River Flow Estimation and Forecasting by Using Two Different Adaptive Neuro-Fuzzy Approaches

This paper demonstrates the application of two different adaptive neuro-fuzzy (ANFIS) techniques for the estimation of monthly streamflows. In the first part of the study, two different ANFIS models, namely ANFIS with grid partition (ANFIS-GP) and ANFIS with sub clustering (ANFIS-SC), were used in one-month ahead streamflow forecasting and the results were evaluated. Monthly flow data from two stations, the Besiri Station on the Garzan Stream and the Baykan Station on the Bitlis Stream in the Firat-Dicle Basin of Turkey were used in the study. The effect of periodicity on the model’s forecasting performance was also investigated. In the second part of the study, the performance of the ANFIS techniques was tested for streamflow estimation using data from the nearby river. The results indicated that the performance of the ANFIS-SC model was slightly better than the ANFIS-GP model in streamflow forecasting.     

Estimation of Daily Pan Evaporation Using Two Different Adaptive Neuro-Fuzzy Computing Techniques

This paper investigates the ability of two different adaptive neuro-fuzzy inference systems (ANFIS) including grid partitioning (GP) and subtractive clustering (SC), in modeling daily pan evaporation (E_pan). The daily climatic variables, air temperature, wind speed, solar radiation and relative humidity of two automated weather stations, San Francisco and San Diego, in California State are used for pan evaporation estimation. The results of ANFIS-GP and ANFIS-SC models are compared with multivariate non-linear regression (MNLR), artificial neural network (ANN), Stephens-Stewart (SS) and Penman models. Determination coefficient (R²), root mean square error (RMSE) and mean absolute relative error (MARE) are used to evaluate the performance of the applied models. Comparison of results indicates that both ANFIS-GP and ANFIS-SC are superior to the MNLR, ANN, SS and Penman in modeling E_pan. The results also show that the difference between the performances of ANFIS-GP and ANFIS-SC is not significant in evaporation estimation. It is found that two different ANFIS models could be employed successfully in modeling evaporation from available climatic data.     

Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques

This study examines and compares the performance of four new attractive artificial intelligence techniques including artificial neural network (ANN), hybrid wavelet-artificial neural network (WANN), Genetic expression programming (GEP), and hybrid wavelet-genetic expression programming (WGEP) for daily mean streamflow prediction of perennial and non-perennial rivers located in semi-arid region of Zagros mountains in Iran. For this purpose, data of daily mean streamflow of the Behesht-Abad (perennial) and Joneghan (non-perennial) rivers as well as precipitation information of 17 meteorological stations for the period 1999–2008 were used. Coefficient of determination (R²) and root mean square error (RMSE) were used for evaluating the applicability of developed models. This study showed that although the GEP model was the most accurate in predicting peak flows, but in overall among the four mentioned models in both perennial and non-perennial rivers, WANN had the best performance. Among input patterns, flow based and coupled precipitation-flow based patterns with negligible difference to each other were determined to be the best patterns. Also this study confirmed that combining wavelet method with ANN and GEP and developing WANN and WGEP methods results in improving the performance of ANN and GEP models.     

Forced convection heat transfer inside an anisotropic porous channel with oblique principal axes: Effect of viscous dissipation

An analytical study on laminar and fully developed forced convection heat transfer in a parallel-plate horizontal channel filled with an anisotropic permeability porous medium is performed. The principal axis of the anisotropic porous medium is oriented from 0 to 90 degrees. A constant heat flux is applied on the outer wall of the channel. Both clear (Newtonian) fluid and Darcy viscous dissipations are considered in the energy equation. Directional permeability ratio parameter A¹ is defined to combine both the effect of the dimensionless permeability ratio parameter K¹=(K₁/K₂) and orientation angle φ into one parameter. The effects of the parameter A¹, the Darcy number Da and the modified Brinkman number Br¹ on the heat transfer and fluid flow characteristics in the channels are investigated and presented in graphs. The obtained results show that the parameters A¹, Da and Br¹ have strong effects on the dimensionless normalized velocity and temperature profiles as well as on the Nusselt number. It is found that for a particular value of A¹, called as critical value A_cr¹, the external heat applied to the surface of the channel is balanced by the internal heat generation due to viscous dissipation and the bulk mean temperature approaches the wall temperature. Hence, the Nusselt number approaches infinity for the critical values A_cr¹.     

An Adaline based arcing fault detection algorithm for single-pole autoreclosers

In this paper, a new Adaline based adaptive single-pole autorecloser algorithm is proposed to discriminate permanent and transient faults in HV transmission lines. The proposed algorithm is implemented by processing only terminal voltages and also used to estimate secondary arc extinction time. The algorithm is simulationally analyzed using ATP version of EMTP by varying fault locations and pre fault loading conditions to demonstrate the capabilities and limitations of the method. In addition to that, measured data, which are taken from an actual power system, are also used for testing the algorithm. Results show that the method can successfully be implemented for real time application and computationally less expensive when compared with other methods.     

Development of an urban health and wellbeing index for work precincts: A comparative study in Sydney,Australia

Work precincts are recognized for their significant role as generators of employment and associated commerce within urban areas. This study describes a method for analyzing the physical characteristics of urban work precincts in promoting the health and wellbeing of their occupants. The following physical parameters are analyzed: public transport accessibility, green and blue spaces, food environments, fitness facilities, supermarkets, and grocery stores. The parameters are assessed using quantitative spatial analysis based on street network data, as well as point of interest data acquired from OpenStreetMap (OSM). The streets and their intersections are stored in the OSM database as links and nodes, respectively. The evaluation of the performance metrics involves measuring the street network distance from each node to the closest node of interest for each parameter. The metrics are then combined, forming an urban health and wellbeing index (UHWI), which can be used to compare the performance of different precincts. The method was tested by investigating four work precincts in Sydney, Australia, all hosting a large office building belonging to the same business institution. Our results identified two of the four precincts with a high UHWI and resulted in the identification of one underperforming precinct.     

Damage detection in Timoshenko beam structures by multilayer perceptron and radial basis function networks

This study investigates the efficiency of artificial neural networks (ANNs) in health monitoring of pristine and damaged beam-like structures. Beam modeling is based on Timoshenko theory. Two commonly used network models, multilayer perceptron (MLP) and radial basis neural network (RBNN), are used. Beam material and geometrical properties, beam end conditions and dynamically obtained data are used as input to the neural networks. The combinations of these parameters yield umpteenth input data. Therefore, to examine the effectiveness of ANNs, the frequency of intact beams is first tried to be determined by the network models, given the material and geometrical characteristics of beam elements and support conditions. The methodology to compute the vibrational data utilized in training the networks is provided. Showing the robustness of network models, the second stage of the study is carried out. At this stage, the crack parameters (e.g. the location and severity of crack) are estimated by the ANNs using the beam properties, beam end conditions and vibrational data, which consist of natural frequencies and mode shape rotation values. Despite the multiplexed input data, no data reduction schemes or multistage computations are executed in training and validation of neural network models. As a result of analysis runs, the optimal MLP and RBNN models are determined. Comparison of these models shows that the optimal RBNN algorithm performs better. The effectiveness of optimal ANN models in the presence of noise is also presented. As a conclusion, the trained network can be used as a diagnosis method in structural health monitoring of beam-like structures.     

Rule-based inference and decomposition for distributed in-network processing in wireless sensor networks

Wireless sensor networks are application specific and necessitate the development of specific network and information processing architectures that can meet the requirements of the applications involved. A common type of application for wireless sensor networks is the event-driven reactive application, which requires reactive actions to be taken in response to events. In such applications, the interest is in the higher-level information described by complex event patterns, not in the raw sensory data of individual nodes. Although the central processing of information produces the most accurate results, it is not an energy-efficient method because it requires a continuous flow of raw sensor readings over the network. As communication operations are the most expensive in terms of energy usage, the distributed processing of information is indispensable for viable deployments of applications in wireless sensor networks. This method not only helps in reducing the total amount of packets transmitted in the network and the total energy consumed by the sensor nodes, but also produces scalable and fault-tolerant networks. For this purpose, we present two schemes that distribute information processing to appropriate nodes in the network. These schemes use reactive rules, which express relations between event patterns and actions, in order to capture reactive behavior. We also share the results of the performance of our algorithms and the simulations based on our approach that show the success of our methods in decreasing network traffic while still realizing the desired functionality.     

Optimization of energy management and conversion in the water systems based on evolutionary algorithms

Neural Computing and Applications - In this article, an application of weed optimization algorithm (WOA) for reservoir operation was proposed. In addition, genetic algorithm (GA) and particle swarm...