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This paper employs machine learning algorithms to detect tax evasion and analyzes tax data. With the development of commercial businesses, traditional algorithms are not appropriate for solving the tax evasion detection problem. Hence, other algorithms with acceptable speed, precision, analysis, and data decisions must be used. In the case of assets and tax assessment, the integration of machine learning models with meta-heuristic algorithms increases accuracy due to optimal parameters. In this paper, intelligent machine learning algorithms are used to solve tax evasion detection. This research uses an improved particle swarm optimization (IPSO) algorithm to improve the multilayer perceptron neural network by finding the optimal weight and improving support vector machine (SVM) classifiers with optimal parameters. The IPSO-MLP and IPSO-SVM models using the IPSO algorithm are used as new models for tax evasion detection. Our proposed system applies the dataset collected from the general administration of tax affairs of West Azerbaijan province of Iran with 1500 samples for the tax evasion detection problem. The evaluations show that the IPSO-MLP model has a higher accuracy rate than the IPSO-SVM model and logistic regression. Moreover, the IPSO-MLP model has higher accuracy than SVM, Naive Bayes, k-nearest neighbor, C5.0 decision tree, and AdaBoost. The accuracy of IPSO-MLP and IPSO-SVM models is 93.68% and 92.24%, respectively. [ABSTRACT FROM AUTHOR]

By accurate predicting of pipe bursts, it is possible to schedule pipe maintenance, rehabilitation and improve level of services in Water Distribution Networks (WDNs). In recent years, artificial intelligence techniques have been used vastly in pipe burst prediction. These techniques need big input dataset for deep learning but big dataset might not be available in old WDNs. This paper aimed to compare the performance of multivariate adaptive regression splines (MARS) and regressive convolution neural network with least square support vector regression (RCNN-LSSVR) for predicting of Pipe Burst Rate (PBR) with limited dataset. Pipes age, diameter, depth of installation, length, average and maximum hydraulic pressure are considered as effective parameters of PBR. Data collected from real-case study in Iran include 158 cases for polyethylene and 124 cases for asbestos cement pipes during 2012–2019. The results indicate that RCNN-LSSVR model has a great performance of PBR prediction on limited dataset. [ABSTRACT FROM AUTHOR]

The preliminary responses on stability assessment are very effective in discontinuous rock slope stabilisations, which geo-mechanical empirical classifications/approaches can be evaluated. Although there are several geomechanical classifications for rock mass characteristics, they still have some complexities and shortcomings. In this paper, correlations based on two flexible classification systems, namely slope mass rating (SMR) and Barton's Qslope, has been discussed as a connection between failure (i.e., safety factor, reliability) and stability conditions (i.e., failure mechanism, support system) which were performed in 300 road/railway slope cases from 12 provinces in Iran. The empirical SMR-Qslope relationship has been used for sedimentary rocks defined lithologically as limestone, marlstone, sandstone, and claystone. The method was used to provide the empirical link to the primary design for discontinuous rock slopes. To this end, after field investigations and the necessary geo-engineering data gathered on the studied slopes. The information used to provide the SMR and Qslope retirements prepare SMR-Qslope relation for Iranian data. The artificial intelligence techniques including k-nearest neighbours, support vector machine, Gaussian process, decision tree, random forest, multilayer perception, and Naïve-Bayes classifiers have been used for detailed classifications. The accurate correlations provided have been implemented and revised in the Python programming language. According to the learning model results, SMR-Qslope equation for Iranian data has been developed as SMR = 6.699 ln (Qslope) + 58.99 / R2 = 0.62 with 0.95 (95%) accuracy. The evaluated results were verified and controlled by Jorda-Bordehore et al. (Stability assessment of rock slopes using empirical approaches: comparison between slope mass rating and Q-slope, 2018) and Maion (Proposta de correlação entre os índices SMR e Q-slope , 2019) empirical relationships as a basic table. By comparing these studies' results, it can be stated that the present study has improved the accuracy of SMR-Qslope relationship. [ABSTRACT FROM AUTHOR]

Groundwater over-abstraction may cause land subsidence (LS), and the LS mapping suffers the subjectivity associated with expert judgment. The paper seeks to reduce the subjectivity associated with the hazard, vulnerability, and risk mapping by formulating an inclusive multiple modeling (IMM), which combines two common approaches of multi-criteria decision-making (MCDM) at Level 1 and artificial intelligence (AI) at Level 2. Fuzzy catastrophe scheme (FCS) is used as MCDM, and support vector machine (SVM) is employed as AI. The developed methodology is applied in Iran's Tasuj plain, which has experienced groundwater depletion. The result highlights hotspots within the study area in terms of hazard, vulnerability, and risk. According to the receiver operating characteristic and the area under curve (AUC), significant signals are identified at both levels; however, IMM increases the modeling performance from Level 1 to Level 2, as a result of its multiple modeling capabilities. In addition, the AUC values indicate that LS in the study area is caused by intrinsic vulnerability rather than man-made hazards. Still, the hazard plays the triggering role in the risk realization. [ABSTRACT FROM AUTHOR]

A scientometric approach is utilized to investigate the dynamic maps of relationships among researchers, institutes, and countries in the field of Artificial Intelligence for Fluid-flow and Heat-transfer (AIFH). The Web of Science database was searched for related publications during the last 40 years (1982 and 2022). A total of 6151 articles were discovered, which were analyzed in detail. Using a bibliometric analysis, the most relevant and most cited sources of publications were identified. The most active researchers, institutions, and countries leading AIFH were reported. Then, the worldwide dynamic collaboration maps and coupling maps of relationships were reported. The Islamic Azad University (1893 T.C.), the Chinese Academy of Sciences (1374 T.C.), and Beihang University (1266 T.C.) were the most influential institutes in AIFH. The most influential countries were China, the USA, and Iran. The dynamic map of collaborations shows a good worldwide collaboration distribution. The USA and China established the most connection with the rest of the world. ANNs are the most studied topic (19.5% of publications), followed by Machine Learning (17.9%) and Neural Networks (15.4%). Support Vector Machines lag behind at 1.4%. ANNs boast the highest total citations (17,064) and H-index (63). Most ANIF papers were published by Medical Physics (119 T.P.). Half of the articles in AIFH were published by five journals of Medical Physics, Neurocomputing, International Journal of Heat and Mass Transfer, International Journal of Radiation Oncology Biology Physics, and IEEE Access. The International Journal of Heat and Mass Transfer received the most citations in AIFH. [ABSTRACT FROM AUTHOR]

By determining the hydraulic flow units (HFUs) in the reservoir rock and examining the distribution of porosity and permeability variables, it is possible to identify areas with suitable reservoir quality. In conventional methods, HFUs are determined using core data. This is while considering the non-continuity of the core data along the well, there is a great uncertainty in generalizing their results to the entire depth of the reservoir. Therefore, using related wireline logs as continuous data and using artificial intelligence methods can be an acceptable alternative. In this study, first, the number of HFUs was determined using conventional methods including Winland R35, flow zone index, discrete rock type and k-means. After that, by using petrophysical logs and using machine learning algorithms including support vector machine (SVM), artificial neural network (ANN), LogitBoost (LB), random forest (RF), and logistic regression (LR), HFUs have been determined. The innovation of this article is the use of different intelligent methods in determining the HFUs and comparing these methods with each other in such a way that instead of using only two parameters of porosity and permeability, different data obtained from wireline logging are used. This increases the accuracy and speed of reaching the solution and is the main application of the methodology introduced in this study. Mentioned algorithms are compared with accuracy, and the results show that SVM, ANN, RF, LB, and LR with 90.46%, 88.12%, 91.87%, 94.84%, and 91.56% accuracy classified the HFUs respectively. [ABSTRACT FROM AUTHOR]

When exposed to water, dispersive (D) soils are eroded and washed away by underground or surface flowing waters. Although soil dispersion is due to its chemical composition, the results of the commonly used chemical method, that is, the Sherard approach, do not match with those of the popular robust Pinhole test. Due to the deficiency of the chemical method, this study aimed to employ artificial intelligence (AI)‐based approaches for predicting the D classification of the soils. To this end, a database containing 321 records of the results of chemical and Pinhole tests on borrow soil samples was collected from various construction sites in Iran. The predictive models for soil dispersion evaluation were developed using the artificial neural network (ANN) and the support vector machine (SVM). The D classification results were presented as output classes versus target classes. Through the comparison of statistical indices, it was found that the results of the proposed models conform to those of the Pinhole test. It was also shown that the ANN model is more accurate than the SVM model for predicting the dispersion potential of the soil. Furthermore, it was indicated that the new models significantly outperform the Sherard method in determining the D classification of the soil. Highlights: Dispersive soils can be classified employing artificial intelligence (AI)‐based methods.AI‐based approaches have superior predictive ability in contrast to traditional approachs.ANN and SVM techniques can accurately identify soil dispersion potential.Higher accuracy of new methods compared to common Sherard approach was validated with Pinhole test results. [ABSTRACT FROM AUTHOR]

Qualitative analysis of water resources is one of the most widely used topics in water resources research today. Researchers use various analysis methods of water parameters to achieve the desired goals in this field. This research uses artificial intelligence (AI), learning machine (LM), data mining, and mathematical techniques to simulate water behavior and estimate its parametric changes. The proposed model used in this study was a Self-adaptive Extreme learning machine (SAELM) to estimate hydrogeological parameters of the Meghan wetland located in Markazi province in Iran. In addition, SAELM simulation results were compared to Least square support vector machine (LSSVM), Multiple linear regression (MLR), and Adaptive Neuro-fuzzy inference system (ANFIS) models. The simulated parameters were Electrical Conductivity (EC), Total Dissolved Solids (TDS), Groundwater Level (GWL), and salinity. This information was related to sampling for 175 months in the study area. Finally, after simulation operation, four models were introduced as superior models. Mentioned exceptional models were SAELM in GWL modeling, SAELM in modeling the EC, MLR in salinity simulation, and LSSVM in the simulation of TDS parameters. Moreover, by five approaches, the models' performance was evaluated. Suggested strategies were performance evaluation by statistical indicators, Wilson score method uncertainty analysis (WSMUA), response & correlation plots, discrepancy ratio charts, and distribution error diagrams. Based on statistical indicators, the SAELMGWL model was the most accurate model with RMSE, MAPE, and R2 indices equal to 0.1496, 0.0043, and 0.9933, respectively. The ANFIS model had the worst results in simulation. [ABSTRACT FROM AUTHOR]

This study performs a GIS-based landslide susceptibility assessment using a convolutional neural network, CNN, in a study area of the Gorzineh-khil region, northeastern Iran. For this assessment, a 15-layered CNN was programmed in the Python high-level language for susceptibility mapping. In this regard, as far as the landside triggering factors are concerned, it was concluded that the geomorphologic/topographic parameters (i.e., slope curvature, topographical elevation, slope aspect, and weathering) and water condition parameters (hydrological gradient, drainage pattern, and flow gradient) are the main triggering factors. These factors provided the landside dataset, which was input to the CNN. We used 80% of the dataset for training and the remaining 20% for testing to prepare the landslide susceptibility map of the study area. In order to cross-validate the resulting map, a loss function, and common classifiers were considered: support vector machines, SVM, k-nearest neighbor, k-NN, and decision tree, DT. An evaluation of the results of the susceptibility assessment revealed that the CNN led the other classes in terms of 79.0% accuracy, 73.0% precision, 75.0% recall, and 77.0% f1-score, and, hence, provided better accuracy and the least computational error when compared to the other models. [ABSTRACT FROM AUTHOR]

Soil water content characteristics included field capacity (FC) and permanent wilting point (PWP) are key soil indicators to study permeability, soil water retention capacity, drainage, irrigation, plant water stress and solute movement. It is necessary to use an appropriate model for predicting these characteristics because of the hardness of their measurement. The present research shows a comparison of intelligent models included ANN, ANFIS, SVM and SVM optimised by firefly and particle swarm meta-heuristic algorithms (SVM-FFA and SVM-PSA) to predict soil water content at −33 and −1500 kPa matric potentials in agricultural soils. Results showed that the SVM-PSA and SVM-FFA hybrid models presented the best performance rather than the others, with predictor variables included geometric mean diameter (dg) of soil particles, bulk density, organic carbon, NDVI and NSMI and the values of R2 and NRMSE in validation dataset, 0.94, 0.081, 0.96 and 0.077 for FC and PWP in SVM-PSA, respectively, and 0.96, 0.079, 0.97 and 0.075 for FC and PWP in SVM-FFA model. Therefore, these intelligent machines are preferable for accurate prediction and could be used to predict soil water content of soils with different soil textures in other regions of Iran or even the world. [ABSTRACT FROM AUTHOR]

Today, various methods have been developed to extract drinking water resources, which scientists use to simulate the quantitative and qualitative water resources parameters. Due to Iran's geographical and climatic characteristics, this region is located on the drought belt in Asia. In this research, some Artificial Intelligence (AI) and mathematical models have been used for groundwater level prediction. The AI models used for this research are Extreme Learning Machine (ELM), Least Square Support Vector Machine (LSSVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR) model. In this study, simultaneously, these models were used to simulate and estimate groundwater level (GWL). The database used in the simulation is the data related to the Total Dissolved Solids (TDS), Electrical Conductivity (EC), Salinity (S), and Time (t) parameters. The results showed that ELM was more accurate than other methods. In Uncertainty Wilson Score Method (UWSM) analysis, ELM had an Underestimation performance and was determined as the more precise model. [ABSTRACT FROM AUTHOR]