Adaptive Neuro-fuzzy Inference System Model Research Articles

Prediction and optimization kerf width in laser beam machining of titanium alloy using genetic algorithm tuned adaptive neuro-fuzzy inference system

Prediction and optimization kerf width in laser beam machining of titanium alloy using genetic algorithm tuned adaptive neuro-fuzzy inference system

Precision in Insurance Forecasting: Enhancing Potential with Ensemble and Combination Models based on the Adaptive Neuro-Fuzzy Inference System in the Egyptian Insurance Industry

ABSTRACT Enhancing the precision of retention ratio predictions holds profound significance for insurance industry decision-makers and those vested in advancing insurance services. Precision helps insurance companies navigate inflationary pressures and evaluate underwriting profitability, enabling reliable prognoses of future underwriting gains. As far as we know, although there have been multiple attempts to construct a predictive model for retention ratio, none of these attempts have used combining models or studied the Egyptian market. Therefore, this study contributes significantly to this developing field by providing combining models, which combined statistical time series models such as Exponential Smoothing (ES), and Autoregressive Integrated Moving Average (ARIMA), with Adaptive Neuro-Fuzzy Inference System (ANFIS). Two different types of combinations are employed with these models. Furthermore, the study introduces three ensemble models designed for the purpose of predicting the retention ratio within the Egyptian insurance market. Dataset was carefully gathered from the EFSA’s annual reports, focused on the property-liability insurance sector within the Egyptian insurance market and covers the time period from 1989 to 2021. Next, the proposed models are assessed employing well-established statistical assessment metrics, namely, Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE), R Square (R2), and Root Mean Square Error (RMSE). The results show that combining and ensemble methods improve predicted accuracy. A multi-linear regression-based ensemble model that combines ARIMA, ES, and ANFIS models outperforms both single and combined models in robustness. The article concludes that the insurance industry can greatly benefit from modern predictive methods to make sound decisions.

Implementation of adaptive neuro-fuzzy inference system in design and process optimization of latent heat storage system

This study focuses on implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS) as an artificial neural network model to optimize latent heat thermal energy storage (LHTES) systems. Experimental data comprising seven input parameters (length, shell diameter, tube diameter, mass flow rate, difference in temperature, latent heat, and density) and two output parameters (charging and partial charging time) are collected and used to train the ANFIS model. Initially, the model is developed using a grid partition method, employing Triangular and Gbell membership functions and assigning diverse membership rules. This initial model demonstrated satisfactory accuracy based on quality metrics (R2, mean absolute, and root mean square error). Then, a subtractive clustering approach is applied to further enhance the model's accuracy, which groups different data clusters together. This enhanced model exhibited superior performance compared to the grid partition method, making it the optimal choice. In addition, an experimentally validated numerical model is developed using COMSOL Multiphysics and is utilized to crosscheck the predicted output of the ANFIS model. From a set of 100 combinations of storage capacity between 0.45 and 0.55 MJ, a fixed latent heat storage capacity of 0.456 MJ was identified by considering the combination with the least charging time based on the input parameters. Using this storage capacity, a minimum charging time of 223 min and a partial charging time of 180 min was determined. The numerical model also confirmed these optimal values.

Developing an Adaptive Neuro-Fuzzy Inference System for Performance Evaluation of Pavement Construction Projects

This study employs an adaptive neuro-fuzzy inference system (ANFIS) to identify critical success factors (CSFs) crucial for the success of pavement construction projects. Challenges such as construction cost delays, budget overruns, disputes, claims, and productivity losses underscore the need for effective project management in pavement projects. In contemporary construction management, additional performance criteria play a vital role in influencing the performance and success of pavement projects during construction operations. This research contributes to the existing body of knowledge by comprehensively identifying a multidimensional set of critical success performance factors that impact pavement and utility project management. A rigorous literature review and consultations with pavement experts identified sixty CSFs, categorized into seven groups. The relative importance of each element and group is determined through the input of 287 pavement construction specialists who participated in an online questionnaire. Subsequently, the collected data undergo thorough checks for normality, dependability, and independence before undergoing analysis using the relative importance index (RII). An ANFIS is developed to quantitatively model critical success factors and assess the implementation performance of construction operations management (COM) in the construction industry, considering aspects such as clustering input/output datasets, fuzziness degree, and optimizing five Gaussian membership functions. The study confirms the significance of three primary CSFs (financial, bureaucratic, and governmental) and communication-related variables through a qualitative structural and behavioral validation process, specifically k-fold cross-validation. The outcomes of this research hold practical implications for the management and assessment of overall performance indices in pavement construction projects. The ANFIS model, validated through robust testing methodologies, provides a valuable tool for industry professionals seeking to enhance the success and efficiency of pavement construction endeavors.

A Sugeno ANFIS Model Based on Fuzzy Factor Analysis for IS/IT Project Portfolio Risk Prediction

Risk inherence jeopardises Information System (IS) and Information Technology (IT) Project Portfolio Management (PPM) to realise thestrategic objectives. Previous studies have mainly provided Artificial Intelligence (AI) and statistical models to predict the overall riskof IS/IT project portfolio, whereas neuro-fuzzy models were rarely used. This paper proposes a Sugeno Adaptive Neuro-Fuzzy InferenceSystem (ANFIS) model based on Fuzzy Factor Analysis (FFA) named ANFIS-OPR to predict the overall risk of IS/IT project portfolio fromhistorical IS/IT project risk data. The ANFIS-OPR inputs are the relevant factor loadings resulting from the FFA application on the IS/IT projects risks set to cope with the curse of dimensionality. Then, the Sugeno ANFIS model is adopted to give strategic interpretability to the predicted IS/IT project portfolio overall risk by implementing the IS/IT Project Management Office (PMO) expert knowledge, represented by fuzzy rules, on the relationship between IS/IT project portfolio strategic alignment and the IS/IT projects risks. The ANFISOPR outputs are the predicted Overall Portfolio Risk (OPR) and Root Mean Square Error (RMSE). The paper also presents an IS/IT PMO case study that shows the proposed ANFIS-OPR efficacy, which predicted the OPR values closely to the OPR estimates with an accepted RMSE of 0.108. The proposed ANFIS-OPR is a novel intelligent decision-making tool that enables the IS/IT PMO to monitor the OPR, considering its linkage with strategic alignment; thus, contingency plans can be carried out appropriately while ensuring that the IS/IT project portfolio is strategically aligned.

Bitcoin Price Prediction with Fuzzy Logic

Due to cryptocurrencies' rising prices, like bitcoin, more and more people are becoming interested in them. Success in this business depends on a good price prediction. Several methods, including heuristic and machine-learning-based ones, can currently estimate the price with varied degrees of success. This study will use the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) model to predict the price's general direction over the next 10 days. Along with popular traders' indicators, the previous day's price will be used. The findings demonstrated that, despite errors, price direction predictions—an increase, a drop, or a stable price—are typically accurate.

An approach for flood flow prediction utilizing new hybrids of ANFIS with several optimization techniques: a case study

Abstract Using machine learning methods is efficient in predicting floods in areas where complete data are not available. Therefore, this study considers the Adaptive Neuro-Fuzzy Inference System (ANFIS) model combined with evolutionary algorithms, namely Harris Hawks Optimization (HHO) and Arithmetic Optimization Algorithm (AOA), to predict the flood of the Shahrchay River in the northwest of Iran. The data used included the daily data of precipitation, evaporation, and runoff in the years 2016 and 2017, where 70% of the data were used for model training and 30% for testing the models. The results showed that although the ANFIS model provided values with high errors in several steps, especially in estimating steps with maximum or minimum values, the use of HHO and AOA resulted in a significant reduction in the error values. The ANFIS–AOA model utilizing an input scenario including the flow in the previous 1–3 days exerted the most promising results in the test data, with the Nash–Sutcliffe efficiency (NSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) of 0.93, 1.34, and 0.69, respectively. According to Taylor's diagram, the ANFIS–AOA hybrid algorithm predicts flood values with greater performance than the other models.

The Effect of Varying Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Parameters on Wind Energy Prediction: A Comparative Study

Owing to the development of technology, the majority of nations throughout the world now rely on fossil fuels and nuclear power plants to meet their energy needs. However, as academic research on this subject has shown, it has become clear that alternative energy uses are necessary due to the gradual depletion of these fuels and their significant negative effects on the environment. In order to ensure energy diversity and end the energy shortage, the development of renewable energy sources is crucial. The prediction of wind power is crucial for effectively utilizing the potential of wind energy. In this study, an adaptive neuro-fuzzy inference system (ANFIS) and an artificial neural network (ANN) have been developed for the prediction of wind power. In this study, data sets were created by taking the daily average wind speeds of the selected wind turbine, the daily average power values it produces, and the daily average wind speed values in the Velimese region. By creating single-hidden layer and multi-hidden layer ANN models, the network was trained multiple times with different activation functions and different numbers of neurons, and wind power prediction was performed. In the ANFIS model, the number of membership functions is kept constant, and wind power prediction is performed using different membership functions. With these ANFIS and ANN models developed with different parameter combinations, it is aimed to determine the most efficient model by performing daily average wind power prediction. Parameter combinations were tested to determine the appropriate models, and as a result, the ANN and ANFIS models were compared with each other.

Conjunctive management of groundwater and surface water resources using a hybrid simulation–optimization method

Abstract This study aimed to determine the optimal conjunctive utilization of groundwater and surface water resources in the Halil River basin, one of the most significant study regions in Kerman Province (Iran). Multi-verse optimizer (MVO) and the ANFIS (adaptive neuro-fuzzy inference systems) simulation model, known as the MVO–ANFIS simulation–optimization model, were used for this purpose. Moreover, the optimal exploitation policy for the studied basin was presented. The ANFIS model yielded a coefficient of determination greater than 0.99 in Baft, Rabor, and Jiroft.This model had a high capability to simulate groundwater levels in these three regions. Therefore, the ANFIS model was adopted as the simulation model to predict the aquifer water table in these regions. Regarding the conjunctive utilization of groundwater and surface water resources, the exploitation policy resulting from the MVO–ANFIS simulation–optimization model had a desirable performance by supplying 91.70, 87.75, and 97.58% of the total demands of Baft, Rabor, and Jiroft, respectively. Moreover, results of water system performance indicators, including reliability (82.96, 72.65, 95.07), resiliency (70, 53.47, 80), vulnerability (29.54, 25.64, 17.02), and sustainability (74.24%, 66.10%, 85.78%) in the mentioned regions, respectively, showed the appropriate performance of the proposed model for the simulation–optimization problem.

A Comprehensive Flood Risk Inundation Mapping and Hybrid Model for Flood Forecasting in the Panam River Basin

The forecasting of flooding plays a pivotal role in the field of hydrology and serves as an essential measure for preventing any possible flood damage. This study presents an analysis of the utilization of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to prevent floods in river basins. ANFIS represents a hybrid approach that combines elements of neural networks and fuzzy logic methodologies for the purpose of analyzing and comparing input and output data. Performance evaluation of ANFIS is based on the ratio of Discrepancy Ratio (D) along with the determination coefficient (R2), Coefficient of correlation (R), and Root Mean Square Error (RMSE). Results: The best ANFIS model was the one that gave the best results during training. The RMSE was 622.43, with a correlation coefficient of 0.93 and an R2 of 0.86. Nevertheless, its validation performance demonstrated slightly elevated RMSE figures and lower correlation and R2 values, with RMSE at 2847.99, R at 0.91, and R2 at 0.83. An Artificial Neural Network (ANN) model with three transfer functions (SIGMOID, HYPERBOLIC TANGENT, and LINEAR) is developed and evaluated based on R = correlation and MSE = mean squared error. Furthermore, the study also entails the development of a flood model for the research area, consisting of 38 cross profiles for a detailed analysis of the river’s profile and flood inundation. This analysis aims to provide precise recommendations for flood protection measures.

Evaluating nano-metal oxide mixed matrix membranes for whey protein separation using hybrid intelligent optimization learning

Ultrafiltration (UF) membranes offer cost-efficient and sustainable techniques for the extraction of whey protein in cheese whey wastewater. This strategy substantially aids in waste minimization and fosters the progression of a circular economy paradigm within the dairy industry. The emerging discipline of machine learning has benefited UF by enhancing the separation performance and deepening the filtration mechanism. In this study, adaptive neuro-fuzzy inference systems (ANFIS) coupled chemometric learning was implemented for the performance evaluation of whey protein recovery in nano metal oxide embedded mixed matrix membranes (MMMs) UF from whey wastewater. The selected input variables are nano metal oxides concentration (NC), pore radius (S), and contact angle (CA). The targeted responses for the parametric dependency analysis are individual whey protein permeability (lysozyme (PLys), lactalbumin (PLA) and bovine serum albumin (PBSA)) and protein rejection (%) (lysozyme (RLys), lactalbumin (RLA) and bovine serum albumin (RBSA)). The Jarque-Bera indicated that the input variables did not deviate from normality, and S and CA have significantly influenced the whey protein rejection and permeability. ANFIS models demonstrate superior predictive capabilities with minimal error for flux under various transmembrane pressure (TMP) in MMMs due to their inherent gradient descent optimization. Among the proteins, a Pearson correlation coefficient approaching unity denotes a significant positive relationship between lysozyme flux (FLys). The ANFIS models exhibited excellent predictive performance in real-time cheese whey effluent flux and rejection data, achieving a mean square error of 0.0003 for experimental flux. This study provides valuable insights for the development of models tailored to small datasets, offering a foundation for predicting and enhancing membrane performance.

Rainfall–runoff modeling using an Adaptive Neuro-Fuzzy Inference System considering soil moisture for the Damanganga basin

Abstract Rainfall is the major component of the hydrologic cycle and it is the primary source of runoff. The main purpose of this study was to estimate daily discharge by employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) model using rainfall and soil moisture data at three different depths (5 cm, 100 cm and bedrock) for the Damanganga basin. The length of the data for the study period 1983–2022 is 39 years. The model employed nine membership functions for each variable of soil moisture, rainfall, discharge and 30 rules were optimized. The results were compared considering a range of model performance indicators as correlation coefficient (R2) and Nash–Sutcliffe efficiency (NSE) coefficient. The model application results shows that soil moisture at bedrock gives more precise value of daily discharge with (R2) and NSE value as 0.9936 and 0.9981, respectively, as compared to the soil moisture at a depth of 5 and 100 cm. The better results obtained for the measurement of soil moisture in the deeper soil layer are consistent with the hydrological behavior anticipated for the analyzed catchment, where the root-zone soil layer is the driver of the runoff response rather than the surface observations. This study can be helpful to hydrologists in selecting appropriate rainfall–runoff models.

Adsorptive uptake of thymol blue from aqueous medium using calcined snail shells: equilibrium, kinetic, thermodynamic, neuro-fuzzy and DFT studies

ABSTRACTS The spontaneous discharge of pollutants or toxicants into the ecosystem as a result of various anthropogenic activities is alarming, this necessitates a drastic cheap, and eco-friendly clean-up approach. Out of all the methods, adsorption has proven to be the most effective. The batch adsorptive removal of Thymol Blue (TB) dye from aqueous solutions is examined in this study utilizing calcined snail shells (CalSS). With the aid of SEM, EDS, XRD, and FTIR, the synthesized adsorbent was examined for different physicochemical characteristics. A machine learning model namely Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed to assess the TB adsorption process while taking into account the important adsorption parameters contact time, temperature, pH, adsorbent dosage, then initial adsorbate concentration. Adsorbent pHpzc was also evaluated. EDX and FTIR confirm the formation of CaO with sharp peaks at 547 cm−1 and C–O and O–H are present. SEM and XRD show an irregularly shaped highly crystalline adsorbent material having a typical particle size of 65 ± 2.81 nm and lattice parameter value of 8.611617 Å. The pHpzc value is 11.04, indicating basic surface characteristics. The pH of 3.0, an adsorbent dose of 10 mg and the highest achievable adsorption efficiency were measured to be 98.75% at 20 °C. The findings from the study fit nicely onto Brouser Sotolongo with q BS = 259.4887 mg/g and R 2 = 0.9888. The pseudosecond order model (PSOM) recorded the least error value of 0.3336 and R 2 = 0.9952. This indicates chemisorption and multilayer adsorption processes. The thermodynamic parameters ΔH° and ΔG° demonstrate the exothermic and spontaneous nature of the adsorption process. The ANFIS model for the TB sequestration was evaluated using relevant statistical metrics, giving Root Mean Square Error (RMSE) value of 1.4644, Mean Absolute Deviation (MAD) value of 0.576, Mean Absolute Error (MAE) value of 1.2974 and Mean Absolute Percentage Error (MAPE) value of 1.5141. This outcome revealed that the ANFIS model and experimental findings are in good agreement. The efficacy of calcined snail shell in removing Thymol Blue dye from aqueous solution was also examined using in-silico technique which was in accordance to the value of the calculated descriptors obtained from the adsorbent as well as the calculated binding affinity. The elimination of TB-polluted wastewater using calcined snail shells is demonstrated in this study to be a successful and environmentally benign process.

Improvement of UAV thrust using the BSO algorithm-based ANFIS model

Abstract Unmanned aerial vehicles (UAVs), which are available in our lives in many areas today, bring with them new expectations and needs along with developing technology. In order to meet these expectations and needs, main subjects such as reducing energy consumption, increasing thrust and endurance, must be taken into account in UAV designs. In this study, Backtracking search optimisation (BSO) algorithm-based adaptive neuro-fuzzy inference system (ANFIS) model is proposed for the first time to improve UAV thrust. For this purpose, first, different batteries and propellers were tested on the thrust measuring device and a data set was obtained. Propeller diameter and pitch, current, voltage and the electronic speed controller (ESC) signal were selected as input, and UAV thrust was selected as output. ANFIS was used to relate input and output parameters that do not have a direct relationship between them. In order to determine the ANFIS parameters at the optimum value, ANFIS was trained with the obtained data set by using BSO algorithm. Then, the objective function based on the optimum ANFIS structure was integrated into BSO algorithm, and the input values that gave the optimum thrust were calculated using BSO algorithm. Simulation results, in which parameters such as engine, battery and propeller affecting the thrust are taken into account equally, emphasise that the proposed method can be used effectively in improving the UAV thrust. This hybrid method, consisting of ANFIS and BSO algorithm, can reduce the cost and time loss in UAV designs and allows many possibilities to be tested.

Machine learning and statistical physics modeling of tetracycline adsorption using activated carbon derived from Cynometra ramiflora fruit biomass

The current investigation reports the usage of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN), the two recognized machine learning techniques in modelling tetracycline (TC) adsorption onto Cynometra ramiflora fruit biomass derived activated carbon (AC). Many characterization methods utilized, confirmed the porous structure of synthesized AC. ANN and ANFIS models utilized pH, dose, initial TC concentration, mixing speed, time duration, and temperature as input parameters, whereas TC removal percentage was designated as the output parameter. The optimized configuration for the ANN model was determined as 6-8-1, while the ANFIS model employed trimf input and linear output membership functions. The obtained results showed a strong correlation, indicated by high R2 values (ANNR2: 0.9939 & ANFISR2: 0.9906) and low RMSE values (ANNRMSE: 0.0393 & ANFISRMSE: 0.0503). Apart from traditional isotherms, the dataset was fitted to statistical physics models wherein, the double-layer with a single energy satisfactorily explained the physisorption mechanism of TC adsorption. The sorption energy was 21.06 kJ/mol, and the number of TC moieties bound per site (n) was found to be 0.42, conclusive of parallel binding of TC molecules to the adsorbent surface. The adsorption capacity at saturation (Qsat) was estimated to be 466.86 mg/g – appreciably more than previously reported values. These findings collectively demonstrate that the AC derived from C. ramiflora fruit holds great potential for efficient removal of TC from a given system, and machine learning approaches can effectively model the adsorption processes.

Evaluation of student failure in higher education by an innovative strategy of fuzzy system combined optimization algorithms and AI

This research suggests two novel metaheuristic algorithms to enhance student performance: Harris Hawk's Optimizer (HHO) and the Earthworm Optimization Algorithm (EWA). In this sense, a series of adaptive neuro-fuzzy inference system (ANFIS) proposed models were trained using these methods. The selection of the best-fit model depends on finding an excellent connection between inputs and output(s) layers in training and testing datasets (e.g., a combination of expert knowledge, experimentation, and validation techniques). The study's primary result is a division of the participants into two performance-based groups (failed and non-failed). The experimental data used to build the models measured fourteen process variables: relocation, gender, age at enrollment, debtor, nationality, educational special needs, current tuition fees, scholarship holder, unemployment, inflation, GDP, application order, day/evening attendance, and admission grade. During the model evaluation, a scoring system was created in addition to using mean absolute error (MAE), mean square error (MSE), and area under the curve (AUC) to assess the efficacy of the utilized approaches. Further research revealed that the HHO-ANFIS is superior to the EWA-ANFIS. With AUC = 0.8004 and 0.7886, MSE of 0.62689 and 0.65598, and MAE of 0.64105 and 0.65746, the failure of the pupils was assessed with the most significant degree of accuracy. The MSE, MAE, and AUC precision indicators showed that the EWA-ANFIS is less accurate, having MSE amounts of 0.71543 and 0.71776, MAE amounts of 0.70819 and 0.71518, and AUC amounts of 0.7565 and 0.758. It was found that the optimization algorithms have a high ability to increase the accuracy and performance of the conventional ANFIS model in predicting students' performance, which can cause changes in the management of the educational system and improve the quality of academic programs.

Ramalan Kestabilan Cerun yang Diperkukuhkan Menggunakan Cerucuk Menggunakan Model Sistem Inferens Neuro-Fuzzy Adaptif (ANFIS)

Predictive analysis using artificial intelligence (AI) has transformed the landscape of forecasting analysis in various research fields. The advancements in AI modelling algorithms have enhanced decision-making, trend identification, and process optimization. In geotechnical engineering, AI assists in predicting soil behaviour, structural stability, and slope stability. The AI model discussed in this paper is the Adaptive Neuro-Fuzzy Inference System (ANFIS). In this study, the ANFIS model predicts slope stability by examining the Factor of Safety (FOS) value. Slope stability analyses reinforced with continuous bored pile walls generated by the numerical computation of the finite element method (FEM) in two dimensions (2D) and three dimensions (3D) are compared with the predictions of the ANFIS model. The numerical FEM computations employ PLAXIS 2D and PLAXIS 3D software. Meanwhile, the ANFIS model is designed within the MATLAB software platform involving 112 data samples. With six input pile parameters and one output, the finding shows that the ANFIS model can learn complex non-linear data and accurately predict the output. This is supported by the R² values of 0.9771 and 0.9965 from comparing the forecasting output with the 2D and 3D FEM outputs, respectively. Meanwhile, the low RMSE values of 0.0187 and 0.0180 each confirm this.

Predicting the precipitated calcium carbonate and unconfined compressive strength of bio-mediated sands through robust hybrid optimization algorithms

Biological soil stabilization through Microbial-Induced Calcite Precipitation (MICP) demonstrates the potential to be an eco-friendly and sustainable approach to enhance the strength properties of cohesionless granular soils. No systematic study has been performed so far in the literature on predicting the strength of bio-mediated sands prior to testing. In this study, to overcome this deficiency, based on a comprehensive set of experimental data compiled from literature, the amounts of Precipitated Calcium Carbonate (PCC) and Unconfined Compressive Strength (UCS) of MICP-treated sands are estimated through hybridizing Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models with metaheuristic algorithms. During the data extraction procedure, a total of 70 and 115 datasets are used to predict the PCC and UCS, respectively. To this end, five key features of urea concentration (Mu), calcium chloride (Mcc), optical density of bacterial suspension (OD600), pH and total volume of injection per volume of sample (Vt), are first selected as the inputs of the hybrid models to estimate the amount of calcium carbonate precipitation within the porous structure of MICP-treated granular medium. The experimental data on the PCC along with the basic characteristics of the host sand including mean grain size (D50), uniformity coefficient (Cu) and void ratio (e), are then adopted as the inputs of the numerical models in the second phase whilst the UCS of bio-mediated sands is set as the output. The performance and accuracy of implemented models are rigorously assessed through analyzing various statistical indices and performance criteria. According to the numerical analysis, the ANN-Particle Swarm Optimization (PSO) and ANFIS-PSO hybrid models show the best performance in predicting PCC and UCS of bio-mediated sands, respectively. Using the well-established Gene Expression Programming (GEP) algorithm, practical correlations are developed to predict the PCC and UCS values of MICP-mediated soils based on the characteristics of biological binding agents as well as the host material. The Machine Learning (ML)-based models presented in this study provide engineers with a fruitful framework for the rough and preliminary estimation of the amount of calcite precipitation as well as the UCS of bio-mediated sands in the field prior to stabilization and performing complementary tests. Nevertheless, it is important to consider the limitations of extrapolation in the models presented in this research.

A fish biodiversity protection based approach for assessing environmental flow regime in rivers

Available environmental flow assessment methods are not able to integrate the impacts of flow regime on the fish biodiversity which means improving these methods is necessary. This study proposes a novel approach to assess environmental flow regime in which the fish biodiversity index is simulated in the structure of the environmental flow assessment for protecting the fish biodiversity values in the case study. Due to considerable impact of water quality parameters as well as physical flow parameters, two combined physical flow and water quality indices were considered as the inputs of simulating fish biodiversity index. Adaptive neuro fuzzy inference system (ANFIS) as well as hydraulic simulation were applied to simulate combined indices of physical flow and water quality. Moreover, a multiple linear regression (MLR) model was utilized for simulating the fish biodiversity index. Due to necessity of simulating natural flow regime in the representative river reach, soil and water assessment tool as a known hydrological tool was applied as well. According to evaluation indices of the case study, ANFIS model as well as SWAT and MLR are reliable to assess environmental flow considering the fish biodiversity. The minimum environmental flow in the study area was assessed 40% of mean annual flow in which the difference between fish biodiversity index between the natural flow and environmental flow is less than 10%. However, significant improvement of water quality is a prerequisite before implementing the proposed environmental flow regime. High computational complexities is one of the weaknesses of the proposed method.

A multi-criteria model of supply chain sustainability assessment and improvement for sugarcane agroindustry

The sustainability of the sugarcane agro-industry supply chain plays a crucial role in providing economic benefits, minimizing social and environmental impacts, and optimizing resource utilization. This research aims to analyze the sustainability performance of the sugarcane agro-industry supply chain using multi-criteria assessment and formulate strategies for sustainability improvement. The study proposes a multi-criteria assessment model with twenty-eight indicators and four dimensions of sustainability: economic, social, environmental, and resources, which were developed based on previous research. The fuzzy inference system (FIS) and multi-dimensional scaling (MDS) methods were utilized to analyze the multi-criteria indicators of sustainability performance in each dimension and overall supply chain. The Adaptive Neuro Fuzzy Inference System (ANFIS) model was used to aggregate multi-dimension sustainability to achieve overall sustainability performance. A fuzzy cognitive map (FCM) framework was developed to formulate strategies for improving the sustainability performance of the supply chain. The research was verified at two sugar mill locations in Java Island, Indonesia. The FIS and MDS models successfully analyzed the sustainability performance of the two sugar agro-industries, showing an average value of “quite sustainable”. The overall sustainability performance using the ANFIS model for mill A and B were 57.2 and 61.9, respectively. Series of FGDs combined with the FCM model successfully formulated five clusters of strategies as initiatives in improving the sustainability performance, namely raw material provision, harvesting and post-harvest activities, production process optimization, IT-based technology implementation, and institutional aspects. This present work seeks to contributes to the development of multi-criteria of sustainability performance for the food industry's supply chain. It also proposes a comprehensive framework for analyzing and improving sustainable supply chain performance under uncertainty using a combination of conventional and fuzzy assessment modeling approach. A practical initiative strategy in sustainability improvement is revealed for the sugarcane agroindustry's supply chain.