework, Scan, for scanning sample-specific miRNA regulation. Since a single network inference method or strategy cannot perform well for all types of new data, Scan incorporates 27 network inference methods and two strategies to infer tissue-specific or cell-specific miRNA regulation from bulk or single-cell RNA-sequencing data. Results on bulk and single-cell RNA-sequencing data demonstrate the effectiveness of Scan in inferring sample-specific miRNA regulation. Moreover, we have found that incorporating the prior information of miRNA targets can generally improve the accuracy of miRNA target prediction. In addition, Scan can contribute to construct cell/tissue correlation networks and recover aggregate miRNA regulatory networks. Finally, the comparison results have shown that the performance of network inference methods is likely to be data-specific, and selecting optimal network inference methods is required for more accurate prediction of miRNA targets.ConclusionsScan provides a useful method to help infer sample-specific miRNA regulation for new data, benchmark new network inference methods and deepen the understanding of miRNA regulation at the resolution of individual samples.

Greedy selection of optimal location of sensors for uncertainty reduction in seismic moment tensor inversion

We address an optimal sensor placement problem through Bayesian experimental design for seismic full waveform inversion for the recovery of the associated moment tensor. The objective is that of optimally choosing the location of the sensors (stations) from which to collect the observed data. The Shannon expected information gain is used as the objective function to search for the optimal network of sensors. A closed form for such objective is available due to the linear structure of the forward problem, as well as the Gaussian modeling of the observational errors and prior distribution. The resulting problem being inherently combinatorial, a greedy algorithm is deployed to sequentially select the sensor locations that form the best network for learning the moment tensor. Numerical results are presented to display the optimal network of sensors and how the uncertainty in the inferred seismic moment tensor contracts. The scenario of full three-dimensional velocity models or unknown earthquake source locations is treated as nuisance uncertainty, contributing to the overall uncertainty without being the focus of the inversion. This is addressed using a consensus approach over a set of realizations of the nuisance parameter. We analyzed the resulting network of stations for the moment tensor inversion under model misspecification, which reflects realistic data-generating processes.

A robust decision-making approach for designing coastal groundwater quality monitoring networks.

This paper presents a new approach to the spatiotemporal design of groundwater quality monitoring networks for coastal aquifers. A fusion model combines the outputs of several developed simulation models to make estimates more accurate. A modified GALDIT method is used to incorporate the aquifer vulnerability to saltwater intrusion. The value of information (VOI) theory is applied to determine sufficient monitoring wells. The groundwater quality monitoring network is designed by employing a robust decision-making (RDM) approach under different management strategies and economic considerations. This approach incorporates the deep uncertainties of some critical variables, including water level and total dissolved solids (TDS) concentration at the coastline and pumping flow rates of agricultural wells. The new methodology is implemented in the coastal Qom-Kahak aquifer, Iran. The results illustrate that the combination model has significantly improved evaluation criteria compared to individual prediction models. The fusion model results indicate that thirty monitoring wells would be ideal. The RDM-based analyses in the Qom-Kahak aquifer showed that an optimal network with 30 monitoring wells outperforms the current network regarding various criteria, such as VOI and variance of estimation error. The new well configuration also demonstrates a suitable spatial distribution. Given that the current sampling frequencies are unsuitable for areas with varying vulnerabilities, we recommend sampling every 3months in areas with moderate vulnerabilities and once every three seasons in areas with low vulnerabilities, based on the information transfer index. Finally, a management strategy in which the pumping rate should be less than 60% of the current rate is suggested to prevent saltwater intrusion into the aquifer.