A hybrid machine learning optimization algorithm for multivariable pore pressure prediction

Pore pressure is essential data in drilling design,and its accurate prediction is necessary to ensure drilling safety and improve drilling efficiency.Traditional methods for predicting pore pressure are limited when forming particular structures and lithology.In this paper,a machine learning algorithm and effective stress theorem are used to establish the transformation model between rock physical parameters and pore pressure.This study collects data from three wells.Well 1 had 881 data sets for model training,and Wells 2 and 3 had 538 and 464 data sets for model testing.In this paper,support vector machine(SVM),random forest(RF),extreme gradient boosting(XGB),and multilayer perceptron(MLP)are selected as the machine learning algorithms for pore pressure modeling.In addition,this paper uses the grey wolf optimization(GWO)algorithm,particle swarm optimization(PSO)algorithm,sparrow search algorithm(SSA),and bat algorithm(BA)to establish a hybrid machine learning optimization algorithm,and proposes an improved grey wolf optimization(IGWO)algorithm.The IGWO-MLP model obtained the minimum root mean square error(RMSE)by using the 5-fold cross-validation method for the training data.For the pore pressure data in Well 2 and Well 3,the coefficients of determination(R^(2))of SVM,RF,XGB,and MLP are 0.9930 and 0.9446,0.9943 and 0.9472,0.9945 and 0.9488,0.9949 and 0.9574.MLP achieves optimal performance on both training and test data,and the MLP model shows a high degree of generalization.It indicates that the IGWO-MLP is an excellent predictor of pore pressure and can be used to predict pore pressure.

Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems

The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.

Improved reservoir characterization by means of supervised machine learning and model-based seismic impedance inversion in the Penobscot field,Scotian Basin

The present research work attempted to delineate and characterize the reservoir facies from the Dawson Canyon Formation in the Penobscot field,Scotian Basin.An integrated study of instantaneous frequency,P-impedance,volume of clay and neutron-porosity attributes,and structural framework was done to unravel the Late Cretaceous depositional system and reservoir facies distribution patterns within the study area.Fault strikes were found in the EW and NEE-SWW directions indicating the dominant course of tectonic activities during the Late Cretaceous period in the region.P-impedance was estimated using model-based seismic inversion.Petrophysical properties such as the neutron porosity(NPHI)and volume of clay(VCL)were estimated using the multilayer perceptron neural network with high accuracy.Comparatively,a combination of low instantaneous frequency(15-30 Hz),moderate to high impedance(7000-9500 gm/cc*m/s),low neutron porosity(27%-40%)and low volume of clay(40%-60%),suggests fair-to-good sandstone development in the Dawson Canyon Formation.After calibration with the welllog data,it is found that further lowering in these attribute responses signifies the clean sandstone facies possibly containing hydrocarbons.The present study suggests that the shale lithofacies dominates the Late Cretaceous deposition(Dawson Canyon Formation)in the Penobscot field,Scotian Basin.Major faults and overlying shale facies provide structural and stratigraphic seals and act as a suitable hydrocarbon entrapment mechanism in the Dawson Canyon Formation's reservoirs.The present research advocates the integrated analysis of multi-attributes estimated using different methods to minimize the risk involved in hydrocarbon exploration.

Pioneering role of machine learning in unveiling intensive care unitacquired weakness

In the research published in the World Journal of Clinical Cases,Wang and Long conducted a quantitative analysis to delineate the risk factors for intensive care unit-acquired weakness(ICU-AW)utilizing advanced machine learning methodologies.The study employed a multilayer perceptron neural network to accurately predict the incidence of ICU-AW,focusing on critical variables such as ICU stay duration and mechanical ventilation.This research marks a significant advancement in applying machine learning to clinical diagnostics,offering a new paradigm for predictive medicine in critical care.It underscores the importance of integrating artificial intelligence technologies in clinical practice to enhance patient management strategies and calls for interdisciplinary collaboration to drive innovation in healthcare.

People Recognition by RGB and NIR Analysis from Digital Image Database Using Cross-Correlation and Wavelets

This document presents a framework for recognizing people by palm vein distribution analysis using cross-correlation based signatures to obtain descriptors. Haar wavelets are useful in reducing the number of features while maintaining high recognition rates. This experiment achieved 97.5% of individuals classified correctly with two levels of Haar wavelets. This study used twelve-version of RGB and NIR (near infrared) wavelength images per individual. One hundred people were studied;therefore 4,800 instances compose the complete database. A Multilayer Perceptron (MLP) was trained to improve the recognition rate in a k-fold cross-validation test with k = 10. Classification results using MLP neural network were obtained using Weka (open source machine learning software).

A Multilayer Perceptron Artificial Neural Network Study of Fatal Road Traffic Crashes

This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential predictors, including traffic volume, prevailing weather conditions, roadway characteristics and features, drivers’ age and gender, and number of lanes. Based on the output of the model and the variables’ importance factors, seven significant variables are identified and used for further analysis to improve the performance of models. The model is optimized by systematically changing the parameters, including the number of hidden layers and the activation function of both the hidden and output layers. The performances of the MLANN models are evaluated using the percentage of the achieved accuracy, R-squared, and Sum of Square Error (SSE) functions.

基于灰色关联度和Shapley值的区间组合预测模型及其应用

在不确定环境下,区间数是复杂系统一种信息表达形式.论文在单项区间预测的基础上,提出单项预测方法与实际值序列之间的灰色关联度作为精度的度量准则,构建区间型组合预测模型.考虑模型求解的复杂性,从对策论的角度出发,把组合预测方法视为一个合作对策,单项预测方法视为合作对策的局中人,灰色关联度作为度量合作对策的收益函数,按照合作对策中Shapley值法在各单项预测模型中进行分配,从而给出组合预测权系数确定的新思路.为了验证模型的有效性,利用西德克萨斯中质原油现货价格,针对论文提出的组合模型进行比较分析,计算结果验证了论文提出的模型的可行性与有效性.

GMLP-IDS: A Novel Deep Learning-Based Intrusion Detection System for Smart Agriculture

Smart Agriculture,also known as Agricultural 5.0,is expected to be an integral part of our human lives to reduce the cost of agricultural inputs,increasing productivity and improving the quality of the final product.Indeed,the safety and ongoing maintenance of Smart Agriculture from cyber-attacks are vitally important.To provide more comprehensive protection against potential cyber-attacks,this paper proposes a new deep learning-based intrusion detection system for securing Smart Agriculture.The proposed Intrusion Detection System IDS,namely GMLPIDS,combines the feedforward neural network Multilayer Perceptron(MLP)and the Gaussian Mixture Model(GMM)that can better protect the Smart Agriculture system.GMLP-IDS is evaluated with the CIC-DDoS2019 dataset,which contains various Distributed Denial-of-Service(DDoS)attacks.The paper first uses the Pearson’s correlation coefficient approach to determine the correlation between the CIC-DDoS2019 dataset characteristics and their corresponding class labels.Then,the CIC-DDoS2019 dataset is divided randomly into two parts,i.e.,training and testing.75%of the data is used for training,and 25%is employed for testing.The performance of the newly proposed IDS has been compared to the traditional MLP model in terms of accuracy rating,loss rating,recall,and F1 score.Comparisons are handled on both binary and multi-class classification problems.The results revealed that the proposed GMLP-IDS system achieved more than 99.99%detection accuracy and a loss of 0.02%compared to traditional MLP.Furthermore,evaluation performance demonstrates that the proposed approach covers a more comprehensive range of security properties for Smart Agriculture and can be a promising solution for detecting unknown DDoS attacks.

Deep Reinforcement Learning Based Spectrum Prediction for Bursty Bands

Spectrum prediction plays an important role for the secondary user(SU)to utilize the shared spectrum resources.However,currently utilized prediction methods are not well applied to spectrum with high burstiness,as parameters of prediction models cannot be adjusted properly.This paper studies the prediction problem of bursty bands.Specifically,we first collect real Wi Fi transmission data in 2.4GHz Industrial,Scientific,Medical(ISM)band which is considered to have bursty characteristics.Feature analysis of the data indicates that the spectrum occupancy law of the data is time-variant,which suggests that the performance of commonly used single prediction model could be restricted.Considering that the match between diverse spectrum states and multiple prediction models may essentially improve the prediction performance,we then propose a deep-reinforcement learning based multilayer perceptron(DRL-MLP)method to address this matching problem.The state space of the method is composed of feature vectors,and each of the vectors contains multi-dimensional feature values.Meanwhile,the action space consists of several multilayer perceptrons(MLPs)that are trained on the basis of multiple classified data sets.We finally conduct experiments with the collected real data and simulations with generated data to verify the performance of the proposed method.The results demonstrate that the proposed method significantly outperforms the stateof-the-art methods in terms of the prediction accuracy.

Arabic Sign Language Gesture Classification Using Deer Hunting Optimization with Machine Learning Model

Sign language includes the motion of the arms and hands to communicate with people with hearing disabilities.Several models have been available in the literature for sign language detection and classification for enhanced outcomes.But the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural networks.This paper introduces an Arabic Sign Language Gesture Classification using Deer Hunting Optimization with Machine Learning(ASLGC-DHOML)model.The presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language gestures.The presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected network(DenseNet169)model.For gesture recognition and classification,a multilayer perceptron(MLP)classifier is exploited to recognize and classify the existence of sign language gestures.Lastly,the DHO algorithm is utilized for parameter optimization of the MLP model.The experimental results of the ASLGC-DHOML model are tested and the outcomes are inspected under distinct aspects.The comparison analysis highlighted that the ASLGC-DHOML method has resulted in enhanced gesture classification results than other techniques with maximum accuracy of 92.88%.

Deep Learning Approach for Automatic Cardiovascular Disease Prediction Employing ECG Signals

Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed lately.Currently,electrocardiogram(ECG)data is analyzed by medical experts to determine the cardiac abnormality,which is time-consuming.In addition,the diagnosis requires experienced medical experts and is error-prone.However,automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning architectures.This study proposes a simple multilayer perceptron(MLP)model for heart disease prediction to reduce computational complexity.ECG dataset containing averaged signals with window size 10 is used as an input.Several competing deep learning and machine learning models are used for comparison.K-fold cross-validation is used to validate the results.Experimental outcomes reveal that the MLP-based architecture can produce better outcomes than existing approaches with a 94.40%accuracy score.The findings of this study show that the proposed system achieves high performance indicating that it has the potential for deployment in a real-world,practical medical environment.

Improving Performance of Recurrent Neural Networks Using Simulated Annealing for Vertical Wind Speed Estimation

An accurate vertical wind speed(WS)data estimation is required to determine the potential for wind farm installation.In general,the vertical extrapolation of WS at different heights must consider different parameters fromdifferent locations,such as wind shear coefficient,roughness length,and atmospheric conditions.The novelty presented in this article is the introduction of two steps optimization for the Recurrent Neural Networks(RNN)model to estimate WS at different heights using measurements from lower heights.The first optimization of the RNN is performed to minimize a differentiable cost function,namely,mean squared error(MSE),using the Broyden-Fletcher-Goldfarb-Shanno algorithm.Secondly,the RNN is optimized to reduce a non-differentiable cost function using simulated annealing(RNN-SA),namely mean absolute error(MAE).Estimation ofWS vertically at 50 m height is done by training RNN-SA with the actualWS data a 10–40 m heights.The estimatedWS at height of 50 m and the measured WS at 10–40 heights are further used to train RNN-SA to obtain WS at 60 m height.This procedure is repeated continuously until theWS is estimated at a height of 180 m.The RNN-SA performance is compared with the standard RNN,Multilayer Perceptron(MLP),Support Vector Machine(SVM),and state of the art methods like convolutional neural networks(CNN)and long short-term memory(LSTM)networks to extrapolate theWS vertically.The estimated values are also compared with realWS dataset acquired using LiDAR and tested using four error metrics namely,mean squared error(MSE),mean absolute percentage error(MAPE),mean bias error(MBE),and coefficient of determination(R2).The numerical experimental results show that the MSE values between the estimated and actualWS at 180mheight for the RNN-SA,RNN,MLP,and SVM methods are found to be 2.09,2.12,2.37,and 2.63,respectively.

A Novel Approach to Heart Failure Prediction and Classification through Advanced Deep Learning Model

In this study, the author will investigate and utilize advanced machine learning models related to two different methodologies to determine the best and most effective way to predict individuals with heart failure and cardiovascular diseases. The first methodology involves a list of classification machine learning algorithms, and the second methodology involves the use of a deep learning algorithm known as MLP or Multilayer Perceptrons. Globally, hospitals are dealing with cases related to cardiovascular diseases and heart failure as they are major causes of death, not only for overweight individuals but also for those who do not adopt a healthy diet and lifestyle. Often, heart failures and cardiovascular diseases can be caused by many factors, including cardiomyopathy, high blood pressure, coronary heart disease, and heart inflammation [1]. Other factors, such as irregular shocks or stress, can also contribute to heart failure or a heart attack. While these events cannot be predicted, continuous data from patients’ health can help doctors predict heart failure. Therefore, this data-driven research utilizes advanced machine learning and deep learning techniques to better analyze and manipulate the data, providing doctors with informative decision-making tools regarding a person’s likelihood of experiencing heart failure. In this paper, the author employed advanced data preprocessing and cleaning techniques. Additionally, the dataset underwent testing using two different methodologies to determine the most effective machine-learning technique for producing optimal predictions. The first methodology involved employing a list of supervised classification machine learning algorithms, including Naïve Bayes (NB), KNN, logistic regression, and the SVM algorithm. The second methodology utilized a deep learning (DL) algorithm known as Multilayer Perceptrons (MLPs). This algorithm provided the author with the flexibility to experiment with different layer sizes and activation functions, such as ReLU, logistic (sigmoid), and Tanh. Both methodologies produced optimal models with high-level accuracy rates. The first methodology involves a list of supervised machine learning algorithms, including KNN, SVM, Adaboost, Logistic Regression, Naive Bayes, and Decision Tree algorithms. They achieved accuracy rates of 86%, 89%, 89%, 81%, 79%, and 99%, respectively. The author clearly explained that Decision Tree algorithm is not suitable for the dataset at hand due to overfitting issues. Therefore, it was discarded as an optimal model to be used. However, the latter methodology (Neural Network) demonstrated the most stable and optimal accuracy, achieving over 87% accuracy while adapting well to real-life situations and requiring low computing power overall. A performance assessment and evaluation were carried out based on a confusion matrix report to demonstrate feasibility and performance. The author concluded that the performance of the model in real-life situations can advance not only the medical field of science but also mathematical concepts. Additionally, the advanced preprocessing approach behind the model can provide value to the Data Science community. The model can be further developed by employing various optimization techniques to handle even larger datasets related to heart failures. Furthermore, different neural network algorithms can be tested to explore alternative approaches and yield different results.

Use of a Neural Network to Measure the Impact of Social Distribution and Access to Infrastructure on the HDI of the Municipalities of Mexico

The Human Development Index (HDI) was created by the United Nations (UN) and is the basis for many other indicators, as well as being the origin of many public policies worldwide. It is a summary measure of life expectancy, education, and per capita income. These components, in addition to being global measures, show difficulty in being impacted and, with this, advancing in the level of human development. This work shows a model that relates variables of social distribution and access to infrastructure in Mexico, with the HDI. These variables were chosen through a statistical analysis based on a set of indicators measured by the National Institute of Statistics and Geography (INEGI) periodically at the municipal level. The statistical analysis shows that there is no simple correlation between these variables and the HDI, so that a supervised learning model based on a neural network was used, therefore proposing a classification technique based on the distribution of data in the underlying metric space. In addition, an attempt was made to find the simplest possible model to reduce the computational cost and in turn obtain information on the variables with the greatest impact on the HDI, with the aim of facilitating the creation of public policies that impact it.

Wireless location algorithm using digital broadcasting signals based on neural network

In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. By the learning ability of the NN and the closely approximate unknown function to any degree of desired accuracy,the input-output mapping relationship between coordinates and the measurement data of time of arrival （TOA） and time difference of arrival （TDOA） is established. A real-time learning algorithm based on the extended Kalman filter （EKF） is used to train the multilayer perceptron （MLP） network by treating the linkweights of a network as the states of the nonlinear dynamic system. Since the EKF-based learning algorithm approximately gives the minimum variance estimate of the linkweights,the convergence is improved in comparison with the backwards error propagation （BP） algorithm. Numerical results illustrate thatthe proposedalgorithmcanachieve enhanced accuracy,and the performance ofthe algorithmis betterthanthat of the BP-based NN algorithm and the least squares （LS） algorithm in the NLOS environments. Moreover,this location method does not depend on a particular distribution of the NLOS error and does not need line-of-sight （ LOS ） or NLOS identification.

Advance Artificial Intelligence Technique for Designing Double T-Shaped Monopole Antenna

Machine learning(ML)has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design strolls.ML is a massive area within artificial intelligence(AI)that focuses on obtaining valuable information out of data,explaining why ML has often been related to stats and data science.An advanced meta-heuristic optimization algorithm is proposed in this work for the optimization problem of antenna architecture design.The algorithm is designed,depending on the hybrid between the Sine Cosine Algorithm(SCA)and the Grey Wolf Optimizer(GWO),to train neural networkbased Multilayer Perceptron(MLP).The proposed optimization algorithm is a practical,versatile,and trustworthy platform to recognize the design parameters in an optimal way for an endorsement double T-shaped monopole antenna.The proposed algorithm likewise shows a comparative and statistical analysis by different curves in addition to the ANOVA and T-Test.It offers the superiority and validation stability evaluation of the predicted results to verify the procedures’accuracy.

Selection for high quality pepper seeds by machine vision and classifiers

This research aimed to improve selection of pepper seeds for separating high-quality seeds from low-quality seeds. Past research has shown that seed vigor is significantly related to the seed color and size, thus several physical features were identified as candidate predictors of high seed quality. Image recognition software was used to automate recognition of seed feature quality using 400 kernels of pepper cultivar 101. In addition, binary logistic regression and a neural network were applied to determine models with high predictive value of seed germination. Single-kernel germination tests were conducted to validate the predictive value of the identified features. The best predictors of seed vigor were determined by the highest correlation observed between the physical features and the subsequent fresh weight of seedlings that germinated from the 400 seeds. Correlation analysis showed that fresh weight was significantly positively correlated with eight physical features： three color features （R, a＊, brightness）, width, length, projected area, and single-kernel density, and weight. In contrast, fresh weight significantly negatively correlated with the feature of hue. In analyses of two of the highest correlating single features,＇ germination percentage increased from 59.3 to 71.8% when a＊〉3, and selection rate peaked at 57.8%. Germination percentage increased from 59.3 to 79.4%, and the selection rate reached 76.8%, when single-kernel weight 〉0.0064 g. The most effective model was based on a multilayer perceptron （MLP） neural network, consisting of 15 physical traits as variables, and a stability calculated as 99.4%. Germination percentage in a calibration set of seeds was 79.1% and the selection rate was 90.0%. These results indicated that the model was effective in predicting seed germination based on physical features and could be used as a guide for quality control in seed selection. Automated systems based on machine vision and model classifiers can contribute to reducing the costs and labor required in the selection of pepper seeds.

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their performance.Antenna size affects the quality factor and the radiation loss of the antenna.Metamaterial antennas can overcome the limitation of bandwidth for small antennas.Machine learning(ML)model is recently applied to predict antenna parameters.ML can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated antenna.The accuracy of the prediction depends mainly on the selected model.Ensemble models combine two or more base models to produce a better-enhanced model.In this paper,a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial Antenna.Two base models are used namely:Multilayer Perceptron(MLP)and Support Vector Machines(SVM).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the ensemble.Dynamic Group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base models.The proposed model is compared with three based models and the average ensemble model.The results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

Precipitation forecasting by large-scale climate indices and machine learning techniques

Global warming is one of the most complicated challenges of our time causing considerable tension on our societies and on the environment.The impacts of global warming are felt unprecedentedly in a wide variety of ways from shifting weather patterns that threatens food production,to rising sea levels that deteriorates the risk of catastrophic flooding.Among all aspects related to global warming,there is a growing concern on water resource management.This field is targeted at preventing future water crisis threatening human beings.The very first stage in such management is to recognize the prospective climate parameters influencing the future water resource conditions.Numerous prediction models,methods and tools,in this case,have been developed and applied so far.In line with trend,the current study intends to compare three optimization algorithms on the platform of a multilayer perceptron(MLP)network to explore any meaningful connection between large-scale climate indices(LSCIs)and precipitation in the capital of Iran,a country which is located in an arid and semi-arid region and suffers from severe water scarcity caused by mismanagement over years and intensified by global warming.This situation has propelled a great deal of population to immigrate towards more developed cities within the country especially towards Tehran.Therefore,the current and future environmental conditions of this city especially its water supply conditions are of great importance.To tackle this complication an outlook for the future precipitation should be provided and appropriate forecasting trajectories compatible with this region's characteristics should be developed.To this end,the present study investigates three training methods namely backpropagation(BP),genetic algorithms(GAs),and particle swarm optimization(PSO)algorithms on a MLP platform.Two frameworks distinguished by their input compositions are denoted in this study:Concurrent Model Framework(CMF)and Integrated Model Framework(IMF).Through these two frameworks,13 cases are generated:12 cases within CMF,each of which contains all selected LSCIs in the same lead-times,and one case within IMF that is constituted from the combination of the most correlated LSCIs with Tehran precipitation in each lead-time.Following the evaluation of all model performances through related statistical tests,Taylor diagram is implemented to make comparison among the final selected models in all three optimization algorithms,the best of which is found to be MLP-PSO in IMF.

Spatial prediction and modeling of soil salinity using simple cokriging, artificial neural networks, and support vector machines in El Outaya plain, Biskra, southeastern Algeria

Soil salinization is one of the most predominant environmental hazards responsible for agricultural land degradation,especially in the arid and semi-arid regions.An accurate spatial prediction and modeling of soil salinity in agricultural land are so important for farmers and decision-makers to develop the appropriate mechanisms to prevent the loss of fertile soil and increase crop production.El Outaya plain is marked by soil salinity increases due to the excessive use of poor groundwater quality for irrigation.This study aims to compare the performance of simple kriging,cokriging(SCOK),multilayer perceptron neural networks(MLP-NN),and support vector machines(SVM)in the prediction of topsoil and subsoil salinity.The field covariates including geochemical properties of irrigation groundwater and physical properties of soil and environmental covariates including digital elevation model and remote sensing derivatives were used as input candidates to SCOK,MLP-NN,and SVM.The optimal input combination was determined using multiple linear stepwise regression(MLSR).The results revealed that the SCOK using field covariates including water electrical conductivity(ECw)and sand percentage(sand%),and environmental covariates including land surface temperature(LST),topographic wetness index(TWI),and elevation could significantly increase the accuracy of soil salinity spatial prediction.The comparison of the prediction accuracy of the different modeling techniques using the Taylor diagram indicated that MLP-NN using LST,TWI,and elevation as inputs were more accurate in predicting the topsoil salinity[ECs(TS)]with a mean absolute error(MAE)of 0.43,root mean square error(RMSE)of 0.6 and correlation coefficient of 0.946.MLP-NN using ECw and sand%as inputs were more accurate in predicting the subsoil salinity[ECs(SS)]with MAE of 0.38,RMSE of0.6,and R of 0.968.