Artificial Neural Network Toolbox Research Articles

Constructal design of a hybrid heat sink with rectangular microchannel and porous fin in a 3D electronic device with artificial neural-network, NSGA-II and different decision-making methods

Constructal theory is used to build a hybrid heat-sink model with rectangular microchannel and porous fin in a 3D electronic device. Firstly, constructal design is conducted with objective of minimizing linear weighted sum of maximum temperature-difference and pump power consumption with fixed total volumes of 3D electronic device, porous fin and microchannel. The optimal microchannel aspect ratio and elemental number are gained. Secondly, multi-objective-optimization is conducted based on NSGA-II, which uses the “gamultiobj” function of Matlab's artificial neural-network toolbox. The findings indicate that when microchannel aspect ratio and elemental number are 0.29 and 38, respectively, complex function achieves its double minimum of 0.810, which is decreased by 19.0% compared to its original value. Therefore, optimizing microchannel aspect ratio and elemental number simultaneously contributes to improving the comprehensive performance of 3D electronic device. Based on LINMAP, TOPSIS and Shannon Entropy decision-making strategies, the multi-objective solution set is compared. The third decision-making method has the smallest deviation index. The corresponding optimal microchannel aspect ratio and elemental number are 0.35 and 32, respectively, which can be used as the optional structure design scheme. The research findings can serve as a new theoretical reference for designing hybrid cooling system in a 3D electronic device.

Artificial Neural Network Approach for Assessing Mechanical Properties and Impact Performance of Natural-Fiber Composites Exposed to UV Radiation.

Amidst escalating environmental concerns, short natural-fiber thermoplastic (SNFT) biocomposites have emerged as sustainable materials for the eco-friendly production of mechanical components. However, their limited durability has prompted research into the experimental evaluation of the deterioration of the mechanical characteristics of SNFT biocomposites, particularly under the influence of ultraviolet rays. However, conducting tests to evaluate the mechanical properties can be time-consuming and expensive. In this study, an artificial neural network (ANN) model was employed to predict the mechanical properties (tensile strength) and the impact performance (resistance and absorbed energy) of polypropylene reinforced with 30 wt.% short flax or wood pine fibers (referred to as PP30-F or PP30-P, respectively). Eight parameters were collected from experimental studies. The ANN input parameters comprised nondestructive test results, including mass, hardness, roughness, and natural frequencies, while the output parameters were the tensile strength, the maximum impact load, and absorbed energy. The model was developed using the ANN toolbox in MATLAB. The linear coefficient of correlation and mean squared error were selected as the metrics for evaluating the performance function and accuracy of the ANN model. They calculate the relationship and the average squared difference between the predicted and actual values. The data analysis conducted by the models demonstrated exceptional predictive capability, achieving an accuracy rate exceeding 96%, which was deemed satisfactory. For both the PP30-F and PP30-P biocomposites, the ANN predictions deviated from the experimental data by 3, 5, and 6% with regard to the impact load, absorbed energy, and tensile strength, respectively.

COMPARATIVE PERFORMANCE ANALYSIS OF ANFIS & ANN ALGORITHMS BASED MPPT ENERGY HARVESTING IN SOLAR PV SYSTEM

This paper presents the development and performance analysis of Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller for a DC to DC converter. The proposed system consists of 2.0 kW PV array, DC to DC boost converter and load. In this research work presents the development and performance analysis of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) based Levenberg-Marquardt (LM), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms are deployed in maximum power point tracking (MPPT) energy harvesting in solar photovoltaic (PV) system to forge a comparative performance analysis of the four different algorithms. A comparative analysis among the algorithms in terms of the performance of handling the trained dataset is presented. The MATLAB/Simulink environment is used to design the maximum power point tracking energy harvesting system and the artificial neural network toolbox is utilized to analyze the developed model. However, considering the dataset training, the correlation between input-output and error, the Levenberg-Marquardt ANFIS algorithm performs better.

A generic model towards maximizing the performance of ship propulsion system using artificial neural network

In this study, optimization based on artificial neural network (ANN) was established to predict the optimal performance parameters of the system, leading to an optimized propulsion system. Optimization related works on propulsion system investigated by researchers are based on specific fuel consumption, emission characteristics, trim and draft optimization using conventional methods amongst others. This creates a gap for a research window in this direction which this work tries to fill by optimizing overall ship propulsion system efficiency using ANN. The models computational development for this research were actualized using ANN tool box in MATLAB. Attention was given to performance parameter that influences the overall performance of the propulsion system. Hence, the overall ship efficiency, η_sp and energy efficiency design index (EEDI) constitute the parameters of interest to be optimized. A comprehensive ANN program code was developed and implemented to create, configure, train and optimize these parameters of interest. Various ANN-based models of a two layered multilayer perceptron (MLP) structure with different configurations were trained and investigated. Results analysis from MATLAB simulation yields η_sp of 0.507398386 and EEDI of 3.490301699 g of CO2/tm. Results show that the MLP configuration of 14-20-7 gives an optimal model for the ANN. The resulting model could predict the optimized performance output of the system with high degree of accuracy with a minimum mean square error (MSE) at 206 epochs. This point gives the lowest MSE performances value of 3.2923e-10 and regression plot between 0.99999 and 1. The percentage parametric optimization of the propulsion system parameters in ANN gives a 2.4% improvement of η_sp and 3.5% improvement of EEDI. These indicate that the use of ANN for parametric optimization of propulsion system is satisfactory

Solar assisted air source heat pump systems for campus water heating in China: Economic optimization of solar fraction design

• TRNSYS software is to simulate the solar assisted air source heat pump system . • The solar fraction design values of solar water heating systems are not economically optimal in China. • Long-term economic optimization of solar fraction design values for solar assisted air source heat pump systems is performed. • An ANN-generated proxy model is to predict the most economical solar fraction design values of solar assisted air source heat pump systems. The solar assisted air source heat pump (SAASHP) system is widely used for campus water heating owing to its cost-effectiveness and cleanliness. In the current design process of the SAASHP system, the solar collector area is determined by the solar fraction ( f ). However, it is difficult to design a cost-optimal f that can comprehensively take the atmospheric factors, device model, electricity price and initial investment into consideration. To address this issue, a SAASHP system that meets the bathing hot water demand of 480 students was established by TRNSYS software, and the cost-optimal solar collector areas and f values for 12 cities across 4 different solar resource regions in China were obtained by the TRNOPT tool of TRNSYS. Moreover, the effects of SC price, electricity price, bathing hot water demand and rated COP of ASHP on cost-optimal f values were investigated by sensitivity analysis. Finally, a proxy model was generated by the artificial neural network toolbox of MATLAB to predict the cost-optimal f values for different scenarios. The results showed that compared to the economically optimized SAASHP system, the designed SAASHP system according to the national design standards for solar heating systems in China has a larger SC area value, leading to an increase in the annual life cycle cost of the system. The difference between the optimized and designed values of solar collector area and f for the SAASHP system was minor in rich resource and sub-rich resource regions but was larger in general resource and poor resource regions. The solar collector price, electricity price, bathing hot water demand and the rated COP of the SAHP were significant factors affecting the annual life cycle cost, the cost-optimal f and solar collector area of the SAASHP system. The proxy model generated by the artificial neural network toolbox of MATLAB can accurately predict the economically optimal solar collector area and f values for SAASHP systems in different cities and system factors, providing a method for the economic design of SAASHP systems in China.

A Comparative Analysis of ANN Algorithms Performance for Maximum Power Point Tracking in Solar Photovoltaic System

Abstract: This paper presents the comparative analysis of Artificial Neural Network (ANN) based algorithms in maximum power point tracking (MPPT) for solar photovoltaic system. The algorithms deployed in this paper are Bayesian Regularization (BR), Levenberg- Marquardt (LM) and Scaled Conjugate Gradient algorithm (SCG).The MPPT model for solar photovoltaic system was designed in MATLAB/Simulink environment and ANN toolbox was used to for analysis. For training 70% data was used and rest 30% was used for validation and testing purpose, which was 15% each. The proposed model was trained seven times for each algorithm and best result was taken. The performance of BR algorithm was better in terms of mean square error which was less than LM algorithm. But with LM algorithm the learning rate, thus time required for training is less so it can be preferred over Time constrained system. SCG algorithm trained the system perfectly with low performance hence it is not suitable for MPPT module. Solar module of 200W with 2 modules in series and 1 module in parallel were taken. The output generated from the trained MPPT solar energy system was 400 W.

Design and Implementation of Organizational Architecture in Organizations in Charge of Combating Smuggling of Goods and Currency with the Aim of Improving the Management of Organizational Networks

In the current situation, one of the concerns in the fight against smuggling of goods and currency is the improvement of the inter-organizational network. The purpose of this research is to design and implement organizational architecture to improve the of organizational networks with SWOT approach, in this area using the artificial neural network toolbox and fuzzy logic in Matlab. This research is applied-modeling in terms of purpose. The statistical population includes expert professors and experts of organizations in charge of combating smuggling of goods and currency. After distributing 100 questionnaires, the sample size of this study is equal to 96 experts who were selected by a combination of two methods of non-probabilistic purposive sampling and snowball sampling. The results show that using the intelligent system, the status of of the organization's network management can be examined numerically and more accurately: In terms of ideal importance, if; The Network Management Based on EA Application Layer status is good, ie exactly 0.813, and Network based on EA data source layer is good, ie exactly 0.824, and Network Management Based on EA Central Component Layer is good, ie exactly 0.819, and Network Management Based on EA Data Preparation Layer is good, ie exactly 0.812, and Network based on EA service quality layer in good condition, ie exactly 0.815;Then; The status of successful implementation of the organization's network management is at the top level (fifth level), ie exactly 0.952.According to the membership functions of language variables by experts, the value of 4.76 within the 5-value range in the range defined for the excellent language variable, ie the success status of the organization's network management, has been calculated exactly 0.952.

Determination of theoretical stress concentration factor for circular/elliptical holes with reinforcement using analytical, finite element method and artificial neural network techniques

Machine parts always work under different types of stresses or loading conditions. The sudden dimensional changes lead to sudden increase in stress on the critical section of the machine parts. This state is defined as stress concentration factor (SCF). The strength of the machine part decreases on the located point at maximum stress. In this study, SCFs of circular/elliptical holes with bead reinforcement in an infinite panel under uniaxial and biaxial stresses were modeled, simulated, calculated and verified using analytical, finite element analysis (FEA) and artificial neural networks (ANN) techniques. This study presents a new model for predicting SCF for panels using artificial intelligence techniques under uniaxial and biaxial loading conditions. Theoretical SCF (Kt) values for different shapes and loading conditions have been compiled and graphical forms of these experimental results were published by Peterson (Peterson’s stress concentration factors, 3rd edn. Wiley, Hoboken, 2007). There is a need to convert these curves into a numerical data set to be used in machine part design easily. Theoretical SCF (Kt) was obtained from the Peterson’s charts in numerical form according to dimensional parameter ratios using high-accuracy graphical computer software. Using these dimensional parameter ratios, a parametric finite element model (FEM) was created. Uniaxial and biaxial boundary conditions were applied to the FEM model. Mesh optimization was accomplished to the parametric FEM model. Mesh formation is compatible with the model according to dimensional sizes and ratios. Mesh optimization was provided to element size and per unit volume. Numerical and FEA results were tested, approved and confirmed with Peterson’s original data using statistical methods. A code was created for the improvement of the ANN model in the Matlab ANN Toolbox Editor. Different ANN model variations were tested and the best-performing ANN model was determined among the tried models. Numerical, FEA and ANN model results were compared and confirmed with one another. The developed model provides an easy method to predict and calculate the stress in defining the Kt according to dimensional ratios and applied loading states (uniaxial/biaxial).

Decision-Making System Based on a Fuzzy Hierarchical Analysis Process and an Artificial Neural Network for Flow Shop Machine Scheduling Model Under Uncertainty

The management of the uncertainty existing in any production system is fundamental to define machine scheduling models that allow programming production instances attached to the real world. In this research, a generalized decision-making system is developed for the management of uncertainty existing in flow shop machine scheduling models. The system assessment the uncertainty existing in internal and external factors that influence the decision-making process of production programming experts, and that is decisive in a final machine scheduling. The system is based on the combination of the Fuzzy Hierarchical Analysis Process, a membership analysis, and an Artificial Neural Network (ANN). The system allows to concentrate the experience of experts in machine scheduling and generalize their knowledge. The efficiency of the system is verified with a Fuzzy Hierarchical Analysis Process Model, the “ANN toolbox” preloaded in MATLAB and variety of structures of an Artificial Neural Network. The results are validated in an industrial application and the system is contrasted against an expert. The results show the efficiency of the system as it defines and predicts the final machine scheduling of production instances; the joint assessment of variables that add uncertainty to the production system allowed to reduce delays in product deliveries.

A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System

In this paper, artificial neural network (ANN) based Levenberg-Marquardt (LM), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms are deployed in maximum power point tracking (MPPT) energy harvesting in solar photovoltaic (PV) system to forge a comparative performance analysis of the three different algorithms. A comparative analysis among the algorithms in terms of the performance of handling the trained dataset is presented. The MATLAB/Simulink environment is used to design the maximum power point tracking energy harvesting system and the artificial neural network toolbox is utilized to analyze the developed model. The proposed model is trained with 1000 dataset of solar irradiance, temperature, and voltages. Seventy percent data is used for training, while 15% data is employed for validation, and 15% data is utilized for testing. The trained datasets error histogram represents zero error in the training, validation, and test phase of data matching. The best validation performance is attained at 1000 epochs with nearly zero mean squared error where the trained data set is converged to the best training results. According to the results, the regression and gradient are 1, 1, 0.99 and 0.000078, 0.0000015739 and 0.26139 for Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient algorithms, respectively. The momentum parameters are 0.0000001 and 50000 for Levenberg-Marquardt and Bayesian Regularization algorithms, respectively, while the Scaled Conjugate Gradient algorithm does not have any momentum parameter. The Scaled Conjugate Gradient algorithm exhibit better performance compared to Levenberg-Marquardt and Bayesian Regularization algorithms. However, considering the dataset training, the correlation between input-output and error, the Levenberg-Marquardt algorithm performs better.

Stochastic One-Step Training for Feedforward Artificial Neural Networks

This paper studies the use and application of a fast method (non-iterative and instantaneous) for Feedforward Neural Networks training in which the weights of the hidden layer are assigned randomly, and the weights of the output layer are trained through a linear regression adjustment. The method solves two of the problems that are present in traditional training: training time and optimal structure. While traditional iterative training methods require long periods to train a single structure, the proposed method allows training a structure in a single step (not iterative). In this way, by scanning the number of neurons in the hidden layer, many structures are trained in a short time, and it is possible to obtain an optimal topology. A quality control criterion of the predictions is proposed based on the coefficient of determination that guarantees short times and an optimal number of hidden neurons to characterize a specific problem. The feasibility of the proposed method is tested by comparing its performance against building functions of the artificial neural networks toolbox in Matlab®, resulting superior in both approximation quality and training time. A rigorous study and analysis are performed for the regression of simulated data on two different surfaces with a specific noise and different topologies of the neural network. The resulting process time is at least 150 times shorter for proposed training than with the iterative training that Matlab uses, thus obtaining well-founded learning rules. A novel way of an amputated matrix is proposed that breaks the paradigm of the way multiple-output systems are trained and improves the quality of predictions with no detriment to training times.

An Integrated System of Artificial Intelligence and Signal Processing Techniques for the Sorting and Grading of Nuts

The existence of conversion industries to sort and grade hazelnuts with modern technology plays a vital role in export. Since most of the hazelnuts produced in Iran are exported to domestic and foreign markets without sorting and grading, it is necessary to have a well-functioning smart system to create added value, reduce waste, increase shelf life, and provide a better product delivery. In this study, a method is introduced to sort and grade hazelnuts by integrating audio signal processing and artificial neural network techniques. A system was designed and developed in which the produced sound, due to the collision of the hazelnut with a steel disk, was taken by the microphone placed under the steel disk and transferred to a PC via a sound card. Then, it was stored and processed by a program written in MATLAB software. A piezoelectric sensor and a circuit were used to eliminate additional ambient noise. The time-domain and wavelet domain features of the data were extracted using MATLAB software and were analyzed using Artificial Neural Network Toolbox. Seventy percent of the extracted data signals were used for training, 15% for validation, and the rest of the data was used to test the artificial neural network (Multilayer Perceptron network with Levenberg-Marquardt Learning algorithm). The model optimization and the number of neurons in the hidden layer were conducted based on mean square error (MSE) and prediction accuracy (PA). A total of 2400 hazelnuts were used to evaluate the system. The optimal neural network structure for sorting and grading hazelnuts was 4-21-3 (four neurons in input layers, 21 neurons in the hidden layer, and three outputs which are the desired classification). This neural network (NN) was used to classify hazelnut as big, small, hollow, or damaged. Results showed 96.1%, 89.3%, and 93.1% accuracy for big/small, hollow, or damaged hazelnuts were obtained, respectively.

Adaptive Control Using NARMA-L2 Model and Electric Circuit for Condenser of Thermal Power Plant

Design and implantation electric circuit for enhancement performance of steam power plant and artificial neural networks technique used to control of condenser. Artificial neural network has been applied to control of the important variables of condenser in Al–Dura power plant in Baghdad such as pressure, temperature. in this study, applied Simulink model in Matlab program (v 2014 a) by using artificial neural network toolbox. the model of condenser of neural network by using NARMA to generated data and train network and using back propagation algorithm for training neural network. The method of control by ANN is off line. the electric circuit of pressure sensor and temperature sensor consist of transformer, dc bridge and voltage regulator. Experimental data of actual power plant obtained from al-dura power plant. comparing results of modelling neural network and electric circuit with experimental data of actual power plant. the results shown the maximum deviation between the ANN and electric circuit with experimental data is less than 4%. artificial neural network can be used in many industrial and engineering application.

Bearing Fault Diagnosis Using Motor Current Signature Analysis and the Artificial Neural Network

Bearings are critical components in rotating machinery. The need for easy and effective bearings fault diagnosis techniques has led to developing different monitoring approaches. In this research, however, a fault diagnosis system for bearings is developed based on the motor current signature analysis (MCSA) technique. Firstly, a test rig was built, and then different bearing faults were simulated and investigated in the test rig. Three current sensors, type SCT013, were interfaced to Arduino MEGA 2560 microcontroller and utilized together for data acquisition. The time-domain signals analysis technique was utilized to extract some characteristic features that are related to the simulated faults. It was noticed that the simulated bearing faults have led to generating vibrations in the induction motors, which in turn cause a change in its magnetic field. For classification (identification) of the extracted features, the artificial neural network (ANN) was employed. An ANN model was developed using the Matlab ANN toolbox to detect the simulated faults and give an indication about the machine health state. The obtained features from the captured motor current signals were utilized for training the ANN model. The results showed the effectiveness of using MCSA based on the time-domain signal analysis in combination with ANN in diagnosis different bearings faults.

Prediction and analysis of College Students’ mental health based on BP neural network

College Students’ mental health is an important part of higher education, so the current research and prediction of College Students’ mental health are of great significance to better solve the problem of College Students’ mental health. Taking a local university as an example, the data from 2011 to 2019 are selected and analyzed. The normalized data processing method is used to assign weights to 11 kinds of factors that affect the health of college students. The training samples of a neural network are selected, and the structural characteristics of the neural network and the artificial neural network toolbox of MATLAB are used to establish the BP based model the mathematical model of the prediction system of College Students’ mental health based on neural network. The results show that the error between the predicted value and the measured value is only 0.88%. On this basis, this paper uses the model to predict the weight of the influencing factors of the mental health status of college students in a local university in 2020 and analyzes the causes of the prediction results, to provide the basis for the current mental health education of college students.

Development of an ANN Model to Simulate the Auxiliary Power Generation from an Irrigation Barrage

The aim of this paper is to correlate the auxiliary power generation from an irrigation reservoir with the power generation of its upstream hydro power project in a cascade reservoir system. System considered for the study is the Pamba Basin- Kakkad Hydro Power Project and Maniyar Reservoir of Pamba Irrigation Project of Kerala State. In a water year from the 1st June to 31st May, irrigation period spans for seven months from November to May in the Irrigation Project. As the water release of the upstream Hydro Electric Plant exceeds the irrigation demand, even during the peak irrigating months of January and February, there is surplus inflow for power generation. A water year is divided to two seasons namely, Irrigation and Non-Irrigation. An Artificial Neural Network (ANN) based model is developed to optimize the power generation using the surplus water and the outputs are compared with the real time data. In the ANN model, the inputs considered are the Generation in units of the upstream Kakkad Hydro Electric Project (Kakkad-HEP), the Rainfall in the catchment and the Irrigation Supply. Spillage from the barrage and the Power Generation in the adjoining Carborandum Universal Madras India Ltd. company’s Small Hydro Electric Project (CUMI-SHEP) is considered as the output. The results obtained shows that the power generation from the ANN model closely matches with the real time data. Analysis is done with the aid of MATLAB ANN tool box.

Automated Valve Fault Detection Based on Acoustic Emission Parameters and Artificial Neural Network

Reciprocating compressor is one of the most popular classes of machines use with wide applications in the industry. However, valve failures in this machine often results unplanned shutdown. Therefore, the effective valve fault detection technique is very necessary to ensure safe operation and to reduce the unplanned shutdown. This paper propose an artificial intelligence (AI) model to detect valve condition in reciprocating compressor based on acoustic emission (AE) parameters measurement and artificial neural network (ANN). A set of experiments were conducted on an industrial reciprocating air compressor with several operational conditions including good valve and faulty valve to acquire AE signal. A fault detection model was then developed from the combination of healthy-faulty data using ANN tool box available in MATLAB. The results of the model validation demonstrated accuracy of valves condition classification exceeding 97%. Eventually, the authors intend to do more efforts for programming this model in smart portable device which can be one of the innovative engineering technologies in the field of machinery condition monitoring in the near future.

Empirical Thresholding and Feature Extraction Based Back Propagation- Artificial Neural Network Model for Fruit Grade

This paper explains a new feature extraction image pre-processing system followed by back propagation- artificial neural networks based system for class categorization of apple fruit images. Scale Conjugate Gradient (SCG) algorithm is used for back propagation. The methodology comprises of three stages in this work. Firstly, various external image based attributes of apple were taken and process in MATLAB. Size and weight features were also considered as important parameters as only color is not sufficient to judge the quality. Secondly, features extraction was done at image pre-processing for making the algorithm lighter by focusing only key features. Support vector machine (SVM) algorithm is also popular for development of relatively light weight classification models. This work uses artificial neural network toolbox in MATLAB for classification. A single hidden layer BP-ANN (Back propagation- artificial neural network) was used with sigmoid activation functions. The result came in terms of suitable output variable which is the quality class of the apple which is chosen A, B, C and D respectively. The modeling result indicates the tremendous match between the data used in training and assumed output values. It also has shorter calculation time due to the SCG algorithm. It is also possible for apple producers and distributors to classify their fruit using this model and reduce the cost by avoiding manual classification.

Meteorolojik Tahmin Destekli Yapay Sinir Ağı ile Gün Öncesi Saatlik Güneş Işınımı Kestirimi: Trabzon ili Örneği

Electricity generation from renewable energy sources is increased day by day. Accurate estimation of electricity generation from the renewable energy sources which have intermittent and variable characteristics is a requirement to ensure stable operation of the electrical grid. In this study, a multi-layer artificial neural network (ANN) system, which is supported by meteorological forecasting data, has been proposed to predict day ahead hourly solar radiation. In this context, the ANN system which operates by based on cause-effect relationship has been designed. In order to increase accuracy of the solar radiation prediction of the designed ANN, a similar day selection algorithm has been developed. A unique ANN has been constituted for each season by evaluating the seasons within itself. The designed ANN model has been designed, trained and tested in MATLAB simulation environment without using codes of the MATLAB ANN toolbox. Day ahead hourly solar radiation of Trabzon province has been predicted by the proposed ANN. The accuracy of the predictions has been evaluated by the mean absolute percentage error (MAPE), the root means squared error (RMSE), the mean absolute error (MAE) and the correlation coefficient (r) performance measures.

Development of a MODIS data based algorithm for retrieving nearshore sea surface salinity along the northern Gulf of Mexico coast

ABSTRACTRemote sensing algorithms for retrieval of Sea Surface Salinity (SSS) were generally developed for deep ocean waters while practical applications of SSS are commonly involved in coastal (particularly nearshore) waters. To fill the gap, this paper presents a new SSS algorithm, called Nearshore SSS Algorithm, developed using the Artificial Neural Networks toolbox in the MATLAB Program and 7 years of cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua data as well as in situ data collected from United States Geological Survey (USGS) stations along the Northern Gulf of Mexico coast. The independent input variables involved in the new algorithm include the water-leaving reflectance at 412 nm, 443 nm, 488 nm, 555 nm, 678 nm, and 645 nm of MODIS Aqua sensor. Results of sensitivity analysis indicate that SSS in nearshore waters is most sensitive to reflectance at 412 nm and 488 nm, followed by the reflectance at 555 nm and 645 nm. While reflectance at 678 nm is also important, reflectance at 443 nm is the least important to SSS in nearshore waters, providing new insights into SSS in coastal waters. MODIS data collected from January 2012 to September 2014 were used for validation of the nearshore SSS algorithm. The linear correlation coefficient of this algorithm for Louisiana coast was 0.7256 and 0.6985 for the development and validation, respectively. A unique feature of this paper is that while the new algorithm is particularly useful to the retrieval of SSS in nearshore waters, it is also expanded to further offshore waters, providing reliable SSS data for various applications of coastal environment and resources management particularly along the northern Gulf of Mexico coast.