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In recent years, new techniques and algorithms such as Artificial Neural Networks (ANNs), Fuzzy Inference Systems (FIS) and Genetic Algorithm (GA) have been used as alternative statistical tools in modeling and forecasting issues. These methods have been extensively used in the field of geosciences and atmospheric physics. The main purpose of this paper is to combine FIS and ANNs for local modeling of the ionosphere Total Electron Content (TEC) in Iran. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed for TEC modeling. Also, Multi-Layer Perceptron ANN (MLP-ANN) and ANN based on Radial Base Functions (RBF) have been designed for analyzing ANFIS results. Observations of 29 Global Positioning System (GPS) stations from the Iranian Permanent GPS Network (IPGN) have been used in 3 different seasons in 2015 and 2016. These stations are located at geomagnetic low latitudes region. Out of these 29 stations, 24 stations for training and 5 stations for testing and validating were selected. The relative and absolute errors have been used to evaluate the accuracy of the proposed model. Also, the results of this paper are compared with the International Reference Ionosphere model (IRI2016). The maximum values of the average relative error for RBF, MLP-ANN, ANFIS and IRI2016 methods are 13.88%, 11.79%, 10.06%, and 18.34%, respectively. Also, the maximum values of the average absolute error for these methods are 2.38, 2.21, 1.5 and 3.36 TECU, respectively. Comparison of diurnal predicted TEC from the ANFIS, RBF, MLP-ANN and IRI2016 models with GPS-TEC revealed that the ANFIS provides more accurate predictions than the other methods in the test area. [ABSTRACT FROM AUTHOR]

• Propose a framework for measuring a maturity level of performance-based budgeting. • Develop a parallel network data envelopment analysis model. • Consider the hierarchical configuration of performance indicators. • Use fuzzy sets theory to deal with vagueness and ambiguity. • Present a case study to demonstrate the applicability of the developed framework. Performance-based budgeting (PBB) aims to formulate and manage public budgetary resources to improve managerial decisions based on actual performance measures of agencies. Although the PBB system has been overwhelmingly applied by various agencies, the progress and maturity of its implementation process are not satisfactory at large. Therefore, it warrants to find, evaluate and improve the performance of organisations in relation to implementing a PBB system. To do so, the composite indicators (CIs) have been proposed to aggregate multiple indicators associated with the PBB system, but their employment is contentious as they often lean on ad-hoc and troublesome assumptions. Data envelopment analysis (DEA) methods as a powerful and established tool help to contend with key limitations of CIs. Although the original DEA method ignores an internal production process, the knowledge of the internal structure of the PBB systems and indicators is of importance to provide further insights when assessing the performance of PBB systems. In this paper, we present a budget assessment framework by breaking a PBB system into two parallel stages including operations performance (OP) and financial performance enhancement (FPE) to open up the black-box structure of the system and consider the indicator hierarchy configuration of each stage. In situations of the hierarchical configuration of indicators, we develop a multilayer parallel network DEA-based CIs model to measure the PBB maturity levels of the system and its stages. It is shown that the discrimination power of the proposed multilayer model is better than the existing models with one layer and in situations of relatively small number of DMUs the model developed in this paper can be a good solution to the dimension reduction of indicators. Moreover, this research leverages fuzzy logic to surmount the subjective information that is often available in collecting indicators of the PBB systems. The major contribution of this research is to examine a case study of a PBB maturity award in Iran, as a developing country with a myriad of financial challenges, to adopt a PBB maturity model as well as point towards the efficacy and applicability of the proposed framework in practice. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • The current paper presents a method for determining land suitability for agricultural assessment in arid regions. • The present research has addressed the fundamental problem of environmental planning, which is soil erosion. • The land suitability evaluations presented in the current work decrease risks and consequently reduce financial losses for spatial and economic capabilities. • Integrating MCE-GIS and fuzzy logic has advanced the conventional map overlay techniques in land use suitability. Abstract The accurate evaluation of ecological resources plays a critical role in optimal planning for spatial development. For its study of an area of about 11.6 Mha, the present research aimed to assess land suitability in arid regions of eastern Iran by employing a fuzzy inference system in a GIS environment. Primary data, such as the physical properties of soil, climatic factors, elevation, land type, vegetation cover, and faults, were investigated and a geomorphologic map was drawn for working units and planning. In addition, a layer of soil erosion potential was extracted according to the CORINE model. As each environmental element poses a different effect on other parameters, the significance and impact of each factor were first prioritized by a multi-criteria decision-making method. Then, a fuzzy inference system was employed to create a land suitability map for the study area. With the Mann-Whitney non-parametric test, the achieved map was adapted to the land reality. Without considering the potential of soil erosion in areas where agricultural and residential land have currently been developing and expanding, the results showed that locational development is possible in 70% of the region (an area of about 8 million hectares). However, when considering the assessment of the soil erosion potential and inappropriate climate and environmental conditions in 81% of the study area, only 10% of the area allows for the development of spatial-economics. Given that the purpose of this research is founded on risk management, the results can be considered by developers for future ventures. [ABSTRACT FROM AUTHOR]

Using the balanced scorecard approach based on sustainable development parameters is a powerful and useful methodology to evaluate the sustainable performance of organization or company. In this paper, a new approach based on sustainability balanced scorecard (SBSC) and multi criteria decision making (MCDM) approaches is developed for evaluating the performance of oil producing companies in Iran. For reflecting the interdependent relationships among factors influencing the problem under consideration, analytical network process (ANP), a branch of the MCDM techniques, is employed. However, using the ANP method for calculating the preference ratings of alternatives is a time-consuming and bothersome process; therefore, COPRAS (COmplex PRoportional ASsessment) technique is adopted to prioritize the feasible alternatives in terms of linguistic variables. Based on this study, the results demonstrate the effectiveness of the proposed model. The performance evaluation model proposed by using a combination of the MCDM methods and the SBSC approach helps authorities to make an attempt for achieving a competitive advantage. [ABSTRACT FROM AUTHOR]

This work focuses on the problem of constructing accurate prediction models using an adaptive neuro-fuzzy inference system (ANFIS) from data that are scarce and poorly scaled. Many real-world problems have limited data to work with and yet require models with high prediction accuracy. Due to the fuzzy nature of many data sets, it is appropriate to construct prediction models using fuzzy inference systems (FIS), rather than traditional artificial neural network (ANN) models. However, the scarcity of the data raises serious concerns when attempting to construct models of this type. In this paper, we address the problem of constructing accurate prediction models using ANFIS in circumstances where capturing large amounts of data is difficult. We propose a new approach, using robust data expansion methods to enhance the data carefully. This approach provides the learning algorithm with enough representative data to allow all of the parameters (premise and consequent) to be optimised. We compare and contrast the effectiveness in reducing prediction error for three specific expansion models using radial basis function (RBF) kernels. When expanding data, for which the underlying smoothness properties are not known, there is often a danger of over-smoothing. This is usually noticeable at turning points and can occur when fitting the data using polynomials or cubic splines for example. To avoid over-smoothing, we introduce a modified multiquadric approach that allows smooth, near-interpolation while maintaining the desirable shape-preserving properties of the data. The proposed method is explained through two case studies — the first to predict gasoline consumption for the oil/fuel industry in Iraq and the second for predicting natural gas consumption in Iran. This is the first study that uses adjustable, shape-preserving re-sampling methods for optimising ANFIS models in low data environments. • ANFIS networks capture complexities not easily found using traditional ANNs. • Model complexity versus data sample size is a significant practical problem. • Data augmentation methods can be constructed using robust continuous models. • Multiquadric RBFs can be easily tuned to provide robust, accurate data expansion. • Complex models can be built with confidence using reliable data expansion. [ABSTRACT FROM AUTHOR]

Abstract: New information technologies and emerging business forces have triggered a new wave of financial innovation–electronic banking (e-banking). Several studies indicate that online bankers are the most profitable and wealthiest segment to banks. On this basis, no bank today can underestimate the power of the online channel. In Iran e-banking, although proven to be a successful innovation, has still not become adopted by the laggards, and hence, has not met all the expectations of banks and customers. There exist multiple organizational, macro environmental and banking industry specific reasons for this, that we use 3D e-readiness concepts for these reasons. Those factors can move/inhibit e-banking development, but the importance of them is not same. Some of them require more attention from banks and policy makers. Therefore, the purpose of this paper is to priority of factors that impact e-banking development in Iran. The special interest is to rating factors that influence e-banking development in Iran. In order to achieve the objective, 30 Iranian bank managers and expert were interviewed using the Standard AHP questionnaire. The findings indicate that “industry e-readiness” is the most important attribute of the e-banking development in Iran. [Copyright &y& Elsevier]

Abstract: The well-known fuzzy rule-based Takagi–Sugeno–Kang (TSK) model is combined with a set of fuzzy regressions (FR) to investigate the impact of the climate change on the electricity consumption duration. The electricity demand forecasts in the short-terms have a vital application in electricity markets. Knowing that the energy is a product of the relation between the climate change and the average consumption duration of the peak load. The paper introduces a type III TSK fuzzy inference machine combined with a set of linear and nonlinear fuzzy regressors in the consequent part to model effects of the climate change on the electricity demand. However, a simplified version of the model is applied to daily data of the average temperature in Tehran, 2004. First, based on an initially fitted nonlinear curve, an optimization model is employed to cluster data into three groups of cold, temperate and hot. The fuzzy data have been expanded to reduce the temperature volatile property. Then the relation is estimated by the fuzzy regressions in company with the TSK model. Numerical results show high efficiency of the proposed combined fuzzy model, as well as a minor decrease in the average absolute error. [Copyright &y& Elsevier]

Network data envelopment analysis (NDEA) is capable of considering operations and interdependence of a system's component processes to measure efficiencies. There are numerous performance evaluation applications in which some indicators have hierarchical structures with a considerable number of sub-indicators. This problem of ignoring the hierarchical structure of indicators weakens the discrimination power of NDEA models and may result in inaccurate efficiency scores. In this paper we propose a generalized fuzzy Multiple-Layer NDEA (GFML-NDEA) model and GFML-NDEA-based composite indicators (GFML-NDEA-CI) to incorporate the hierarchical structures of indicators in the ambit of the particular two-stage NDEA models. To demonstrate the usefulness of the GFML-NDEA-CI model proposed, its application was tested by evaluating the efficiency of the performance-based budgeting (PBB) system in 14 governmental agencies in Iran. The comparative analysis results obtained from the GFML-NDEA-CI (multi-layer) model with those from the single-layer fuzzy NDEA-CI model indicate that the number of efficient decision-making units (DMUs) in the one-layer model is eight, whereas it is solely one DMU in the multi-layer model. The discrimination power of the multi-layer model proposed is significantly increased by observing that standard deviation of efficiency scores are increased by 41%, 61%, and 84% for possibility levels 0, 0.5, and 1, respectively. This is obtained while reducing information entropy, thus suggesting that the proposed model yields more reliable scores. • We propose a generalized multilayer two-stage network DEA model. • Fuzzy set theory is used to represent the vagueness and ambiguity in the data. • We propose a generalized fuzzy multiple layer two-stage DEA based-CI model. • A case study demonstrates the applicability of the model. [ABSTRACT FROM AUTHOR]