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Lake Water Surface Area (WSA) plays a vital role in environmental preservation and future water resource planning and management. Accurately mapping, monitoring and forecasting Lake WSA changes are of great importance to regulatory agencies. This study used the MODIS satellite images to extract a monthly time series of WSA of two lakes located in Iran from 2001 to 2019. Following a consequence of image and time series preprocessing to obtain the preprocessed lake surface area time series, the outcomes were modeled by the Long-Short-Term Memory (LSTM) deep learning (DL) method, the stochastic Seasonal Auto-Regressive Integrated Moving Average (SARIMA) method and hybridization of these two techniques with the objective of developing WSA forecasts. After separate standardization and normalization of AL TS and reevaluation of the preprocessed data, the SARIMA (1, 0, 0) (0, 1, 1)12 model outperformed sole LSTM models with correlation index of (R) 0.819, mean absolute error (MAE) of 49.425 and mean absolute percentage error (MAPE) of 0.106. On the other hand, the hybridization (stochastic-DL) enhanced the reproduction of the primal statistical properties of WSA data and caused better mediation. However, the other accuracy indices did not change markedly (R 0.819, MAE 49.310, MAPE 0.105). The multi-step preprocessing and reevaluation also caused all LSTM models to produce their best results by less than 12 inputs.
Keyvan Soltani; Arash Azari; Mohammad Zeynoddin; Afshin Amiri; Isa Ebtehaj; Taha B.M.J. Ouarda; Bahram Gharabaghi; Hossein Bonakdari. Lake Surface Area Forecasting Using Integrated Satellite-SARIMA-Long-Short-Term Memory Model. 2021, 1 .
AMA StyleKeyvan Soltani, Arash Azari, Mohammad Zeynoddin, Afshin Amiri, Isa Ebtehaj, Taha B.M.J. Ouarda, Bahram Gharabaghi, Hossein Bonakdari. Lake Surface Area Forecasting Using Integrated Satellite-SARIMA-Long-Short-Term Memory Model. . 2021; ():1.
Chicago/Turabian StyleKeyvan Soltani; Arash Azari; Mohammad Zeynoddin; Afshin Amiri; Isa Ebtehaj; Taha B.M.J. Ouarda; Bahram Gharabaghi; Hossein Bonakdari. 2021. "Lake Surface Area Forecasting Using Integrated Satellite-SARIMA-Long-Short-Term Memory Model." , no. : 1.
Entropy models have been recently adopted in many studies to evaluate the shear stress distribution in open-channel flows. Although the uncertainty of Shannon and Tsallis entropy models were analyzed separately in previous studies, the uncertainty of other entropy models and comparisons of their reliability remain an open question. In this study, a new method is presented to evaluate the uncertainty of four entropy models, Shannon, Shannon-Power Law (PL), Tsallis, and Renyi, in shear stress prediction of the circular channels. In the previous method, the model with the largest value of the percentage of observed data within the confidence bound (Nin) and the smallest value of Forecasting Range of Error Estimation (FREE) is the most reliable. Based on the new method, using the effect of Optimized Forecasting Range of Error Estimation (FREEopt) and Optimized Confidence Bound (OCB), a new statistic index called FREEopt-based OCB (FOCB) is introduced. The lower the value of FOCB, the more certain the model. Shannon and Shannon PL entropies had close values of the FOCB equal to 8.781 and 9.808, respectively, and had the highest certainty, followed by ρgRs and Tsallis models with close values of 14.491 and 14.895, respectively. However, Renyi entropy, with the value of FOCB equal to 57.726, had less certainty.
Amin Kazemian-Kale-Kale; Azadeh Gholami; Mohammad Rezaie-Balf; Amir Mosavi; Ahmed Sattar; Amir Azimi; Bahram Gharabaghi; Hossein Bonakdari. Uncertainty Assessment of Entropy-Based Circular Channel Shear Stress Prediction Models Using a Novel Method. Geosciences 2021, 11, 308 .
AMA StyleAmin Kazemian-Kale-Kale, Azadeh Gholami, Mohammad Rezaie-Balf, Amir Mosavi, Ahmed Sattar, Amir Azimi, Bahram Gharabaghi, Hossein Bonakdari. Uncertainty Assessment of Entropy-Based Circular Channel Shear Stress Prediction Models Using a Novel Method. Geosciences. 2021; 11 (8):308.
Chicago/Turabian StyleAmin Kazemian-Kale-Kale; Azadeh Gholami; Mohammad Rezaie-Balf; Amir Mosavi; Ahmed Sattar; Amir Azimi; Bahram Gharabaghi; Hossein Bonakdari. 2021. "Uncertainty Assessment of Entropy-Based Circular Channel Shear Stress Prediction Models Using a Novel Method." Geosciences 11, no. 8: 308.
Shortwave radiation density flux (SRDF) modeling can be key in estimating actual evapotranspiration in plants. SRDF is the result of the specific and scattered reflection of shortwave radiation by the underlying surface. SRDF can have profound effects on some plant biophysical processes such as photosynthesis and land surface energy budgets. Since it is the main energy source for most atmospheric phenomena, SRDF is also widely used in numerical weather forecasting. In the current study, an improved version of the extreme learning machine was developed for SRDF forecasting using the historical value of this variable. To do that, the SRDF through 1981–2019 was extracted by developing JavaScript-based coding in the Google Earth Engine. The most important lags were found using the auto-correlation function and defined fifteen input combinations to model SRDF using the improved extreme learning machine (IELM). The performance of the developed model is evaluated based on the correlation coefficient (R), root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash–Sutcliffe efficiency (NSE). The shortwave radiation was developed for two time ahead forecasting (R = 0.986, RMSE = 21.11, MAPE = 8.68%, NSE = 0.97). Additionally, the estimation uncertainty of the developed improved extreme learning machine is quantified and compared with classical ELM and found to be the least with a value of ±3.64 compared to ±6.9 for the classical extreme learning machine. IELM not only overcomes the limitation of the classical extreme learning machine in random adjusting of bias of hidden neurons and input weights but also provides a simple matrix-based method for practical tasks so that there is no need to have any knowledge of the improved extreme learning machine to use it.
Isa Ebtehaj; Keyvan Soltani; Afshin Amiri; Marzban Faramarzi; Chandra Madramootoo; Hossein Bonakdari. Prognostication of Shortwave Radiation Using an Improved No-Tuned Fast Machine Learning. Sustainability 2021, 13, 8009 .
AMA StyleIsa Ebtehaj, Keyvan Soltani, Afshin Amiri, Marzban Faramarzi, Chandra Madramootoo, Hossein Bonakdari. Prognostication of Shortwave Radiation Using an Improved No-Tuned Fast Machine Learning. Sustainability. 2021; 13 (14):8009.
Chicago/Turabian StyleIsa Ebtehaj; Keyvan Soltani; Afshin Amiri; Marzban Faramarzi; Chandra Madramootoo; Hossein Bonakdari. 2021. "Prognostication of Shortwave Radiation Using an Improved No-Tuned Fast Machine Learning." Sustainability 13, no. 14: 8009.
Accurate modeling of groundwater level (GWL) is a critical and challenging issue in water resources management. The GWL fluctuations rely on many nonlinear hydrological variables and uncertain factors. Therefore, it is important to use an approach that can reduce the parameters involved in the modeling process and minimize the associated errors. This study presents a novel approach for time series structural analysis, multi-step preprocessing, and GWL modeling. In this study, we identified the time series deterministic and stochastic terms by employing a one-, two-, and three-step preprocessing techniques (a combination of trend analysis, standardization, spectral analysis, differencing, and normalization techniques). The application of this approach is tested on the GWL dataset of the Kermanshah plains located in the northwest region of Iran, using monthly observations of 60 piezometric stations from September 1991 to August 2017. By removing the dominant nonstationary factors of the GWL data, a linear model with one autoregressive and one seasonal moving average parameter, detrending, and consecutive non-seasonal and seasonal differencing were created. The quantitative assessment of this model indicates the high performance in GWL forecasting with the coefficient of determination (R2) 0.94, scatter index (SI) 0.0004, mean absolute percentage error (MAPE) 0.0003, root mean squared relative error (RMSRE) 0.0004, and corrected Akaike's information criterion (AICc) 151. Moreover, the uncertainty and accuracy of the proposed linear-based method are compared with two conventional nonlinear methods, including multilayer perceptron artificial neural network (MLP-ANN) and adaptive neuro-fuzzy inference systems (ANFIS). The uncertainty of the proposed method in this study was ± 0.105 compared to ± 0.114 and ± 0.126 for the best results of the ANN and the ANFIS models, respectively.
Arash Azari; Mohammad Zeynoddin; Isa Ebtehaj; Ahmed M. A. Sattar; Bahram Gharabaghi; Hossein Bonakdari. Integrated preprocessing techniques with linear stochastic approaches in groundwater level forecasting. Acta Geophysica 2021, 1 -17.
AMA StyleArash Azari, Mohammad Zeynoddin, Isa Ebtehaj, Ahmed M. A. Sattar, Bahram Gharabaghi, Hossein Bonakdari. Integrated preprocessing techniques with linear stochastic approaches in groundwater level forecasting. Acta Geophysica. 2021; ():1-17.
Chicago/Turabian StyleArash Azari; Mohammad Zeynoddin; Isa Ebtehaj; Ahmed M. A. Sattar; Bahram Gharabaghi; Hossein Bonakdari. 2021. "Integrated preprocessing techniques with linear stochastic approaches in groundwater level forecasting." Acta Geophysica , no. : 1-17.
Isa Ebtehaj; Hossein Bonakdari. Discussion of “Comparative Study of Time Series Models, Support Vector Machines, and GMDH in Forecasting Long-Term Evapotranspiration Rates in Northern Iran” by Afshin Ashrafzadeh, Ozgur Kişi, Pouya Aghelpour, Seyed Mostafa Biazar, and Mohammadreza Askarizad Masouleh. Journal of Irrigation and Drainage Engineering 2021, 147, 07021005 .
AMA StyleIsa Ebtehaj, Hossein Bonakdari. Discussion of “Comparative Study of Time Series Models, Support Vector Machines, and GMDH in Forecasting Long-Term Evapotranspiration Rates in Northern Iran” by Afshin Ashrafzadeh, Ozgur Kişi, Pouya Aghelpour, Seyed Mostafa Biazar, and Mohammadreza Askarizad Masouleh. Journal of Irrigation and Drainage Engineering. 2021; 147 (6):07021005.
Chicago/Turabian StyleIsa Ebtehaj; Hossein Bonakdari. 2021. "Discussion of “Comparative Study of Time Series Models, Support Vector Machines, and GMDH in Forecasting Long-Term Evapotranspiration Rates in Northern Iran” by Afshin Ashrafzadeh, Ozgur Kişi, Pouya Aghelpour, Seyed Mostafa Biazar, and Mohammadreza Askarizad Masouleh." Journal of Irrigation and Drainage Engineering 147, no. 6: 07021005.
While numerous studies have investigated physically-based analytical approaches for estimating stream flow probability distributions and occurrences of overbank flow, these types of models are limited by their associated complexity to incorporate a wide range of data from all components of the hydrologic system to model their influence on river flows. Alternatively, the Generalized Structure Group Method of Data Handling (GS-GMDH) is a polynomial network approach used in this study to train and test models for daily and hourly times series flow prediction for riverine flooding using available data from 1990-2018 and 1996-2018, respectively. The model is found to accurately predict daily flows with R2, RMSE, MAE, Bias and NSE of 0.6441, 46.884, 6.700, 1.800 and 0.6441, respectively, for nine years of flow data in application to the Bow River in Alberta, Canada. Hourly flow data used to train (70%) and test (30%) the GS-GMDH model results in R2, RMSE, MAE, Bias and NSE of 0.998, 3.323, 0.997, 0.00438 and 0.998, respectively. The trained hourly model can predict up to 17 hours in advance while maintaining R2 greater than 0.90. Horizontal error highlights a weakness in model performance, contrary to other evaluation statistics, due to presence of imitation error.
Mostafa Elkurdy; Andrew D. Binns; Hossein Bonakdari; Bahram Gharabaghi; Edward McBean. Early detection of riverine flooding events using the group method of data handling for the Bow River, Alberta, Canada. International Journal of River Basin Management 2021, 1 -12.
AMA StyleMostafa Elkurdy, Andrew D. Binns, Hossein Bonakdari, Bahram Gharabaghi, Edward McBean. Early detection of riverine flooding events using the group method of data handling for the Bow River, Alberta, Canada. International Journal of River Basin Management. 2021; ():1-12.
Chicago/Turabian StyleMostafa Elkurdy; Andrew D. Binns; Hossein Bonakdari; Bahram Gharabaghi; Edward McBean. 2021. "Early detection of riverine flooding events using the group method of data handling for the Bow River, Alberta, Canada." International Journal of River Basin Management , no. : 1-12.
One of the unintended consequences of urban stormwater quality management wet ponds is the thermal enrichment of the shallow pond water during the summer months. The outflow from thermally enriched ponds can degrade the cold and cool water aquatic habitat in urban streams. An accurate model of the pond temperature profile is needed to assess the pond effluent thermal load. However, most models developed for this purpose are process-based and not simple to use, requiring a lengthy monitoring dataset to calibrate. This paper introduces a new design/assessment tool to predict the hourly water temperatures in these ponds at different depths during the summer season dry-weather warming periods and the diurnal cycles. We compiled monitoring data from five ponds in the Greater Toronto Area from 2013 − 2016 to evaluate the ponds' thermal impact on the receiving cold water streams. Using these datasets, we developed a new equation that allows for separating the active upper portion and stable lower portion of the pond. This ensures the focus is on the active upper part, where the majority of the heat transfer within the pond occurs and exhibits diurnal temperature fluctuations. We also used machine-learning tools to develop accurate equations for the bottom temperature and surface temperature, as well as, key equation parameters that characterize the thermal profiles. The developed thermal profile equation allows for the simple determination of other key thermal metrics such as average temperature, energy stored, thermocline location, which may be used as inputs to model wet weather flow conditions and pond outflow temperature. The results show that the proposed machine-learning model is acceptably accurate and capable, according to the low mean absolute percentage error (MAPE) and coefficient of determination (R2) values of 5% and 0.965, respectively, for temperature prediction for all depths. This work provides a building block for the overall objective to develop a new, easy to use, wet weather explicit equation for predicting the outlet temperature from stormwater management wet ponds since it establishes the initial thermal condition before the storm hits.
Stephen Stajkowski; Alex Laleva; Hani Farghaly; Hossein Bonakdari; Bahram Gharabaghi. Modelling dry-weather temperature profiles in urban stormwater management ponds. Journal of Hydrology 2021, 598, 126206 .
AMA StyleStephen Stajkowski, Alex Laleva, Hani Farghaly, Hossein Bonakdari, Bahram Gharabaghi. Modelling dry-weather temperature profiles in urban stormwater management ponds. Journal of Hydrology. 2021; 598 ():126206.
Chicago/Turabian StyleStephen Stajkowski; Alex Laleva; Hani Farghaly; Hossein Bonakdari; Bahram Gharabaghi. 2021. "Modelling dry-weather temperature profiles in urban stormwater management ponds." Journal of Hydrology 598, no. : 126206.
Accurate runoff forecasting plays a considerable role in the appropriate water resource planning and management. The spatial and temporal evaluation of the flood susceptibility was explored in the Quebec basin, Canada. This study provides a new strategy for runoff modelling as one of the complicated variables by developing new machine learning techniques along with remote sensing. A novel scheme of the Group Method of Data Handling (GMDH) known as the generalized structure of GMDH (GSGGMDH) is developed to overcome this classical approach's limitation. A simple time series based scenario with exogenous variables including precipitation and Normalized Difference Vegetation Index (NDVI) was introduced for runoff forecasting. MODIS data included MOD13Q1 product was employed and a JavaScript code was developed to preprocess collected data in the Google Earth Engine (GEE) environment. Using different seasonal and non-seasonal lags of all input variables, the developed GSGMDH found the most optimum input combination for each station in terms of simplicity and accuracy, simultaneously (average values; SI = 0.554, RMSRE = 1.55, MAE = 5.076). The precipitation values are modelled with the CanEsm2 climate change model. To apply NDVI for runoff forecasting, a simple spatial-temporal GSGMDH based model was developed (average values; SI = 0.27; RMSRE = 8.27, MAE = 0.08). The forecasting results indicated that the months in which the maximum runoff occurred have changed, and these months have increased compared to the historic period. In the historical period, the frequency of maximum runoff was in April and March. Still, for the two forecasting periods (i.e. 2020–2039 and 2040–2059), the months in which the maximum runoff has occurred have changed, and their amount has been reduced and added to other months, especially February and August.
Keyvan Soltani; Isa Ebtehaj; Afshin Amiri; Arash Azari; Bahram Gharabaghi; Hossein Bonakdari. Mapping the spatial and temporal variability of flood susceptibility using remotely sensed normalized difference vegetation index and the forecasted changes in the future. Science of The Total Environment 2021, 770, 145288 .
AMA StyleKeyvan Soltani, Isa Ebtehaj, Afshin Amiri, Arash Azari, Bahram Gharabaghi, Hossein Bonakdari. Mapping the spatial and temporal variability of flood susceptibility using remotely sensed normalized difference vegetation index and the forecasted changes in the future. Science of The Total Environment. 2021; 770 ():145288.
Chicago/Turabian StyleKeyvan Soltani; Isa Ebtehaj; Afshin Amiri; Arash Azari; Bahram Gharabaghi; Hossein Bonakdari. 2021. "Mapping the spatial and temporal variability of flood susceptibility using remotely sensed normalized difference vegetation index and the forecasted changes in the future." Science of The Total Environment 770, no. : 145288.
A simplified empirical equation is developed for widespread prediction of dynamic catchment response time. This model allows for time‐to‐peak prediction to evolve from static, lumped models, thereby providing a single value for any storm within a given catchment, using a single set of input parameters, that can be applied to a dynamic model, thus accounting for the variability between storm sizes and catchment moisture conditions. These dynamic prediction methods are translated to North America for the first time. This allows the concepts and prediction methods for catchment response time prediction previously established for the United Kingdom (UK), to be translated to a simple empirical equation for use in North America, through the use of selected study areas in Canada and the United States. This reconfigured model is statistically successful in both the UK and North America and allows for a straightforward implementation of dynamic time‐to‐peak prediction. Further, the reconfigured model introduces the use of a runoff coefficient (Rc) to encompass historical catchment wetness, increasing the ease of incorporating antecedent moisture condition into predictions. This article is protected by copyright. All rights reserved.
Mistaya Langridge; Ed McBean; Hossein Bonakdari; Bahram Gharabaghi. A dynamic prediction model for time‐to‐peak. Hydrological Processes 2021, 35, 1 .
AMA StyleMistaya Langridge, Ed McBean, Hossein Bonakdari, Bahram Gharabaghi. A dynamic prediction model for time‐to‐peak. Hydrological Processes. 2021; 35 (1):1.
Chicago/Turabian StyleMistaya Langridge; Ed McBean; Hossein Bonakdari; Bahram Gharabaghi. 2021. "A dynamic prediction model for time‐to‐peak." Hydrological Processes 35, no. 1: 1.
In this study, an extension of a group Multi-Criteria Decision-Making (MCDM) method based on the fuzzy ELECTRE III (ELimination Et Choice Translating REality) model is presented for water supply choice optimization. The fuzzy ELECTRE III method is improved by using three credibility definitions - concordance, discordance, and net degrees. Experts' opinions are transformed into triangular fuzzy numbers based on the level of uncertainty associated with quantitative and qualitative criteria. The main priority of this method compared to other existing MCDM is that it is a more effective way of dealing with the uncertainties in projects as the application of the opinions is made based on a group decision. A Case Study of a water supply system for the Gamasiab Basin located in the Kermanshah province of Iran is examined to demonstrate the application of the model. Comparing the introduced method's results with the existing MCDMs, including fuzzy TOPSIS and fuzzy AHP methods, indicated that the new method stands more consistent with the local experts' opinions. Therefore, the proposed method is recommended as the optimal decision-making technique for similar applications of complex water supply engineering projects.
Amir Noori; Hossein Bonakdari; Amir Hossein Salimi; Bahram Gharabaghi. A group Multi-Criteria Decision-Making method for water supply choice optimization. Socio-Economic Planning Sciences 2020, 77, 101006 .
AMA StyleAmir Noori, Hossein Bonakdari, Amir Hossein Salimi, Bahram Gharabaghi. A group Multi-Criteria Decision-Making method for water supply choice optimization. Socio-Economic Planning Sciences. 2020; 77 ():101006.
Chicago/Turabian StyleAmir Noori; Hossein Bonakdari; Amir Hossein Salimi; Bahram Gharabaghi. 2020. "A group Multi-Criteria Decision-Making method for water supply choice optimization." Socio-Economic Planning Sciences 77, no. : 101006.
Background An important task in developing accurate public health intervention evaluation methods based on historical interrupted time series (ITS) records is to determine the exact lag time between pre- and post-intervention. We propose a novel continuous transitional data-driven hybrid methodology using a non-linear approach based on a combination of stochastic and artificial intelligence methods that facilitate the evaluation of ITS data without knowledge of lag time. Understanding the influence of implemented intervention on outcome(s) is imperative for decision makers in order to manage health systems accurately and in a timely manner. Methods To validate a developed hybrid model, we used, as an example, a published dataset based on a real health problem on the effects of the Italian smoking ban in public spaces on hospital admissions for acute coronary events. We employed a continuous methodology based on data preprocessing to identify linear and nonlinear components in which autoregressive moving average and generalized structure group method of data handling were combined to model stochastic and nonlinear components of ITS. We analyzed the rate of admission for acute coronary events from January 2002 to November 2006 using this new data-driven hybrid methodology that allowed for long-term outcome prediction. Results Our results showed the Pearson correlation coefficient of the proposed combined transitional data-driven model exhibited an average of 17.74% enhancement from the single stochastic model and 2.05% from the nonlinear model. In addition, data demonstrated that the developed model improved the mean absolute percentage error and correlation coefficient values for which 2.77% and 0.89 were found compared to 4.02% and 0.76, respectively. Importantly, this model does not use any predefined lag time between pre- and post-intervention. Conclusions Most of the previous studies employed the linear regression and considered a lag time to interpret the impact of intervention on public health outcome. The proposed hybrid methodology improved ITS prediction from conventional methods and could be used as a reliable alternative in public health intervention evaluation.
Hossein Bonakdari; Jean-Pierre Pelletier; Johanne Martel-Pelletier. A continuous data driven translational model to evaluate effectiveness of population-level health interventions: case study, smoking ban in public places on hospital admissions for acute coronary events. Journal of Translational Medicine 2020, 18, 1 -21.
AMA StyleHossein Bonakdari, Jean-Pierre Pelletier, Johanne Martel-Pelletier. A continuous data driven translational model to evaluate effectiveness of population-level health interventions: case study, smoking ban in public places on hospital admissions for acute coronary events. Journal of Translational Medicine. 2020; 18 (1):1-21.
Chicago/Turabian StyleHossein Bonakdari; Jean-Pierre Pelletier; Johanne Martel-Pelletier. 2020. "A continuous data driven translational model to evaluate effectiveness of population-level health interventions: case study, smoking ban in public places on hospital admissions for acute coronary events." Journal of Translational Medicine 18, no. 1: 1-21.
Drought is one of the most environmentally impactful hydrologic processes with devastating economic consequences for many rural communities in arid and semi-arid countries all over the world. In this research, we have employed satellite data and a stochastic approach for forecasting the changes in lake surface areas and demonstrated for the application of the new technique for the case study of the Lake Gregory in Australia. High-resolution Landsat satellite images are used on a monthly time scale from Landsat 5, 7, and 8, on days that are not cloudy. The software ENVI 5.3, using normalized difference vegetation index (NDVI), and modify normalized difference water index (MNDWI) indices were employed to obtain the lake surface maps, and satellite images have been split into water and non-water using a decision tree. The ArcGIS 10.3 software was used to calculate the area of the Lake monthly. The overall trend data shows that from 2004 to 2019, the LS is steadily declining, reaching its lowest area in 2019.The TRMM satellite monthly precipitation (P) and temperature (T) measurement were obtained to investigate the correlation between these changes and regional precipitation. We developed a novel generalized group method of data handling (GGMDH) to forecast lake surface (LS) fluctuations, in which the LS time-series database is extracted from the satellite imagery. For downscaling, precipitation and three different scenarios are defined based on climate change projections to forecast the LS in the 2020–2060 period. The comparison of the GGMDH with stochastic models integrated with preprocessing scenarios indicates the GGMDH in long-term LS forecasting outperforms the stochastic model. The result showed GGMDH is the best model among other ones to modeling lake surface by R2 (%) = 94.16, RMSE = 8.77 for the forecasting stage. The forecasted surface of the Lake Gregory fluctuated from 226 to 0.008 km2 in the future.
Keyvan Soltani; Afshin Amiri; Mohammad Zeynoddin; Isa Ebtehaj; Bahram Gharabaghi; Hossein Bonakdari. Forecasting monthly fluctuations of lake surface areas using remote sensing techniques and novel machine learning methods. Theoretical and Applied Climatology 2020, 143, 713 -735.
AMA StyleKeyvan Soltani, Afshin Amiri, Mohammad Zeynoddin, Isa Ebtehaj, Bahram Gharabaghi, Hossein Bonakdari. Forecasting monthly fluctuations of lake surface areas using remote sensing techniques and novel machine learning methods. Theoretical and Applied Climatology. 2020; 143 (1-2):713-735.
Chicago/Turabian StyleKeyvan Soltani; Afshin Amiri; Mohammad Zeynoddin; Isa Ebtehaj; Bahram Gharabaghi; Hossein Bonakdari. 2020. "Forecasting monthly fluctuations of lake surface areas using remote sensing techniques and novel machine learning methods." Theoretical and Applied Climatology 143, no. 1-2: 713-735.
Sedimentation in open channels occurs frequently and is relative to system inflow. The long-term retention of sediments on channel beds can increase the possibility of variations in deposits and their eventual consolidation. This study compares three hybrid artificial intelligence methods in estimating sediment transport without sedimentation (STWS). We employed the Particle Swarm Optimization (PSO), Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) methods in combination with the Artificial Neural Network (ANN) to overcome the weakness of ANN training with conventional algorithms. We used the ICA, GA and PSO methods to optimize the weights of the ANN layers. Using dimensional analysis, we placed the effective parameters in predicting sediment transport into five non-dimensional groups. Six models are proposed and run using three hybrid methods (18 models in total). As the comparisons demonstrate, the proposed combined models are more accurate than ANN and existing equations in estimating the densimetric Froude number (Fr). However, we found the ICA–ANN superior to GA–ANN and PSO–ANN, as it produces explicit solutions to the problem. The ICA–ANN has the lowest prediction uncertainty band for Fr of all developed models. Moreover, the variation trend of the Fr for all input variables (except overall friction factor of sediment) is a second-order polynomial.
Isa Ebtehaj; Hossein Bonakdari; Amir Hossein Zaji; Bahram Gharabaghi. Evolutionary optimization of neural network to predict sediment transport without sedimentation. Complex & Intelligent Systems 2020, 7, 401 -416.
AMA StyleIsa Ebtehaj, Hossein Bonakdari, Amir Hossein Zaji, Bahram Gharabaghi. Evolutionary optimization of neural network to predict sediment transport without sedimentation. Complex & Intelligent Systems. 2020; 7 (1):401-416.
Chicago/Turabian StyleIsa Ebtehaj; Hossein Bonakdari; Amir Hossein Zaji; Bahram Gharabaghi. 2020. "Evolutionary optimization of neural network to predict sediment transport without sedimentation." Complex & Intelligent Systems 7, no. 1: 401-416.
This paper presents an extensive and practical study of the estimation of stable channel bank shape and dimensions using the maximum entropy principle. The transverse slope (St) distribution of threshold channel bank cross-sections satisfies the properties of the probability space. The entropy of St is subject to two constraint conditions, and the principle of maximum entropy must be applied to find the least biased probability distribution. Accordingly, the Lagrange multiplier (λ) as a critical parameter in the entropy equation is calculated numerically based on the maximum entropy principle. The main goal of the present paper is the investigation of the hydraulic parameters influence governing the mean transverse slope
Hossein Bonakdari; Azadeh Gholami; Amir Mosavi; Amin Kazemian-Kale-Kale; Isa Ebtehaj; Amir Azimi. A Novel Comprehensive Evaluation Method for Estimating the Bank Profile Shape and Dimensions of Stable Channels Using the Maximum Entropy Principle. Entropy 2020, 22, 1218 .
AMA StyleHossein Bonakdari, Azadeh Gholami, Amir Mosavi, Amin Kazemian-Kale-Kale, Isa Ebtehaj, Amir Azimi. A Novel Comprehensive Evaluation Method for Estimating the Bank Profile Shape and Dimensions of Stable Channels Using the Maximum Entropy Principle. Entropy. 2020; 22 (11):1218.
Chicago/Turabian StyleHossein Bonakdari; Azadeh Gholami; Amir Mosavi; Amin Kazemian-Kale-Kale; Isa Ebtehaj; Amir Azimi. 2020. "A Novel Comprehensive Evaluation Method for Estimating the Bank Profile Shape and Dimensions of Stable Channels Using the Maximum Entropy Principle." Entropy 22, no. 11: 1218.
Real-time monitoring of river water quality is at the forefront of a proactive urban water management strategy to meet the global challenge of vital freshwater resource sustainability. The concentration of dissolved oxygen (DO) is a primary indicator of the health state of the aquatic habitats, and its modeling is crucial for river water quality management. This paper investigates the importance of the choices of different techniques for preprocessing and stochastic modeling for developing a simple and reliable linear stochastic model for forecasting DO in urban rivers. We describe several methods of evaluation, preprocessing, and modeling for the DO parameter time series in the Credit River, Ontario, Canada, to achieve the optimum data preprocessing and input selection techniques and consequently obtain the optimum performance of the stochastic models as an effective river management tool. The Manly normalization and standardization (Std) methods were chosen for preprocessing the time series. Modeling the preprocessed time series using the stochastic autoregressive integrated moving average (ARIMA) model resulted in very accurate forecasts with a negligible difference from sole normalization and spectral analysis (Sf) methods.
Stephen Stajkowski; Mohammad Zeynoddin; Hani Farghaly; Bahram Gharabaghi; Hossein Bonakdari. A Methodology for Forecasting Dissolved Oxygen in Urban Streams. Water 2020, 12, 2568 .
AMA StyleStephen Stajkowski, Mohammad Zeynoddin, Hani Farghaly, Bahram Gharabaghi, Hossein Bonakdari. A Methodology for Forecasting Dissolved Oxygen in Urban Streams. Water. 2020; 12 (9):2568.
Chicago/Turabian StyleStephen Stajkowski; Mohammad Zeynoddin; Hani Farghaly; Bahram Gharabaghi; Hossein Bonakdari. 2020. "A Methodology for Forecasting Dissolved Oxygen in Urban Streams." Water 12, no. 9: 2568.
Measurement and prediction of wastewater quality parameters are crucial for evaluating the risk to the receiving waters. This study presents new methods for the identification of outlier data and smoothing as an effective pre-processing technique prito to modelling. This new data processing method uses a combination of the autoregressive integrated moving average (ARIMA) model and -the adaptive neuro fuzzy inference system with fuzzy C-means clustering (FCM) (ANFIS-FCM). These new pre-processing methodsare compared to previously employed non-linear approaches for modelling of wastewater influent/effluent 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD) and total suspended solids (TSS). Linear modelling of each parameter, 242 linear models, were investigated, and a linear model for each parameter was selected. The results of the non-linear models led to an acceptable prediction for qualitative parameters so that the high coefficient of determination (R2) was observed for the influent and effluent BOD and TSS, respectively. The range of the R2 for all models was recorded as 0.8–0.87 and 0.83–0.89, respectively. By a combination of the linear and non-linear mothods a hybrid model was introduced. The proposed hybrid model for the influent BOD with the highest correlation between the observed and predicted values, and limited scattering was identified as the optimal model (R2 = 0.95). The use of hybrid models to predict wastewater quality parameters improved the performance and efficiency of the models. In addition, a comparison of the hybrid model with the recently developed models in the literature indicates that the developed ARIMA-ANFIS-FCM outperformed other models.
Khadije Lotfi; Hossein Bonakdari; Isa Ebtehaj; Robert Delatolla; Ali Akbar Zinatizadeh; Bahram Gharabaghi. A novel stochastic wastewater quality modeling based on fuzzy techniques. Journal of Environmental Health Science and Engineering 2020, 18, 1099 -1120.
AMA StyleKhadije Lotfi, Hossein Bonakdari, Isa Ebtehaj, Robert Delatolla, Ali Akbar Zinatizadeh, Bahram Gharabaghi. A novel stochastic wastewater quality modeling based on fuzzy techniques. Journal of Environmental Health Science and Engineering. 2020; 18 (2):1099-1120.
Chicago/Turabian StyleKhadije Lotfi; Hossein Bonakdari; Isa Ebtehaj; Robert Delatolla; Ali Akbar Zinatizadeh; Bahram Gharabaghi. 2020. "A novel stochastic wastewater quality modeling based on fuzzy techniques." Journal of Environmental Health Science and Engineering 18, no. 2: 1099-1120.
This paper proposes a model based on gene expression programming for predicting discharge coefficient of triangular labyrinth weirs. The parameters influencing discharge coefficient prediction were first examined and presented as crest height ratio to the head over the crest of the weir (p/y), crest length of water to channel width (L/W), crest length of water to the head over the crest of the weir (L/y), Froude number (F = V/√(gy)) and vertex angle (θ) dimensionless parameters. Different models were then presented using sensitivity analysis in order to examine each of the dimensionless parameters presented in this study. In addition, an equation was presented through the use of nonlinear regression (NLR) for the purpose of comparison with Gene Expression Programming (GEP). The results of the studies conducted by using different statistical indexes indicated that GEP is more capable than NLR. This is to the extent that GEP predicts discharge coefficient with an average relative error of approximately 2.5% in such manner that the predicted values have less than 5% relative error in the worst model.
Hossein Bonakdari; Isa Ebtehaj; Bahram Gharabaghi; Ali Sharifi; Amir Mosavi. Prediction of Discharge Capacity of Labyrinth Weir with Gene Expression Programming. Advances in Intelligent Systems and Computing 2020, 202 -217.
AMA StyleHossein Bonakdari, Isa Ebtehaj, Bahram Gharabaghi, Ali Sharifi, Amir Mosavi. Prediction of Discharge Capacity of Labyrinth Weir with Gene Expression Programming. Advances in Intelligent Systems and Computing. 2020; ():202-217.
Chicago/Turabian StyleHossein Bonakdari; Isa Ebtehaj; Bahram Gharabaghi; Ali Sharifi; Amir Mosavi. 2020. "Prediction of Discharge Capacity of Labyrinth Weir with Gene Expression Programming." Advances in Intelligent Systems and Computing , no. : 202-217.
Laboratory experiments were conducted to investigate the hydraulics and discharge characteristics of sharp-crested weir culverts with downstream ramps for hydraulically smooth wall boundary and for free, partially submerged, and fully submerged-flow conditions. Four weir-culvert models were tested with different weir heights, ramp lengths, and culvert heights. The partially submerged-flow conditions were created when a slight increase in tailwater caused the headwater to rise due to partial submergence of the culvert. In this flow regime, supercritical flow over the ramp interacted with culvert outflow and this flow regime was classified as Supercritical Jet Interaction Regime or SJIR. Once tailwater reached the weir crest, the weir flow regime became submerged indicating fully submerged-flow condition. Based on the variations of water surface profiles, submerged flows were further classified into Surface Jump Regime (SJR), Surface Wave Regime (SWR), and Deeply Submerged Regime (DSR) and the boundaries between each flow regime were defined by regime plots. To predict discharge of weir culverts with downstream ramps, variations of discharge coefficient with weir geometry and flow regimes were studied and empirical formulations were developed. In addition, free and partially submerged flow discharges were predicted by implementing the interaction factor. It was found that the interaction factor was independent of the Froude number in SJR, while it correlated with the Froude number in SJIR. Under submerged-flow conditions, evaluation of the discharge characteristics of the proposed weir-culvert model showed correlations with water surface flow regimes and a three-stage prediction formula was proposed for estimation of submerged flow. The energy losses over weir-culvert models were also calculated and it decreased with submergence.
Saeed Salehi; Amir H. Azimi; Hossein Bonakdari. Hydraulics of sharp-crested weir culverts with downstream ramps in free-flow, partially, and fully submerged-flow conditions. Irrigation Science 2020, 39, 191 -207.
AMA StyleSaeed Salehi, Amir H. Azimi, Hossein Bonakdari. Hydraulics of sharp-crested weir culverts with downstream ramps in free-flow, partially, and fully submerged-flow conditions. Irrigation Science. 2020; 39 (2):191-207.
Chicago/Turabian StyleSaeed Salehi; Amir H. Azimi; Hossein Bonakdari. 2020. "Hydraulics of sharp-crested weir culverts with downstream ramps in free-flow, partially, and fully submerged-flow conditions." Irrigation Science 39, no. 2: 191-207.
Accurate forecast of the magnitude and timing of the flood peak river discharge and the extent of inundated areas during major storm events are a vital component of early warning systems around the world that are responsible for saving countless lives every year. This study assesses the forecast accuracy of two different linear and non-linear approaches to predict the daily river discharge. A new linear stochastic method is produced by evaluating a detailed comparison between three pre-processing approaches, differencing, standardization, spectral analysis, and trend removal. Daily river discharge values of the Bow River with strong seasonal and non-seasonal correlations located in Alberta, Canada were utilized in this study. The stochastic term for this daily flow time series is calculated with an auto-regressive integrated moving average. We found that seasonal differencing is the best stationarization method for periodic effect elimination. Moreover, the proposed non-linear Group Method of Data Handling (GMDH) model could overcome the known accuracy limitations of the classical GMDH models that use only two inputs in each neuron from the adjacent layer. The proposed new non-linear GMDH-based method (named GS-GMDH) can improve the structure of the classical linear GMDH. The GS-GMDH model produced the most accurate forecasts in the Bow River case study with statistical indices such as the coefficient of determination and Nash-Sutcliffe for the daily discharge time series higher than 97% and relative error less than 6%. Finally, an explicit equation for estimation of the daily discharge of the Bow River is developed using the proposed GS-GMDH model to showcase the practical application of the new method in flood forecasting and management.
Hossein Bonakdari; Andrew D. Binns; Bahram Gharabaghi. A Comparative Study of Linear Stochastic with Nonlinear Daily River Discharge Forecast Models. Water Resources Management 2020, 34, 3689 -3708.
AMA StyleHossein Bonakdari, Andrew D. Binns, Bahram Gharabaghi. A Comparative Study of Linear Stochastic with Nonlinear Daily River Discharge Forecast Models. Water Resources Management. 2020; 34 (11):3689-3708.
Chicago/Turabian StyleHossein Bonakdari; Andrew D. Binns; Bahram Gharabaghi. 2020. "A Comparative Study of Linear Stochastic with Nonlinear Daily River Discharge Forecast Models." Water Resources Management 34, no. 11: 3689-3708.
Many variables in our environment are impacted by alternations in soil temperature (Tsoc). Fluctuations in Tsoc alter gas absorption and emission capacity of the soil, which mutually influence climate changes. Hence, providing viable methodologies of modeling Tsoc is of great importance. Since thorough investigations of time series structure are mostly neglected, and appropriate pre-processing methods are not applied to them, the direct use of nonlinear methods for soil temperature forecasting has become more common than other approaches. In this study, unlike most of the existing studies that estimate Tsoc as a variables based approach, soil temperature is forecasted using a linear stochastic based methodology with sufficient knowledge of time series structure. With this methodology, the components of the Tsoc time series are determined in order to perform stochastic modeling using Holt-Winters advanced exponential smoothing. The results of this procedure are compared with two stochastic techniques based on seasonal standardization (stdω) and spectral analysis (sf) in terms of six Tsoc time series at each station. The Tsoc. time series applied in the current study were measured at Bandar Abbas and Kerman synoptic stations, Iran, at depths of 5, 10, 20, 30, 50, and 100 cm. Owing to the comparison of the applied techniques in different Tsoc time series analysis, the stochastic model with Holt-Winters advanced exponential smoothing (Bandar Abbas station: (R2% = 96.649, RMSE = 0.977, MAPE% = 1.963, AICc = −733.006; Kerman station: R2% = 92.416, RMSE = 1.806, MAPE% = 7.793, AICc = 360.410) outperformed the stdω and sf methods. In addition to comparing linear stochastic methods, the results of Tsoc modeling are compared with two powerful nonlinear methods. In an antecedent study on the mentioned sites, Nahvi et al. (2016) employed extreme learning machine (ELM) and self-adaptive evolutionary ELM (SaE-ELM) nonlinear techniques. A comparison of results indicates that the proposed methodology outperformed the nonlinear models (ELM and SaE-ELM). Indeed, the proposed linear based stochastic model not only enhanced forecasting accuracy but also demonstrated the capability of linear models in Tsoc time series forecasting. Concerning the comprehensibility and generality of the framework, it can apply to other stations with different climatological conditions.
Mohammad Zeynoddin; Isa Ebtehaj; Hossein Bonakdari. Development of a linear based stochastic model for daily soil temperature prediction: One step forward to sustainable agriculture. Computers and Electronics in Agriculture 2020, 176, 105636 .
AMA StyleMohammad Zeynoddin, Isa Ebtehaj, Hossein Bonakdari. Development of a linear based stochastic model for daily soil temperature prediction: One step forward to sustainable agriculture. Computers and Electronics in Agriculture. 2020; 176 ():105636.
Chicago/Turabian StyleMohammad Zeynoddin; Isa Ebtehaj; Hossein Bonakdari. 2020. "Development of a linear based stochastic model for daily soil temperature prediction: One step forward to sustainable agriculture." Computers and Electronics in Agriculture 176, no. : 105636.