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This paper extends hybrid-type optimization models of genetic algorithm adaptive network-based fuzzy inference system (GA-ANFIS) for predicting the soil permeability coefficient (SPC) of different types of soil. In these models, GA optimizes parameters of a subtractive clustering technique that controls the structure of the ANFIS model’s fuzzy rule...
Citations
... For more detailed information on new modern techniques, and learning expert systems based on fuzzy logic and neural networks, see [20][21][22][23][24][25][26][27][28][29][30][31][32][33]. ...
This paper presents a model for determining the availability of continuous systems at
open pits using the neuro-fuzzy system. The concept of availability is divided into partial indicators
(synthetic indicators and sub-indicators). The presented model in relation to already existing models
for determining availability uses a combination of the advantages of artificial neural networks and
fuzzy logic. The case study addressed the I ECC (bucket wheel excavator–conveyors–crushing plant)
system of the open pit Drmno, Kostolac. In this paper, in addition to the ANFIS model for determining
the availability of continuous systems, a simulation model was developed. The obtained results of the
ANFIS model were verified with the help of a simulation model that uses certain assumptions about
the distribution of failures. This paper was created as a result of several years of field and theoretical
research into the availability of continuous systems in open pits, and completes a cycle that consists
of several published articles on the subject of modeling the behavior of these systems in real time
using a time picture of the state, expert assessment, simulation and AI models, while respecting the
multidisciplinarity of the problem (mining technological, mechanical, and information technological
aspects). The developed ANFIS model is a key instrument for improving operational efficiency and
resource management in the mining sector. Its ability to accurately predict the availability of the ECC
system brings not only operational benefits through reduced downtime and optimized maintenance,
but also a potential reduction in overall costs at coal open pits. Such an innovative model marks a
significant step forward in the mining industry, especially when it comes to continuous systems in
coal open pits.
... For more detailed information on new modern techniques, and learning expert systems based on fuzzy logic and neural networks, see [20][21][22][23][24][25][26][27][28][29][30][31][32][33]. ...
This paper presents a model for determining the availability of continuous systems at open pits using the neuro-fuzzy system. The concept of availability is divided into partial indicators (synthetic indicators and sub-indicators). The presented model in relation to already existing models for determining availability uses a combination of the advantages of artificial neural networks and fuzzy logic. The case study addressed the I ECC (bucket wheel excavator–conveyors–crushing plant) system of the open pit Drmno, Kostolac. In this paper, in addition to the ANFIS model for determining the availability of continuous systems, a simulation model was developed. The obtained results of the ANFIS model were verified with the help of a simulation model that uses certain assumptions about the distribution of failures. This paper was created as a result of several years of field and theoretical research into the availability of continuous systems in open pits, and completes a cycle that consists of several published articles on the subject of modeling the behavior of these systems in real time using a time picture of the state, expert assessment, simulation and AI models, while respecting the multidisciplinarity of the problem (mining technological, mechanical, and information technological aspects). The developed ANFIS model is a key instrument for improving operational efficiency and resource management in the mining sector. Its ability to accurately predict the availability of the ECC system brings not only operational benefits through reduced downtime and optimized maintenance, but also a potential reduction in overall costs at coal open pits. Such an innovative model marks a significant step forward in the mining industry, especially when it comes to continuous systems in coal open pits.
... The mathematical formulas in each layer were adapted to our study based on Jang (1993) [25]. The parameters {σ, c} in the Gaussian formula (equation (2)) are critical in shaping the membership functions for the input variables (x), representing the standard deviation and center, respectively [26,27]. In our paper, we specified Z, N, and I as the input variables and adapted the Gaussian formula by incorporating these variables as parameters, as shown in equation (3). ...
... To expedite convergence, ANFIS utilizes a hybrid learning algorithm combining gradient descent (GD) and least square (LS) algorithms simultaneously. The LS algorithm estimates consequent parameters as the input signal propagates forward through the layers up to Layer 4. Subsequently, the differences (errors) between the target and network outputs are calculated, and the error rates are backpropagated to estimate the input parameters using the GD algorithm [27]. Further details on the hybrid training algorithm employed in ANFIS can be found in Jang [25]. ...
This study aims to predict the magnetic moments of nuclei with odd-A numbers in a certain region of which magnetic moment has not yet been calculated, using the Adaptive Neuro-Fuzzy Inference System (Anfis) method. In our Anfis model, the proton number (Z), neutron number (N), and spin value (I) are used as inputs for nuclei with 1≤Z≤88. With these input data, 526 odd-A nuclei are trained, and 124 nuclei are tested. The fact that the Anfis model was closer to the experimental data in the training and testing processes than the theoretical methods encouraged us to make inferences about the nuclei of which experimental nuclear magnetic moment is unknown. To that end, inferences have been made for 165 nuclei without experimental magnetic moments in regions 1≤Z≤28. In this region, Na, F, and P isotopes have been specifically studied. Magnetic moment value inferences made for these isotopes using Anfis have also been compared with the theoretical results of the Quasiparticle-Phonon Nuclear Method (QPNM) and with the Shell Model calculations. There is a satisfactory agreement between our predictions and the results of these two theories. Besides, the values obtained from the test and train operations are within the minimum error limits (~%0.03-0.04), the reliability of our system has been proven and a study that has not yet been done in the literature has been conducted. Since the Neuro-Fuzzy system will be a first in the field of nuclear technologies, we believe that the outputs of our study will be a good reference for future studies.
... ANNs are developed based on mathematical models that mimic biological NNs as information processing systems [43,44]. They are the most promising candidates for mimicking the activities and abilities of the human brain and nervous system [2,28,29]. Information processing in an ANN is performed using processors with simple individual neurons. ...
Changes in the pore water pressure of soil are essential factors that affect the movement of structures during and after construction in terms of stability and safety. Soil permeability represents the quantity of water transferred using pore water pressure. However, these changes cannot be easily identified and require considerable time and money. This study predicted and evaluated the soil permeability coefficient using a multiple regression (MR) model, adaptive network-based fuzzy inference system (ANFIS), general deep neural network (DNN) model, and DNN using the dendrite concept (DNN−T, which was proposed in this study). The void ratio, unit weight, and particle size were obtained from 164 undisturbed samples collected from the embankments of reservoirs in South Korea as input variables for the aforementioned models. The data used in this study included seven input variables, and the ratios of the training data to the validation data were randomly extracted, such as 6:4, 7:3, and 8:2, and were used. The analysis results for each model showed a median correlation of r = 0.6 or less and a low model efficiency of Nash–Sutcliffe efficiency (NSE) = 0.35 or less as a result of predicting MR and ANFIS. The DNN and DNN−T both have good performance, with a strong correlation of r = 0.75 or higher. Evidently, the DNN−T performance in terms of r, NSE, and root mean square error (RMSE) improved more than that of the DNN. However, the difference between the mean absolute percent error (MAPE) of DNN−T and the DNN was that the error of the DNN was small (11%). Regarding the ratio of the training data to the verification data, 7:3 and 8:2 showed better results compared to 6:4 for indicators, such as r, NSE, RMSE, and MAPE. We assumed that this phenomenon was caused by the DNN−T thinking layer. This study shows that DNN−T, which changes the structure of the DNN, is an alternative for estimating the soil permeability coefficient in the safety inspection of construction sites and is an excellent methodology that can save time and budget.
... The importance of determining the soil permeability coefficient is widely acknowledged, and is affected by a variety of parameters, including mineralogy, soil density, soil structures, water content, void ratio, and others [1]. Ganjidoost et al. [2] reported that three category factors remarkably affect the soil permeability coefficient, namely, permeable soil parameters (density, clay content, viscosity etc.), inherent soil parameters (Atterberg limits, particle size distribution, etc.), and compacted soil factors (porosity, water content, density, etc.). Most of these factors are closely related to each other. ...
In the design stage of construction projects, determining the soil permeability coefficient is one of the most important steps in assessing groundwater, infiltration, runoff, and drainage. In this study, various kernel-function-based Gaussian process regression models were developed to estimate the soil permeability coefficient, based on six input parameters such as liquid limit, plastic limit, clay content, void ratio, natural water content, and specific density. In this study, a total of 84 soil samples data reported in the literature from the detailed design-stage investigations of the Da Nang–Quang Ngai national road project in Vietnam were used for developing and validating the models. The models’ performance was evaluated and compared using statistical error indicators such as root mean square error and mean absolute error, as well as the determination coefficient and correlation coefficient. The analysis of performance measures demonstrates that the Gaussian process regression model based on Pearson universal kernel achieved comparatively better and reliable results and, thus, should be encouraged in further research.
... Based on the previous studies, the Gaussian type of MFs was chosen [70][71][72][73], and the MFs number was determined by trial and error. The ANFIS configuration parameters and the MFs of input variables were shown in Table S3, and Figure S1, respectively. ...
Photocatalytic degradation is one of the effective methods to remove various pollutants from domestic and industrial effluents. Several operational parameters can affect the efficiency of photocatalytic degradation. Performing experimental methods to obtain the percentage degradation (%degradation) of pollutants in different operating conditions is costly and time-consuming. For this reason, the use of computational models is very useful to present the %degradation in various operating conditions. In our previous work, Fe3O4/TiO2 nanocomposite containing different amounts of silver nanoparticles (Fe3O4/TiO2/Ag) were synthesized, characterized by various analytical techniques and applied to degradation of 2,4-dichlorophenol (2,4-DCP). In this work, a series of models, including stochastic gradient boosting (SGB), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), the improvement of ANFIS with genetic algorithm (GA-ANFIS), and particle swarm optimization (PSO-ANFIS) were developed to estimate the removal percentage of 2,4-DCP. The model inputs comprised of catalyst dosage, radiation time, initial concentration of 2,4-DCP, and various volumes of AgNO3. Evaluating the developed models showed that all models can predict the occurring phenomena with good compatibility, but the PSO-ANFIS and the SGB models gave a high accuracy with the coefficient of determination (R 2) of 0.99. Moreover, the relative contributions, and the relevancy factors of input parameters were evaluated. The catalyst dosage and radiation time had the highest (32.6%), and the lowest (16%) relative contributions on the predicting of removal percentage of 2,4-DCP, respectively.
... One of these efforts was a study to predict river flow with ANFIS (adaptive neuro-fuzzy inference system) and compare it with conventional methods in which ANFIS showed better results than conventional methods for predicting river flow (Nayak et al., 2004). Also, several research studies have been conducted in the field of application of classical intelligent models which were applied to predict water permeability and soil penetration coefficient (Ganjidoost et al., 2015), rainfall-runoff forecast (Tayfur and Singh 2006;Akrami et al., 2014), evaporation and transpiration prediction (Ehteram et al., 2019), canal inlet speed prediction, and spatial distribution of groundwater quality (Khashaei-Siuki and Sarbazi 2013). Predicting the amount of suspended sediment in the river is also one of the fields of interest among scientists to evaluate the performance of intelligent methods, and several studies in this field have been published by them. ...
Predicting the amount of sediment in water resource projects is one of the most important measures to be taken, while sediments have an unknown nature in their behavior. In this research, using the data recorded at the Mazrae station between 2002 and 2013, the amount of sediment in the catchment area of Maku Dam has been predicted using different models of intelligent algorithms. Recorded data including river flow (m³/s), sediment concentration (mg/L), and temperature (°C) were considered input data, and sediment load (ton/day) was considered output data. Initially, using the correlation test, the relationship between each input data with output data was considered. The results show high correlation of sediment concentration data and river flow with sediment load and low correlation of temperature data with these data. In order to find the best combination of data for prediction, the combination of single, binary, and triple data was considered in sensitivity analysis. In order to achieve the purpose of this study, first with the classical adaptive neuro-fuzzy inference system (ANFIS), the amount of sediment load was predicted, and then using evolutionary algorithms in ANFIS training, their performance was examined. The intelligent algorithms used in this study were ant colony optimization extended to continuous domain, particle swarm optimization, differential evolution, and genetic algorithm. The results showed that adaptive neuro-fuzzy inference system–ant colony optimization extended to continuous domain, adaptive neuro-fuzzy inference system–particle swarm optimization, adaptive neuro-fuzzy inference system–genetic algorithm, adaptive neuro-fuzzy inference system–differential evolution, and classical ANFIS had the best performance in predicting the amount of sediment load. In the meantime, it was observed that the coefficient of determination, root mean square error, and scatter index in the test mode for the adaptive neuro-fuzzy inference system–ant colony optimization extended to continuous domain algorithm with the best prediction dataset (sediment concentration + river flow) are equal to 0.991, 13.001, and (ton/day), 0.112, and those for the ANFIS with the weakest prediction (temperature + river flow) are equal to 0.490, 107.383 (ton/day), and 0.929, respectively. The present study showed that the use of intelligent algorithms in ANFIS training has been able to improve its performance in predicting the amount of sediment load in the catchment area of Maku Dam.
... erefore, artificial intelligence (AI) or machine learning (ML) methods have been developed in recent decades to accurately predict the k-value of the soil and to reduce cost and time using limited geotechnical parameters. Such methods include artificial neural network (ANN) [11][12][13][14], adaptive neural fuzzy system (ANFIS) [15,16], and hybrid optimization models of genetic algorithms with adaptive neural fuzzy inference system (GA-ANFIS) [15], support vector machine (SVM), random forest (RF) [12], M5P, and Gaussian process (GP) [17]. ...
... erefore, artificial intelligence (AI) or machine learning (ML) methods have been developed in recent decades to accurately predict the k-value of the soil and to reduce cost and time using limited geotechnical parameters. Such methods include artificial neural network (ANN) [11][12][13][14], adaptive neural fuzzy system (ANFIS) [15,16], and hybrid optimization models of genetic algorithms with adaptive neural fuzzy inference system (GA-ANFIS) [15], support vector machine (SVM), random forest (RF) [12], M5P, and Gaussian process (GP) [17]. ...
The permeability coefficient (k-value) of the soil is an important parameter used in the civil engineering design of roads, tunnels,
dams, and other structures. However, the determination of k-value by experimental methods in the laboratory or the field is still
costly and time-consuming. Moreover, it requires special equipment and special care in the collection of soil samples for
laboratory study. Therefore, in this study, we have proposed machine learning (ML) hybrid model: teaching learning-based
optimization of artificial neural network (TLBO-ANN) to predict the k-value of soil based on limited parameters (natural water
content, void ratio, specific gravity, liquid limit, plastic limit, and clay content) which can be determined easily in the laboratory.
Test results of 84 soil samples obtained from the Da Nang-Quang Ngai expressway project in Vietnam are used in the model
development. Statistical indicators such as correlation coefficient (R), root mean square error (RMSE), and mean absolute error
(MAE) are used to validate and evaluate the accuracy of the model. The results show that the TLBO-ANN model is an effective
tool in predicting correctly the k-value (R = 0.905) of soil for the consideration in the design of structures founded on the soil.
... It is capable of predicting SSC for the daily and monthly duration (Firat and Gongor 2010). ANFIS can also estimate soil permeability coefficient with the help of genetic algorithms (Ganjidoost et al. 2015). Rezaei and Fereydooni (2015) compared ANFIS and ANN model for monthly suspend sediment load prediction and concluded that ANFIS performance is better compared to ANN. ...
Rivers are one of the major sources of water, and they fulfil many of the requirements of humans. Rivers provide water for drinking, irrigation, act as a way for transportation, and many other things. Any change in the water of the river quantitatively or qualitatively affects human life severely. In India, the river Ganga is the most essential and holy river. Sedimentation is the most important factor affecting the flow of any river. Natural forces primarily influence it. However, the growing urbanisation of cities along the river’s banks is also influencing the sediment process. Estimation of daily suspended sediment load is a difficult and extensive procedure that includes inaccuracy too. Nowadays, soft computing techniques are frequently used to predict suspended sediment concentrations (SSC). This study outlines the capabilities of a neural network and an adaptive neuro-fuzzy inference system in estimating the suspended sediment load of the Ganga at Varanasi gauging stations 25° 19′ 29.45″ N 83° 2′ 9.17″ E. The daily data of discharge (Q) in m3/s and SSC in mg/l from January 2010 to December 2012 is used for training of artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) models with the three different sets of combination data. The efficacy of these models is checked based on various statistical parameters. The results show that both ANN and ANFIS models work well in suspended sediment concentration prediction. However, the performance ANN (R2 = 0.939 and RMSE = 166.325) model is slightly better than that of the ANFIS models (R2 = 0.938 and RMSE = 168.542) for a range of studies. Thus, ANN and ANFIS can be successfully applied to predict the concentration of sediment load of the Ganga river for the selected zone. However, a wide range of datasets is required to generalise the present models for better prediction of suspended sediment concentration.
... Regarding the prediction of the soil permeability coefficient, there are several studies using the ML method, for instance, ANN, adaptive neuro-fuzzy system (ANFIS), and hybrid optimization model of genetic algorithm-ANFIS (GA-ANFIS) [1,2,[15][16][17]. Sezer et al. [17] used an ANFIS to estimate the permeability of granular soil; they concluded that the ANFIS algorithm is superior to estimate the permeability of granular soil considering grain-size distribution and particle shape [2]. ...
... Sezer et al. [17] used an ANFIS to estimate the permeability of granular soil; they concluded that the ANFIS algorithm is superior to estimate the permeability of granular soil considering grain-size distribution and particle shape [2]. However, the hybrid model GA-ANFIs outperformed in terms of prediction accuracy compared with single ANN, ANFIS model, and hybrid GA-ANN model [15]. In general, soft computing-based models are great tools for the prediction of the properties of soil. ...
... ANN is known as a common and powerful technique that imitates the activity and performance of the human brain and nervous system [15][16][17]. is technique has many crucial abilities such as generalization and learning from data and can deal with a large variable. It was reported that the major characteristics of ANN comprises continuous nonlinear dynamics, high fault tolerance, collective computation, self-learning, selforganization, and real-time treatment [25]. ...
Determination of the permeability coefficient (K) of soil is considered as one of the essential steps to assess infiltration, runoff, groundwater, and drainage in the design process of the construction projects. In this study, three cost-effective algorithms, namely, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), which are well-known as advanced machine learning techniques, were used to predict the permeability coefficient (K) of soil (10⁻⁹ cm/s), based on a set of simple six input parameters such as natural water content w (%), void ratio (e), specific density (g/cm³), liquid limit (LL) (%), plastic limit (PL) (%), and clay content (%). For this, a total of 84 soil samples data collected from the detailed design stage investigations of Da Nang-Quang Ngai national road project in Vietnam was used to generate training (70%) and testing (30%) datasets for building and validating the models. Statistical error indicators such as RMSE and MAE and correlation coefficient (R) were used to evaluate and compare performance of the models. The results show that all the three models performed well (R > 0.8) for the prediction of permeability coefficient of soil, but the RF model (RMSE = 0.0084, MAE = 0.0049, and R = 0.851) is more efficient compared with the other two models, namely, ANN (RMSE = 0.001, MAE = 0.005, and R = 0.845) and SVM (RMSE = 0.0098, MAE = 0.0064, and R = 0.844). Thus, it can be concluded that the RF model can be used for accurate estimation of the permeability coefficient (K) of the soil.
