River flow estimation using adaptive neuro fuzzy inference system

River flow is an essential component of the global water cycle and arguably the best monitored variable in land hydrology. Both anthropogenic climate change as well as direct human influences on the terrestrial environment influence river flow at local to planetary scales. Here, we survey recent scientific advances in quantifying past trends and projecting future changes in global river flow, with focus on trends in flow volumes and seasonality. Previously published evidence of past changes is complemented by an analysis of changes in river flow using in-situ observations and river discharge estimates from a global re-analysis product. Available literature on future projections is accompanied by an analysis of Global Climate Model projections routed through the global river network.Systematic patterns of past changes in river flow emerge at the regional to global scales, despite significant uncertainties and small-scale spatial variability. These uncertainties are related e.g. to differences in study periods, considered indicators of change, availability of in-situ observations, and uncertainties of model-based reconstructions. Some of the most pronounced changes in past river flow include increasing river flow in the northern high latitudes and robust decreasing trends in significant parts of central and south America, the Mediterranean region, the southern tip of Africa as well as central and southern Australia. In other world regions, available in-situ observations are sparse or there is conflicting evidence, meaning that trends are still uncertain. We further show systematic shifts in the river flow seasonality with a tendency for earlier streamflow and a dampened seasonal cycle in across many parts of the world likely to due to higher temperatures and earlier snowmelt, with later streamflow in the Mediterranean linked to changes in rainfall.Climate model driven assessments of future changes in river flow suggest that total global discharge to the oceans may increase with global warming, albeit with large regional differences in the direction of change and substantial model uncertainty. Across several studies based on different model ensembles, there is consensus for increasing river flow in the northern high latitudes and some evidence for increasing river flow in tropical Africa, the Indian subcontinent and eastern and tropical Africa. Finally, we highlight regions in which past changes in river flow are consistent with future projections, which include but are not limited to increasing river flow in northern North America and northern Europe, as well as drying tendencies in the Mediterranean, the southern tip of Africa and Southern Australia. The consistency between model projections and historical trends in these regions gives confidence to future water management decisions, while disparities between historical trends and projections highlights regions where better understanding of the processes governing past and future change in river flow will be required moving forward.

This paper introduces a new technique for efficient control of traffic flow in congested area. It is done through an optimal prediction of traffic flow, which is an important issue in intelligent transportation systems (ITS). However, the traffic flow is affected by manifold factors including nonlinear behavior of motorists. Therefore, it can be defined as an intensely non-linear time-variant system. As a practical case, the traffic system on a 5-street intersection in the city of Mashhad, Iran is tested. In this work, an innovative input selection adaptive network-based fuzzy inference system (ANFIS) approach is proposed which is an added- value to the ANFIS prediction system used in this work. ANFIS is a fuzzy system that possesses the self-training neural-network adaptive capability which has been served as an optimal controller proven by many applications. The flexibility of welcoming additional number of parameters plus with training process made ANFIS a good choice for various non-linear applications. The proposed technique has been modeled in MATLAB software and obtained results exhibit that the ANFIS model with the novel effective input determination technique represented promising outcomes. The results were compared with the classical model to support the proposed objectives.

Computational intelligence (CI) tools have been successfully applied in different fields with superior performances. Neuro-fuzzy system (NFS) is one the approach which combines the benefits of two powerful CI tools known as artificial neural networks (ANN) and fuzzy logic. Although NFS has attracted researchers in many areas of study, few of its applications have been undertaken in hydrological modeling. Adaptive Network-based Fuzzy Inference System (ANFIS) is so far the most established NFS technique and this study is an application of ANFIS in river stage prediction by using rainfall and stage antecedents as inputs in the tropical catchment of Bekok River in Malaysia. To evaluate the performance of the ANFIS model, it was compared with a traditional modeling technique known as autoregressive model with exogenous inputs (ARX). The results of this study were evaluated based on several statistical measures such as coefficient of efficiency (CE), coefficient of determination (r2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results showed that ANFIS can successfully predict the river stage and it outperforms ARX model significantly. ANFIS was also found better in estimating peak river stages comparing to ARX model. This study demonstrates the auspicious potential of ANFIS in river stage modeling.

One of the most important physical properties of the soil is its mechanical strength. Increasing soil mechanical strength will lead to increments in the draft, fuel consumption, work duration and equipment wear, but will follow the reduction in the root growth. Awareness of the soil cone index (as a criterion of the arable soil compaction) in order to production management in connection with the soil physical properties have high importance, especially in precision farming. On the other hand, finding the methods and models that be able to create the best function or model to estimate the soil cone index at the least cost and use of available data are crucial for researchers. The aim of this study was predicting arable soils cone index values by effective parameters on the soil cone index, including bulk density, soil moisture content and soil electrical conductivity by using Fuzzy and neuro-fuzzy systems. In this study, for measurement and determination of the influencing factors on the soil cone index value, the experimental design was the factorial experiment based on a randomized complete block design with five replications. The experimental field had three types of loam, sandy loam and loamy sand soils. The modeling of soil cone index was performed by using effective parameters such as bulk density, moisture content and soil electrical conductivity in the fuzzy and adaptive neuro-fuzzy inference system (ANFIS) systems. For fuzzification of input and output parameters, the linguistic variables, including very low (VL), low (L), medium (M), high (H) and very high (VH) were used. Since determining the type and number of membership functions was conducted experimentally, the triangular membership function for both input and output variables was used due to the high accuracy and convenience in system design. In ANFIS model, 80% and 20% of total data were considered as training and test data, respectively. The numbers of membership functions were selected 5 for each input parameters. ANFIS training was done by the hybrid method. The average of coefficients of determination (R2) were obtained 80.1% and 97.9% for the fuzzy and ANFIS models, respectively. The obtained model through ANFIS presented the high accuracy of 2.54% rather than the fuzzy models with the accuracy of 9.68%. Also in comparison with regression models, ANFIS model has high accuracy and can be used to estimate the soil cone index in agricultural land.

Lean Six Sigma (LSS) is a manufacturing strategy that aims at improving the firm's competitiveness and operational performance through waste reduction and process variation. Sustainable manufacturing aims at creating manufactured products with minimal negative environmental impacts. In order to facilitate zero defects and improve their sustainable performance, manufacturing firms have started to adopt both LSS and sustainable manufacturing strategies to attain integrated benefits. This article reports a research carried out to analyse the performance of LSS system integrated with sustainability considerations using multi grade fuzzy (MGF) and adaptive neuro fuzzy inference system (ANFIS) approaches. During this research, a performance assessment model was designed. The score based on MGF approach is found to be 6.74 and that of ANFIS approach is 6.51. Based on computation, the case organisation was found to possess 'strong LSS performance with sustainability considerations'. The study could facilitate improvement in LSS performance incorporated with sustainability aspects.

ERP projects’ failing to meet user expectations is a serious problem. This research develops an Adaptive Neuro Fuzzy Inference System (ANFIS) model, to predict the key ERP outcome “User Satisfaction” using causal factors present during an implementation as predictors. Data for training and testing the models was from a cross section of firms that had implemented ERPs. ANFIS is compared with other prediction techniques, ANN and MLRA. The results establish that ANFIS is able to predict outcome well with an error (RMSE) of 0.277 and outperforms ANN and MLRA with errors of 0.85 and 0.86 respectively. This study is expected to provide guidelines to managers and academia to predict ERP outcomes ex ante, and thereby enable corrective actions to redirect ailing projects.

In this paper, a hybrid meta-heuristic artificial intelligence (AI) method, named “ANFIS–PSOGA,” is developed for modeling the discharge coefficient of circular side orifices for the first time. For this purpose, the adaptive neuro fuzzy inference system (ANFIS) network is optimized by means of two robust optimization algorithms: the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm. Furthermore, the Monte Carlo simulations are utilized to enhance the performance of the AI hybrid model. Besides, the k-fold cross-validation method is employed in order to verify the numerical results. Initially, the performance of three fuzzy inference system (FIS) generations of the ANFIS model including grid partitioning, subtractive clustering and fuzzy C-means clustering are evaluated. After that, using the parameters affecting the discharge coefficient, eleven ANFIS models are defined and then the best ANFIS model and the most influencing input parameters are identified through the conduction of a sensitivity analysis. For the superior ANFIS model, mean absolute percentage error, root-mean-square error and the correlation coefficient (R) are obtained 0.021, 0.020 and 0.871, respectively. Also, the Froude number (Fr) is detected as the most effective input parameter. Moreover, the discharge coefficient of the circular side orifices and the flow field within main channels along the side orifices are simulated through the adoption of a computational fluid dynamics (CFD) model. Additionally, the flow field at three different cross sections (beginning, middle and end of orifice) along the side weir is evaluated. For instance, by advancing the flow toward the side orifice, the Froude number is reduced. Besides, at each cross section, the maximum shear stress is estimated to occur in the vicinity of the crest of the circular side orifice. According to the CFD results, the maximum lateral velocity is estimated near the circular side orifice. In addition, the results yielded by the ANFIS, ANFIS–PSO, ANFIS–PSOGA and CFD models are compared and an uncertainty analysis is carried out for the models. Based on the comparison, ANFIS–PSOGA has the best performance in simulating the discharge coefficient. Ultimately, a simple and practical computer code for the best model (ANFIS–PSOGA) is proposed which can be easily used by scholars and engineers in practical usages.

In order to make system reliable, it should inhibit guarantee for basic service, data flow, composition of services, and the complete workflow. In service-oriented architecture (SOA), the entire software system consists of an interacting group of autonomous services. Some soft computing approaches have been developed for estimating the reliability of service oriented systems (SOSs). Still much more research is expected to estimate reliability in a better way. In this paper, we proposed SoS reliability based on an adaptive neuro fuzzy inference system (ANFIS) approach. We estimated the reliability based on some defined parameter. Moreover, we compared its performance with a plain FIS (fuzzy inference system) for similar data sets and found the proposed approach gives better reliability estimation.

Adaptive network based fuzzy inference system (ANFIS) is an intelligent neuro-fuzzy technique used for modeling and control of ill-defined and uncertain systems. ANFIS is based on the input-output data pairs of the system under consideration. The size of the input-output data set is very crucial when the data available is very less and the generation of data is a costly affair. Under such circumstances, optimization in the number of data used for learning is of prime concern. In this paper we have proposed an ANFIS based system modeling where the number of data pairs employed for training is minimized by application of an technique called the V-Fold technique. Our proposed method is experimentally validated by applying it to two separate sets of data obtained from the benchmark Box and Jenkins gas furnace data set and the thermal power plant of the NEEPCO (North Eastern Electrical Power Corporation Limited) . By employing our proposed method the number of data required for learning in the ANFIS network could be significantly reduced to around one-eighth of the requirement for the conventional ANFIS method. This result in the saving of the computation time as well as computation complexity is remarkably reduced. The results obtained by applying our proposed method are compared with those obtained by using conventional ANFIS network. It was found that our model compares favorably well with conventional ANFIS model.

Computer numerically controlled (CNC) milling has been one of the most commonly used manufacturing processes for the performance of multiple operations, from tiny integrated circuits to heavy-duty mining machine gearboxes. It is a well-known machining process that offers close tolerances and repeated operations. However, the choice of machining parameters to achieve a desired part’s surface roughness (SR) remains a challenge. In the present study, artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) approaches have been used to predict and monitor the surface roughness of aluminum Al6061 machined blocks. Furthermore, both models have been hybridized with genetic algorithm (GA) and particle swarm optimization (PSO) to investigate the potential enhancement in the prediction performance of the hybrid approach. The results show that factors such as the population size, the acceleration values, the choice of membership functions, and the number of neurons and layers significantly influence the prediction performance of the proposed models. Through a parametric analysis, this study demonstrates how the configuration of the models could affect the prediction performance. While exhibiting the impact of models’ hyperparameter combination on the prediction ability, this study provides insight into the development of suitable prediction models and the potential of soft computing techniques to predict the surface roughness of aluminum Al6061 blocks on CNC machines.

An effectiveness model for an indirect evaporative cooling (IEC) system: Comparison of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and fuzzy inference system (FIS) approach

This study presents an adaptive neuro-fuzzy inference system (ANFIS) approach performed to estimate the number of adverse events where the dependent variables are adverse events leading to four types of variables: number of people killed, wounded, hijacked and total number of adverse events. Fourteen infrastructure development projects were selected based on allocated budgets values at different time periods, population density, and previous month adverse event numbers selected as independent variables. Firstly, number of independent variables was reduced by using ANFIS input selection approach. Then, several ANFIS models were performed and investigated for Afghanistan and the whole country divided into seven regions for analysis purposes. Performances of models were assessed and compared based on the mean absolute errors. The difference between observed and estimated value was also calculated within \({\pm }1\) range with values around 90 %. We included multiple linear regression (MLR) model results to assess the predictive power of the ANFIS approach, in comparison to a traditional statistical approach. When the model accuracy was calculated according to the performance metrics, ANFIS showed greater predictive accuracy than MLR analysis, as indicated by experimental results. As a result of this study, we conclude that ANFIS is able to estimate the occurrence of adverse events according to economical infrastructure development project data.

This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) for tracking SEDC motor speed in order to optimize the parameters of the transient speed response by finding out the perfect training data provider for the ANFIS. The controller was adjusted using PI, PD and PIPD to generate data sets to configure the ANFIS rules. The performance of the ANFIS controllers using these the different data sets was investigated. The efficiencies of the three controllers were compared to each other, where the PI, PD, and PIPD configurations were replaced by ANFIS to enhance the dynamic action of the controller. The performance of the proposed configurations was tested under different operating situations. Matlab's Simulink toolbox was used to implement the designed controllers. The resultant responses proved that the ANFIS based on the PIPD dataset performed better than the ANFIS based on the PI and PD data sets. Moreover, the suggested controller showed a rapid dynamic response and delivered better performance under various operating conditions.

Adaptive network-based fuzzy inference system analysis of mixed convection in a two-sided lid-driven cavity filled with a nanofluid

The energy and draft requirements of a disk plow have been recognized as essential factors when attempting to correctly match it with tractor power. This study examines the possible of using an adaptive neuro-fuzzy inference system (ANFIS) approach and its performance compared to a multiple linear regression (MLR) model to determine the energy and draft requirements of a disk plow. A total of 133 data patterns were obtained by conducting experiments in the field and from the literature. Of these 133 data points, 121 were arbitrarily selected and used for training, and the remaining 12 were used for testing the models. The input variables were plowing depth, plowing speed, soil texture index, initial soil moisture content, initial soil bulk density, disk diameter, disk angle, and disk tilt angle, and output variable was draft of the disk plow. Four membership functions were used with ANFIS: a triangular membership function, generalized bell-shaped membership function, trapezoidal membership function, and Gaussian curve membership function. An evaluation of the outcomes of the ANFIS and MLR modeling shows that the triangular membership function performed better than the other functions. When the ANFIS model draft predictions were compared to the measured values, the average relative error was –1.97%. A comparison of the ANFIS model with other approaches showed that the energy and draft requirements of the disk plow could be estimated with satisfactory accuracy. Keywords: ANFIS, MLR, disk plow, draft, tillage DOI: 10.25165/j.ijabe.20201302.4077 Citation: Al-Dosary N M N, Al-Hamed S A, Aboukarima A M. Application of adaptive neuro-fuzzy inference system to predict draft and energy requirements of a disk plow. Int J Agric & Biol Eng, 2020; 13(2): 198–207.