The Experts below are selected from a list of 202095 Experts worldwide ranked by ideXlab platform

Ozgur Kisi - One of the best experts on this subject based on the ideXlab platform.

  • stream flow forecasting of poorly gauged mountainous watershed by least square support vector machine fuzzy genetic algorithm and m5 model tree using Climatic Data from nearby station
    Water Resources Management, 2018
    Co-Authors: Rana Muhammad Adnan, Ozgur Kisi, Xiaohui Yuan, Muhammad Adnan, Asif Mehmood
    Abstract:

    Forecasting stream flow is a very importance issue in water resources planning and management. The ability of three soft computing methods, least square support vector machine (LSSVM), fuzzy genetic algorithm (FGA) and M5 model tree (M5T), in forecasting daily and monthly stream flows of poorly gauged mountainous watershed using nearby hydro-meteorological Data is investigated in the current study. In the first application, monthly stream flows of Hunza river are forecasted using local stream flow Data of Hunza and precipitation and temperature Data of nearby station. LSSVM provides slightly better forecasts than the FGA and M5T models. Stream flow and temperature inputs generally give better forecasts compared to other inputs. In the second application, daily stream flows of Hunza river are forecasted using local stream flow Data of Hunza and precipitation and temperature Data of nearby station. Better results are obtained from the models comprising only stream flow inputs. In general, a better accuracy is obtained from LSSVM models in relative to the FGA and M5T. The results indicate that the monthly and daily stream flows of Hunza can be accurately forecasted by using only nearby Climatic Data. In the third application, daily stream flows of Hunza river are forecasted using local stream flow and Climatic Data and the models’ accuracy is slightly increased in relative to the previous applications. LSSVM generally performs superior to the FGA and M5T in forecasting daily stream flow of Hunza river using local stream flow and Climatic inputs.

  • modelling long term groundwater fluctuations by extreme learning machine using hydro Climatic Data
    Hydrological Sciences Journal-journal Des Sciences Hydrologiques, 2018
    Co-Authors: Meysam Alizamir, Ozgur Kisi, Mohammad Zounematkermani
    Abstract:

    The ability of the extreme learning machine (ELM) is investigated in modelling groundwater level (GWL) fluctuations using hydro-Climatic Data obtained for Hormozgan Province, southern Iran. Monthly...

  • prediction of long term monthly precipitation using several soft computing methods without Climatic Data
    International Journal of Climatology, 2015
    Co-Authors: Ozgur Kisi, Hadi Sanikhani
    Abstract:

    Accurate estimation of precipitation is an important issue in water resources engineering, management and planning. The accuracy of four different soft computing methods, adaptive neuro-fuzzy inference system (ANFIS) with grid partition (GP), ANFIS with subtractive clustering (SC), artificial neural networks (ANN) and support vector regression (SVR), is investigated in predicting long-term monthly precipitation without Climatic Data. The periodicity component, longitude, latitude and altitude Data from 50 stations in Iran are used as inputs to the applied models. The ANFIS-GP model is found to perform generally better than the other models in predicting long-term monthly precipitation. The SVR model provides the worst estimates. The maximum correlations are found to be 0.935 and 0.944 for the ANFIS-SC and SVR models in Fasa station, respectively. The highest correlations of the ANFIS-GP and ANN models are found to be 0.964 and 0.977 for the Bam and Tabas (Zabol) stations. The minimum correlations are 0.683 and 0.661 for the ANFIS-GP and SVR models in Urmia station while the ANFIS-SC and ANN models provide the minimum correlations of 0.696 and 0.785 in the Sari and Bandar Lengeh stations, respectively. The comparison results show that the long-term monthly precipitations of any site can be successfully predicted by ANFIS-GP model without any weather Data. The monthly and annual precipitations are also mapped and evaluated by using the optimal ANFIS-GP model in the study. The precipitation maps revealed that the highest amounts of precipitation occur in the north, southwestern and west regions, while the lowest values are seen in the east and southeastern parts of the Iran.

  • long term monthly evapotranspiration modeling by several Data driven methods without Climatic Data
    Computers and Electronics in Agriculture, 2015
    Co-Authors: Ozgur Kisi, Mohammad Zounematkermani, Hadi Sanikhani, Faegheh Niazi
    Abstract:

    Different Data-driven methods are compared in predicting monthly ET0.The longitude, latitude and altitude Data from 50 stations are used as inputs.The gene expression programming provides the worst estimates.ET0 of any site can be successfully estimated without Climatic measurements.The ET0 maps show that the highest amounts of ET0 occurred in the southern and especially southeastern parts of the Iran. In this study, the ability of four different Data-driven methods, multilayer perceptron artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) with grid partition (GP), ANFIS with subtractive clustering (SC) and gene expression programming (GEP), was investigated in predicting long-term monthly reference evapotranspiration (ET0) by using Data from 50 stations in Iran. The periodicity component, station latitude, longitude and altitude values were used as inputs to the applied models to predict the long-term monthly ET0 values. The overall accuracies of the multilayer perceptron ANN, ANFIS-GP and ANFIS-SC models were found to be similar to each other. The GEP model provided the worst estimates. The maximum determination coefficient (R2) values were found to be 0.997, 998 and 0.994 for the ANN, ANFIS-GP and ANFIS-SC models in Karaj station, respectively. The highest R2 value (0.978) of GEP model was found for the Qom station. The minimum R2 values were respectively found as 0.959 and 0.935 for the ANN and ANFIS-GP models in Bandar Abbas station while the ANFIS-SC and GEP models gave the minimum R2 values of 0.937 and 0.677 in the Tabriz and Kerman stations, respectively. The results indicated that the long-term monthly reference evapotranspiration of any site can be successfully estimated by Data-driven methods applied in this study without Climatic measurements. The interpolated maps of ET0 were also obtained by using the optimal ANFIS-GP model and evaluated in the study. The ET0 maps showed that the highest amounts of reference evapotranspiration occurred in the southern and especially southeastern parts of the Iran.

  • predicting daily pan evaporation by soft computing models with limited Climatic Data
    Hydrological Sciences Journal-journal Des Sciences Hydrologiques, 2015
    Co-Authors: Sungwon Kim, Jalal Shiri, Ozgur Kisi, Vijay P Singh, Gorka Landeras
    Abstract:

    AbstractAccurate prediction of daily pan evaporation (PE) is important for monitoring, surveying, and management of water resources as well as reservoir management and evaluation of drinking water supply systems. This study develops and applies soft computing models to predict daily PE in a dry climate region of south-western Iran. Three soft computing models, namely the multilayer perceptron-neural networks model (MLP-NNM), Kohonen self-organizing feature maps-neural networks model (KSOFM-NNM), and gene expression programming (GEP), were considered. Daily PE was predicted at two stations using temperature-based, radiation-based, and sunshine duration-based input combinations. The results obtained by the temperature-based 3 (TEM3) model produced the best results for both stations. The Mann-Whitney U test was employed to compute the rank of different input combination for hypothesis testing. Comparison between the soft computing models and multiple linear regression model (MLRM) demonstrated the superiorit...

Hongbo Shao - One of the best experts on this subject based on the ideXlab platform.

  • support vector machine based models for modeling daily reference evapotranspiration with limited Climatic Data in extreme arid regions
    Water Resources Management, 2015
    Co-Authors: Xiaohu Wen, Hongbo Shao
    Abstract:

    Evapotranspiration is a major factor that controls hydrological process and its accurate estimation provides valuable information for water resources planning and management, particularly in extremely arid regions. The objective of this research was to evaluate the use of a support vector machine (SVM) to model daily reference evapotranspiration (ET0) using limited Climatic Data. For the SVM, four combinations of maximum air temperature (T max ), minimum air temperature (T min ), wind speed (U 2 ) and daily solar radiation (R s ) in the extremely arid region of Ejina basin, China, were used as inputs with T max and T min as the base Data set. The results of SVM models were evaluated by comparing the output with the ET0 calculated using Penman–Monteith FAO 56 equation (PMF-56). We found that the ET0 estimated using SVM with limited Climatic Data was in good agreement with those obtained using the conventional PMF-56 equation employing the full complement of meteorological Data. In particular, three Climatic parameters, T max , T min , and R s were enough to predict the daily ET0 satisfactorily. Moreover, the performance of SVM method was also compared with that of artificial neural network (ANN) and three empirical models including Priestley-Taylor, Hargreaves, and Ritchie. The results showed that the performance of SVM method was the best among these models. This offers significant potential for more accurate estimation of the ET0 with scarce Data in extreme arid regions.

  • support vector machine based models for modeling daily reference evapotranspiration with limited Climatic Data in extreme arid regions
    Water Resources Management, 2015
    Co-Authors: Xiaohu Wen, Hongbo Shao
    Abstract:

    Evapotranspiration is a major factor that controls hydrological process and its accurate estimation provides valuable information for water resources planning and management, particularly in extremely arid regions. The objective of this research was to evaluate the use of a support vector machine (SVM) to model daily reference evapotranspiration (ET 0 ) using limited Climatic Data. For the SVM, four combinations of maximum air temperature (T max ), minimum air temperature (T min ), wind speed (U 2 ) and daily solar radiation (R s ) in the extremely arid region of Ejina basin, China, were used as inputs with T max and T min as the base Data set. The results of SVM models were evaluated by comparing the output with the ET 0 calculated using Penman–Monteith FAO 56 equation (PMF-56). We found that the ET 0 estimated using SVM with limited Climatic Data was in good agreement with those obtained using the conventional PMF-56 equation employing the full complement of meteorological Data. In particular, three Climatic parameters, T max , T min , and R s were enough to predict the daily ET 0 satisfactorily. Moreover, the performance of SVM method was also compared with that of artificial neural network (ANN) and three empirical models including Priestley-Taylor, Hargreaves, and Ritchie. The results showed that the performance of SVM method was the best among these models. This offers significant potential for more accurate estimation of the ET 0 with scarce Data in extreme arid regions. Copyright Springer Science+Business Media Dordrecht 2015

Thomas W Gillespie - One of the best experts on this subject based on the ideXlab platform.

  • global tropical dry forest extent and cover a comparative study of bioClimatic definitions using two Climatic Data sets
    PLOS ONE, 2021
    Co-Authors: Jonathan Pando Ocon, Thomas Ibanez, Janet Franklin, Stephanie Pau, Gunnar Keppel, Gonzalo Rivastorres, Michael Edward Shin, Thomas W Gillespie
    Abstract:

    There is a debate concerning the definition and extent of tropical dry forest biome and vegetation type at a global spatial scale. We identify the potential extent of the tropical dry forest biome based on bioClimatic definitions and Climatic Data sets to improve global estimates of distribution, cover, and change. We compared four bioClimatic definitions of the tropical dry forest biome–Murphy and Lugo, Food and Agriculture Organization (FAO), DryFlor, aridity index–using two Climatic Data sets: WorldClim and Climatologies at High-resolution for the Earth’s Land Surface Areas (CHELSA). We then compared each of the eight unique combinations of bioClimatic definitions and Climatic Data sets using 540 field plots identified as tropical dry forest from a literature search and evaluated the accuracy of World Wildlife Fund tropical and subtropical dry broadleaf forest ecoregions. We used the definition and climate Data that most closely matched field Data to calculate forest cover in 2000 and change from 2001 to 2020. Globally, there was low agreement (< 58%) between bioClimatic definitions and WWF ecoregions and only 40% of field plots fell within these ecoregions. FAO using CHELSA had the highest agreement with field plots (81%) and was not correlated with the biome extent. Using the FAO definition with CHELSA Climatic Data set, we estimate 4,931,414 km 2 of closed canopy (≥ 40% forest cover) tropical dry forest in 2000 and 4,369,695 km 2 in 2020 with a gross loss of 561,719 km 2 (11.4%) from 2001 to 2020. Tropical dry forest biome extent varies significantly based on bioClimatic definition used, with nearly half of all tropical dry forest vegetation missed when using ecoregion boundaries alone, especially in Africa. Using site-specific field validation, we find that the FAO definition using CHELSA provides an accurate, standard, and repeatable way to assess tropical dry forest cover and change at a global scale.

Xiaohu Wen - One of the best experts on this subject based on the ideXlab platform.

  • wavelet analysis artificial neural network conjunction models for multi scale monthly groundwater level predicting in an arid inland river basin northwestern china
    Hydrology Research, 2017
    Co-Authors: Xiaohu Wen, Qi Feng, Ravinesh C Deo
    Abstract:

    In this study, the ability of a wavelet analysis–artificial neural network (WA-ANN) conjunction model for multi-scale monthly groundwater level forecasting was evaluated in an arid inland river basin, northwestern China. The WA-ANN models were obtained by combining discrete wavelet transformation with ANN. For WA-ANN model, three different input combinations were trialed in order to optimize the model performance: (1) ancient groundwater level only, (2) ancient Climatic Data, and (3) ancient groundwater level combined with Climatic Data to forecast the groundwater level for two wells in Zhangye basin. Based on the key statistical measures, the performance of the WA-ANN model was significantly better than ANN model. However, WA-ANN model with ancient groundwater level as its input yielded the best performance for 1-month groundwater forecasts. For 2- and 3-monthly forecasts, the performance of the WA-ANN model with integrated ancient groundwater level and Climatic Data as inputs was the most superior. Notwithstanding this, the WA-ANN model with only ancient Climatic Data as its inputs also exhibited accurate results for 1-, 2-, and 3-month groundwater forecasting. It is ascertained that the WA-ANN model is a useful tool for simulation of multi-scale groundwater forecasting in the current study region.

  • support vector machine based models for modeling daily reference evapotranspiration with limited Climatic Data in extreme arid regions
    Water Resources Management, 2015
    Co-Authors: Xiaohu Wen, Hongbo Shao
    Abstract:

    Evapotranspiration is a major factor that controls hydrological process and its accurate estimation provides valuable information for water resources planning and management, particularly in extremely arid regions. The objective of this research was to evaluate the use of a support vector machine (SVM) to model daily reference evapotranspiration (ET0) using limited Climatic Data. For the SVM, four combinations of maximum air temperature (T max ), minimum air temperature (T min ), wind speed (U 2 ) and daily solar radiation (R s ) in the extremely arid region of Ejina basin, China, were used as inputs with T max and T min as the base Data set. The results of SVM models were evaluated by comparing the output with the ET0 calculated using Penman–Monteith FAO 56 equation (PMF-56). We found that the ET0 estimated using SVM with limited Climatic Data was in good agreement with those obtained using the conventional PMF-56 equation employing the full complement of meteorological Data. In particular, three Climatic parameters, T max , T min , and R s were enough to predict the daily ET0 satisfactorily. Moreover, the performance of SVM method was also compared with that of artificial neural network (ANN) and three empirical models including Priestley-Taylor, Hargreaves, and Ritchie. The results showed that the performance of SVM method was the best among these models. This offers significant potential for more accurate estimation of the ET0 with scarce Data in extreme arid regions.

  • support vector machine based models for modeling daily reference evapotranspiration with limited Climatic Data in extreme arid regions
    Water Resources Management, 2015
    Co-Authors: Xiaohu Wen, Hongbo Shao
    Abstract:

    Evapotranspiration is a major factor that controls hydrological process and its accurate estimation provides valuable information for water resources planning and management, particularly in extremely arid regions. The objective of this research was to evaluate the use of a support vector machine (SVM) to model daily reference evapotranspiration (ET 0 ) using limited Climatic Data. For the SVM, four combinations of maximum air temperature (T max ), minimum air temperature (T min ), wind speed (U 2 ) and daily solar radiation (R s ) in the extremely arid region of Ejina basin, China, were used as inputs with T max and T min as the base Data set. The results of SVM models were evaluated by comparing the output with the ET 0 calculated using Penman–Monteith FAO 56 equation (PMF-56). We found that the ET 0 estimated using SVM with limited Climatic Data was in good agreement with those obtained using the conventional PMF-56 equation employing the full complement of meteorological Data. In particular, three Climatic parameters, T max , T min , and R s were enough to predict the daily ET 0 satisfactorily. Moreover, the performance of SVM method was also compared with that of artificial neural network (ANN) and three empirical models including Priestley-Taylor, Hargreaves, and Ritchie. The results showed that the performance of SVM method was the best among these models. This offers significant potential for more accurate estimation of the ET 0 with scarce Data in extreme arid regions. Copyright Springer Science+Business Media Dordrecht 2015

Stefan Wiesmeier - One of the best experts on this subject based on the ideXlab platform.

  • modelling of conditions for accelerated lifetime testing of humidity impact on pv modules based on monitoring of Climatic Data
    Solar Energy Materials and Solar Cells, 2012
    Co-Authors: Michael Koehl, M Heck, Stefan Wiesmeier
    Abstract:

    Abstract Water is considered as an important degradation factor for PV-modules by causing hydrolysis of polymeric components, corrosion of glass and of metallic components like grids and interconnectors. The type approval testing of PV-modules according to the IEC standards takes water into account by means of the so called Damp-Heat test and the Frost-Thaw test. The test-conditions were designed long time ago and initially meant to reflect the service life of a module, but the type approval testing is not applicable for prediction of long-term durability behaviour, but a qualification test for the assurance of a minimum quality level. Basically absorption and mass transport of water into and within the module has to be considered. These phenomena depend on the ambient climate of the PV-module in use, the design (back-sheet, glass-glass, all polymer, e.g.), the operation conditions (rack-mounted, on rooftop, building integrated) and the material composition. We try to evaluate test conditions that are primarily based on the stresses occurring during the operation at specific locations by using monitored Climatic Data and applying phenomenological models for the estimation of the moisture load at the surfaces of PV-modules as function of the module temperature. A simple time transformation function was used for the design of appropriate damp-heat tests as accelerated service life tests. The evaluated testing times differ up to an order of magnitude for different Climatic locations, depending on the kinetics of the dominant degradation processes.

  • modeling of the nominal operating cell temperature based on outdoor weathering
    Solar Energy Materials and Solar Cells, 2011
    Co-Authors: Michael Koehl, M Heck, Stefan Wiesmeier, Jochen Wirth
    Abstract:

    Simple analytical and statistical models for the evaluation of the temperature of PV-modules from Climatic Data (ambient temperature, global solar irradiation, and wind speed) are investigated. The parameters which describe the effects of cell technology and module design were evaluated from outdoor exposure Data at different Climatic regions. The models were validated by comparison of the simulated module temperatures with measured module temperatures at different test sites. A simplified way to determine a Realistic Nominal Module Temperature (ROMT) instead of the Nominal Operating Cell Temperature (NOCT) is proposed.

  • evaluation of the accelerated life testing conditions for pv modules based on measured and simulated weathering stress
    Photovoltaic Specialists Conference, 2011
    Co-Authors: Michael Koehl, M Heck, Stefan Wiesmeier
    Abstract:

    The development of novel cost-reducing components for PV-modules requires accelerated service life testing procedures for their qualification. A methodology was developed, which can facilitate the estimation of the durability of PV-modules at locations with available Climatic Data.