Shervin Motamedi

Soft computing approaches for forecasting reference evapotranspiration

The GP, SVM-FFA, ANN and SVM-Wavelet modeling of ET0 was reported.SVM-Wavelet had the smallest RMSE of 0.233mmday-1 in testing phase.The ANN model had the largest RMSE of 0.450mmday-1.SVM-Wavelet model was found to perform better than the GP, SVM-FFA and ANN models. Accurate estimation of reference evapotranspiration (ET0) is needed for planning and managing water resources and agricultural production. The FAO-56 Penman-Monteith equation is used to determinate ET0 based on the data collected during the period 1980-2010 in Serbia. In order to forecast ET0, four soft computing methods were analyzed: genetic programming (GP), support vector machine-firefly algorithm (SVM-FFA), artificial neural network (ANN), and support vector machine-wavelet (SVM-Wavelet). The reliability of these computational models was analyzed based on simulation results and using five statistical tests including Pearson correlation coefficient, coefficient of determination, root-mean-square error, absolute percentage error, and mean absolute error. The end-point result indicates that SVM-Wavelet is the best methodology for ET0 prediction, whereas SVM-Wavelet and SVM-FFA models have higher correlation coefficient as compared to ANN and GP computational methods.

Determination of the most influential weather parameters on reference evapotranspiration by adaptive neuro-fuzzy methodology

The monthly ET0 data were obtained by the Penman-Monteith method.ANFIS was applied for selection of the most influential ET0 parameters.Tmin, ea and sunshine hours are the most influential for ET0 estimation.Variables selection with ANFIS improves ET0 predictive accuracies.The ANFIS model can be used for ET0 estimation with high reliability. The adaptive neuro-fuzzy inference system (ANFIS) is applied for selection of the most influential reference evapotranspiration (ET0) parameters. This procedure is typically called variable selection. It is identical to finding a subset of the full set of recorded variables that illustrates good predictive abilities. The full weather datasets for seven meteorological parameters were obtained from twelve weather stations in Serbia during the period 1980-2010. The monthly ET0 data are obtained by the Penman-Monteith method, which is proposed by Food and Agriculture Organization of the United Nations as the standard method for the estimation of ET0. As the performance evaluation criteria of the ANFIS models the following statistical indicators were used: the root mean squared error (RMSE), Pearson correlation coefficient (r) and coefficient of determination (R2). Sunshine hours are the most influential single parameter for ET0 estimation (RMSE=0.4398mm/day). The obtained results indicate that among the input variables sunshine hours, actual vapor pressure and minimum air temperature, are the most influential for ET0 estimation. The maximum relative humidity and maximum air temperature are the most influential optimal combination of two parameters (RMSE=0.2583mm/day).

Extreme learning machine based prediction of daily dew point temperature

An ELM-based model is proposed to predict daily dew point temperature.Weather data for two Iranian stations with different climate conditions were used.ELM model enjoys much greater predictions capability than SVM and ANN.Application of the proposed ELM model would be highly promising and appealing. The dew point temperature is a significant element particularly required in various hydrological, climatological and agronomical related researches. This study proposes an extreme learning machine (ELM)-based model for prediction of daily dew point temperature. As case studies, daily averaged measured weather data collected for two Iranian stations of Bandar Abass and Tabass, which enjoy different climate conditions, were used. The merit of the ELM model is evaluated against support vector machine (SVM) and artificial neural network (ANN) techniques. The findings from this research work demonstrate that the proposed ELM model enjoys much greater prediction capability than the SVM and ANN models so that it is capable of predicting daily dew point temperature with very favorable accuracy. For Tabass station, the mean absolute bias error (MABE), root mean square error (RMSE) and correlation coefficient (R) achieved for the ELM model are 0.3240?C, 0.5662?C and 0.9933, respectively, while for the SVM model the values are 0.7561?C, 1.0086?C and 0.9784, respectively and for the ANN model are 1.0324?C, 1.2589?C and 0.9663, respectively. For Bandar Abass station, the MABE, RMSE and R for the ELM model are 0.5203?C, 0.6709?C and 0.9877, respectively whereas for the SVM model the values are 1.0413?C, 1.2105?C and 0.9733, and for the ANN model are 1.3205?C, 1.5530?C and 0.9617, respectively. The study results convincingly advocate that ELM can be employed as an efficient method to predict daily dew point temperature with much higher precision than the SVM and ANN techniques.

A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm

Forecasting lake level at various horizons is reported here.SVM coupled with FA was used to forecast lake level.Results demonstrate the SVM-FA superiority. Forecasting lake level at various horizons is a critical issue in navigation, water resource planning and catchment management. In this article, multistep ahead predictive models of predicting daily lake levels for three prediction horizons were created. The models were developed using a novel method based on support vector machine (SVM) coupled with firefly algorithm (FA). The FA was applied to estimate the optimal SVM parameters. Daily water-level data from Urmia Lake in northwestern Iran were used to train, test and validate the used technique. The prediction results of the SVM-FA models were compared to the genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results showed that an improvement in the predictive accuracy and capability of generalization can be achieved by the SVM-FA approach in comparison to the GP and ANN in 1 day ahead lake level forecast. Moreover, the findings indicated that the developed SVM-FA models can be used with confidence for further work on formulating a novel model of predictive strategy for lake level prediction.

Adaptive neuro-fuzzy methodology for noise assessment of wind turbine.

Wind turbine noise is one of the major obstacles for the widespread use of wind energy. Noise tone can greatly increase the annoyance factor and the negative impact on human health. Noise annoyance caused by wind turbines has become an emerging problem in recent years, due to the rapid increase in number of wind turbines, triggered by sustainable energy goals set forward at the national and international level. Up to now, not all aspects of the generation, propagation and perception of wind turbine noise are well understood. For a modern large wind turbine, aerodynamic noise from the blades is generally considered to be the dominant noise source, provided that mechanical noise is adequately eliminated. The sources of aerodynamic noise can be divided into tonal noise, inflow turbulence noise, and airfoil self-noise. Many analytical and experimental acoustical studies performed the wind turbines. Since the wind turbine noise level analyzing by numerical methods or computational fluid dynamics (CFD) could be very challenging and time consuming, soft computing techniques are preferred. To estimate noise level of wind turbine, this paper constructed a process which simulates the wind turbine noise levels in regard to wind speed and sound frequency with adaptive neuro-fuzzy inference system (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

Modelling thermal comfort of visitors at urban squares in hot and arid climate using NN-ARX soft computing method

Visitors utilize the urban space based on their thermal perception and thermal environment. The thermal adaptation engages the user’s behavioural, physiological and psychological aspects. These aspects play critical roles in user’s ability to assess the thermal environments. Previous studies have rarely addressed the effects of identified factors such as gender, age and locality on outdoor thermal comfort, particularly in hot, dry climate. This study investigated the thermal comfort of visitors at two city squares in Iran based on their demographics as well as the role of thermal environment. Assessing the thermal comfort required taking physical measurement and questionnaire survey. In this study, a non-linear model known as the neural network autoregressive with exogenous input (NN-ARX) was employed. Five indices of physiological equivalent temperature (PET), predicted mean vote (PMV), standard effective temperature (SET), thermal sensation votes (TSVs) and mean radiant temperature (Tmrt) were trained and tested using the NN-ARX. Then, the results were compared to the artificial neural network (ANN) and the adaptive neuro-fuzzy inference system (ANFIS). The findings showed the superiority of the NN-ARX over the ANN and the ANFIS. For the NN-ARX model, the statistical indicators of the root mean square error (RMSE) and the mean absolute error (MAE) were 0.53 and 0.36 for the PET, 1.28 and 0.71 for the PMV, 2.59 and 1.99 for the SET, 0.29 and 0.08 for the TSV and finally 0.19 and 0.04 for the Tmrt.

Prediction of ultrasonic pulse velocity for enhanced peat bricks using adaptive neuro-fuzzy methodology

Ultrasonic pulse velocity is affected by defects in material structure. This study applied soft computing techniques to predict the ultrasonic pulse velocity for various peats and cement content mixtures for several curing periods. First, this investigation constructed a process to simulate the ultrasonic pulse velocity with adaptive neuro-fuzzy inference system. Then, an ANFIS network with neurons was developed. The input and output layers consisted of four and one neurons, respectively. The four inputs were cement, peat, sand content (%) and curing period (days). The simulation results showed efficient performance of the proposed system. The ANFIS and experimental results were compared through the coefficient of determination and root-mean-square error. In conclusion, use of ANFIS network enhances prediction and generation of strength. The simulation results confirmed the effectiveness of the suggested strategies.

Utilization of geotextile tube for sandy and muddy coastal management: a review.

Threats to beaches have accelerated the coastal destruction. In recent decades, geotextile tubes were used around the world to prevent coastal erosion, to encourage beach nourishment, and to assist mangrove rehabilitation. However, the applications of geotextile tube in sandy and muddy coasts have different concerns as the geological settings are different. Applications of geotextile tubes in sandy beaches were mainly to prevent coastline from further erosion and to nourish the beach. However, for the muddy coasts, mangrove rehabilitation and conservation were additional concerns in coastal management schemes. The mangrove forests are natural barriers which can be found on the muddy coasts of many tropical countries. In this paper, the viability of geotextile tubes in sandy and muddy beaches was analysed. The advantages and disadvantages of the utilization of geotextile tubes in coastal management were discussed based on the experiences from the tropical countries such as Mexico, Malaysia, and Thailand. From the case studies, impressive improvements in coastal restoration after installation of geotextile tubes were shown. Based on the discussion, several recommendations to improve the application of geotextile tubes were suggested in this paper.

A comparative study for estimation of wave height using traditional and hybrid soft-computing methods

The present study developed a wave height prediction model by the recorded climatic data. We used 1-year buoy data for training and testing the developed soft-computing model. Models were developed using a novel method based on the Support Vector Machine (SVM) coupled with the Firefly Algorithm (FFA). This research work used the FFA for estimating the optimum parameters. In addition, this work compared the predicted results of SVM-FFA model to the artificial neural networks (ANNs) and genetic programming (GP). The results indicate that the SVM-FFA approach attains an improvement in capability of generalization and predictive accuracy in comparison to the GP and ANN. A thorough statistical analysis was conducted to compare the predictions of three models i.e., among the SVM-FFA, ANN, and GP. A high R2 value of 0.979 was obtained for the SVM-FFA predictions. Further, the ANN and GP results showed R2 values of 0.524 and 0.525, respectively. Moreover, achieved results indicate that the developed SVM-FFA model can be used with confidence for future research works on formulating novel models for predictive strategy on wave height. The results also show that the new algorithm can learn thousands of times faster than the former popular learning algorithms. This study finds that the application of SVM-FFA is the likely alternative method for estimating the wave height.

Long-Term Assessment of an Innovative Mangrove Rehabilitation Project: Case Study on Carey Island, Malaysia

Wave energy and storm surges threaten coastal ecology and nearshore infrastructures. Although coastal structures are conventionally constructed to dampen the wave energy, they introduce tremendous damage to the ecology of the coast. To minimize environmental impact, ecofriendly coastal protection schemes should be introduced. In this paper, we discuss an example of an innovative mangrove rehabilitation attempt to restore the endangered mangroves on Carey Island, Malaysia. A submerged detached breakwater system was constructed to dampen the energy of wave and trap the sediments behind the structure. Further, a large number of mangrove seedlings were planted using different techniques. Further, we assess the possibility of success for a future mangrove rehabilitation project at the site in the context of sedimentology, bathymetry, and hydrogeochemistry. The assessment showed an increase in the amount of silt and clay, and the seabed was noticeably elevated. The nutrient concentration, the pH value, and the salinity index demonstrate that the site is conducive in establishing mangrove seedlings. As a result, we conclude that the site is now ready for attempts to rehabilitate the lost mangrove forest.