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The present study aimed to model reconnaissance drought index (RDI) time series at three various time scales (i.e., RDI-6, RDI-9, RDI-12). Two weather stations located at Iran, namely Tehran and Dezful, were selected as the case study. First, support vector regression (SVR) was utilized as the standalone modeling technique. Then, hybrid models were implemented via coupling the standalone SVR with two bio-inspired-based techniques including firefly algorithm (FA) and whale optimization algorithm (WOA) as well as wavelet analysis (W). Accordingly, the hybrid SVR-FA, SVR-WOA, and W-SVR models were proposed. It is worth mentioning that six mother wavelets (i.e., Haar, Daubechies (db2, db4), Coifflet, Symlet, and Fejer-Korovkin) were employed in development of the hybrid W-SVR models. The performance of models was assessed through root mean square error (RMSE), mean absolute error (MAE), Willmott index (WI), and Nash-Sutcliffe efficiency (NSE). Generally, the implemented coupled models illustrated better results than the standalone SVR in modeling the RDI time series of studied locations. Besides, the Coifflet mother wavelet was found to be the best-performing wavelet. The most accurate results were achieved for RDI-12 modeling via the W-SVR utilizing db4(2) at Tehran station (RMSE = 0.253, MAE = 0.174, WI= 0.888, NSE = 0.934) and Coifflet(2) at Dezful station (RMSE = 0.301, MAE = 0.166, WI= 0.910, NSE = 0.936). As a result, the hybrid models developed in the current study, specifically W-SVR ones, can be proposed as suitable alternatives to the single SVR.
Farshad Ahmadi; Saeid Mehdizadeh; Babak Mohammadi. Development of Bio-Inspired- and Wavelet-Based Hybrid Models for Reconnaissance Drought Index Modeling. Water Resources Management 2021, 1 -21.
AMA StyleFarshad Ahmadi, Saeid Mehdizadeh, Babak Mohammadi. Development of Bio-Inspired- and Wavelet-Based Hybrid Models for Reconnaissance Drought Index Modeling. Water Resources Management. 2021; ():1-21.
Chicago/Turabian StyleFarshad Ahmadi; Saeid Mehdizadeh; Babak Mohammadi. 2021. "Development of Bio-Inspired- and Wavelet-Based Hybrid Models for Reconnaissance Drought Index Modeling." Water Resources Management , no. : 1-21.
Soil cation exchange capacity (CEC) strongly influences the chemical, physical, and biological properties of soil. As the direct measurement of the CEC is difficult, costly, and time-consuming, the indirect estimation of CEC from chemical and physical parameters has been considered as an alternative method by researchers. Accordingly, in this study, a new hybrid model using a support vector machine (SVM), coupling with particle swarm optimization (PSO), and integrated invasive weed optimization (IWO) algorithm is developed for estimating the soil CEC. The physical and chemical data (i.e., clay, organic matter (OM), and pH) from two field sites of Taybad and Semnan in Iran were used for validating the new proposed approach. The ability of the proposed model (SVM-PSOIWO) was compared with the individual model (SVM) and the hybrid model (SVM-PSO). The results of the SVM-PSOIWO model were also compared with those of existing studies. Different performance evaluation criteria such as RMSE, R 2, MAE, RRMSE, and MAPE, Box plots, and scatter diagrams were used to test the ability of the proposed models for estimation of the CEC values. The results showed that the SVM-PSOIWO model with the RMSE (R 2) of 0.229 Cmol + kg−1 (0.924) was better than those of the SVM and SVM-PSO models with the RMSE (R 2) of 0.335 Cmol + kg−1 (0.843) and 0.279 Cmol + kg−1 (0.888), respectively. Furthermore, the ability of the SVM-PSOIWO model compared with existing studies, which used the genetic expression programming, artificial neural network, and multivariate adaptive regression splines models. The results indicated that the SVM-PSOIWO model estimates the CEC more accurately than existing studies.
Samad Emamgholizadeh; Babak Mohammadi. New hybrid nature-based algorithm to integration support vector machine for prediction of soil cation exchange capacity. Soft Computing 2021, 1 -14.
AMA StyleSamad Emamgholizadeh, Babak Mohammadi. New hybrid nature-based algorithm to integration support vector machine for prediction of soil cation exchange capacity. Soft Computing. 2021; ():1-14.
Chicago/Turabian StyleSamad Emamgholizadeh; Babak Mohammadi. 2021. "New hybrid nature-based algorithm to integration support vector machine for prediction of soil cation exchange capacity." Soft Computing , no. : 1-14.
Accurate and timely monitoring of streamflow and its variation is crucial for water resources management in watersheds. This study aimed at evaluating the performance of two process-driven conceptual rainfall-runoff models (HBV: Hydrologiska Byråns Vattenbalansavdelning, and NRECA: Non Recorded Catchment Areas) and seven hybrid models based on three artificial intelligence (AI) methods (adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), and group method of data handling (GMDH)) in simulating streamflow in four river basins in Indonesia. HBV and NRECA were developed based on precipitation data. Various combinations of 1-month lagged precipitation data together with outputs of HBV and NRECA were used for developing ANFIS and SVM models, and the best results of ANFIS and SVM formed the inputs to GMDH. Results showed that AI-based hybrid models have generally led to more accurate streamflow estimates compared with HBV and NRECA, and the GMDH model had the best performance at Cipero, Kedungdowo, Notog, and Sukowati stations, with RMSEs of 12.21, 6.07, 20.35, and 24.2 m3 s−1, respectively. More accurate estimation of peak values in training set at Cipero and Sukowati stations, and in both training and testing sets at Kedungdowo station was another advantage of GMDH. Hybrid models based on AI methods can be suitable alternatives to hydrological models, particularly in watersheds where there is a lack of measured data (e.g. climatic parameters, land cover-plant growth data, soil data, stream conditions, and properties of groundwater aquifers), provided that appropriate inputs are used.
Babak Mohammadi; Roozbeh Moazenzadeh; Kevin Christian; Zheng Duan. Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models. Environmental Science and Pollution Research 2021, 1 -17.
AMA StyleBabak Mohammadi, Roozbeh Moazenzadeh, Kevin Christian, Zheng Duan. Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models. Environmental Science and Pollution Research. 2021; ():1-17.
Chicago/Turabian StyleBabak Mohammadi; Roozbeh Moazenzadeh; Kevin Christian; Zheng Duan. 2021. "Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models." Environmental Science and Pollution Research , no. : 1-17.
Solar radiation plays a pivotal role in the energy balance at the Earth's surface, evaporation, snow melting, water requirements of plants, and hydrological control of catchments. In this work, performance of ERA-Interim (a reanalysis dataset) was examined to estimate solar radiation at Ahvaz, BandarAbbas, and Kermanshah weather stations representing the even spatial distribution over Iran using eight empirical models and an artificial intelligence-based model (SVM: Support Vector Machine). In the calibration set, SVM exhibited the best performance with RMSEs of 249, 299 and 437 J.cm−2.day−1 at the aforementioned stations, respectively. In validation set, SVM reduced the errors in the estimates of solar radiation by 2.5 and 7.3 percent compared to the best empirical model at Ahvaz station (Abdallah model, RMSE = 242 J.cm−2.day−1) and Kermanshah station (Angstrom-Prescott model, RMSE = 315 J.cm−2.day−1), respectively. During the validation at BandarAbbas station, Bahel and Abdallah model (RMSE = 309 J.cm−2.day−1), Angstrom-Prescott model (RMSE = 310 J.cm−2.day−1) and SVM (RMSE = 312 J.cm−2.day−1) showed a relatively similar performance. The results also showed that the ERA-Interim dataset can be a comparatively suitable alternative to some of the empirical models, where radiation or the input parameters of empirical models are not directly measured, with RMSEs of 382.81, 320.82 and 414.1 J.cm−2.day−1 at Ahvaz, BandarAbbas, and Kermanshah stations, respectively (in validation phase); although its error rates are significant compared with the SVM model, and substituting it for artificial intelligence-based models is not recommended.
Babak Mohammadi; Roozbeh Moazenzadeh; Quoc Bao Pham; Nadhir Al-Ansari; Khalil Ur Rahman; Duong Tran Anh; Zheng Duan. Application of ERA-Interim, empirical models, and an artificial intelligence-based model for estimating daily solar radiation. Ain Shams Engineering Journal 2021, 1 .
AMA StyleBabak Mohammadi, Roozbeh Moazenzadeh, Quoc Bao Pham, Nadhir Al-Ansari, Khalil Ur Rahman, Duong Tran Anh, Zheng Duan. Application of ERA-Interim, empirical models, and an artificial intelligence-based model for estimating daily solar radiation. Ain Shams Engineering Journal. 2021; ():1.
Chicago/Turabian StyleBabak Mohammadi; Roozbeh Moazenzadeh; Quoc Bao Pham; Nadhir Al-Ansari; Khalil Ur Rahman; Duong Tran Anh; Zheng Duan. 2021. "Application of ERA-Interim, empirical models, and an artificial intelligence-based model for estimating daily solar radiation." Ain Shams Engineering Journal , no. : 1.
Palmer Drought Severity Index (PDSI) is known as a robust agricultural drought index since it considers the water balance conditions in the soil. It has been widely used as a reference index for monitoring agricultural drought. In this study, the PDSI time series were calculated for nine synoptic stations to monitor agricultural drought in semi-arid region located at Zagros mountains of Iran. Autoregressive Moving Average (ARMA) was used as the stochastic model while Radial Basis Function Neural Network (RBFNN) and Support Vector Machine (SVM) were applied as Machine Learning (ML)-based techniques. According to the time series analysis of PDSI, for the driest months the most PDSI drought events are normal drought and mild drought conditions. As an innovation, Dragonfly Algorithm (DA) was used in this study to optimize the SVM’s parameters, called as the hybrid SVM-DA model. It is worthy to mention that the hybrid SVM-DA is developed as a meta-innovative model for the first time in hydrological studies. The novel hybrid SVM-DA paradigm could improve the SVM’s accuracy up to 29% in predicting PDSI and therefore was found as the superior model. The best statistics for this model were obtained as Root Mean Squared Error (RMSE) = 0.817, Normalized RMSE (NRMSE) = 0.097, Wilmott Index (WI) = 0.940, and R = 0.889. The Mean Absolute Error values of the PDSI predictions via the novel SVM-DA model were under 0.6 for incipient drought, under 0.7 for mild and moderate droughts. In general, the error values in severe and extreme droughts were more than the other classes; however, the hybrid SVM-DA was the best-performing model in most of the cases.
Pouya Aghelpour; Babak Mohammadi; Saeid Mehdizadeh; Hadigheh Bahrami-Pichaghchi; Zheng Duan. A novel hybrid dragonfly optimization algorithm for agricultural drought prediction. Stochastic Environmental Research and Risk Assessment 2021, 1 -19.
AMA StylePouya Aghelpour, Babak Mohammadi, Saeid Mehdizadeh, Hadigheh Bahrami-Pichaghchi, Zheng Duan. A novel hybrid dragonfly optimization algorithm for agricultural drought prediction. Stochastic Environmental Research and Risk Assessment. 2021; ():1-19.
Chicago/Turabian StylePouya Aghelpour; Babak Mohammadi; Saeid Mehdizadeh; Hadigheh Bahrami-Pichaghchi; Zheng Duan. 2021. "A novel hybrid dragonfly optimization algorithm for agricultural drought prediction." Stochastic Environmental Research and Risk Assessment , no. : 1-19.
Abu Reza Md. Towfiqul Islam; Swapan Talukdar; Susanta Mahato; Sk Ziaul; Kutub Uddin Eibek; Shumona Akhter; Quoc Bao Pham; Babak Mohammadi; Firoozeh Karimi; Nguyen Thi Thuy Linh. Correction to: Machine learning algorithm-based risk assessment of riparian wetlands in Padma River basin of Northwest Bangladesh. Environmental Science and Pollution Research 2021, 1 -1.
AMA StyleAbu Reza Md. Towfiqul Islam, Swapan Talukdar, Susanta Mahato, Sk Ziaul, Kutub Uddin Eibek, Shumona Akhter, Quoc Bao Pham, Babak Mohammadi, Firoozeh Karimi, Nguyen Thi Thuy Linh. Correction to: Machine learning algorithm-based risk assessment of riparian wetlands in Padma River basin of Northwest Bangladesh. Environmental Science and Pollution Research. 2021; ():1-1.
Chicago/Turabian StyleAbu Reza Md. Towfiqul Islam; Swapan Talukdar; Susanta Mahato; Sk Ziaul; Kutub Uddin Eibek; Shumona Akhter; Quoc Bao Pham; Babak Mohammadi; Firoozeh Karimi; Nguyen Thi Thuy Linh. 2021. "Correction to: Machine learning algorithm-based risk assessment of riparian wetlands in Padma River basin of Northwest Bangladesh." Environmental Science and Pollution Research , no. : 1-1.
Solar radiation (Rs) is one of the main parameters controlling the energy balance at the Earth’s surface and plays a major role in evapotranspiration and plant growth, snow melting, and environmental studies. This work aimed at evaluating the performance of seven empirical models in estimating daily solar radiation over 1990–2004 (calibration) and 2004–2010 (validation) at 13 Peruvian meteorological stations. With the same variables used in empirical models (temperature) as well as two other parameters, namely precipitation and relative humidity, new models were developed by multiple linear regression analysis (proposed models). In calibration of empirical models with the same variables, the lowest estimation errors were 227.1 and 236.3 J·cm−2·day−1 at Tacna and Puno stations, and the highest errors were 3958.4 and 3005.7 at San Ramon and Junin stations, respectively. The poorest-performing empirical models greatly overestimated Rs at most stations. The best performance of a proposed model (in terms of percentage of error reduction) was 73% compared to the average of all empirical models and 93% relative to the poorest result of empirical models, both at San Ramon station. According to root mean square errors (RMSEs) of proposed models, the worst and the best results are achieved at San Martin station (RMSE = 508.8 J·cm−2·day−1) and Tacna station (RMSE = 223.2 J·cm−2·day−1), respectively.
Babak Mohammadi; Roozbeh Moazenzadeh. Performance Analysis of Daily Global Solar Radiation Models in Peru by Regression Analysis. Atmosphere 2021, 12, 389 .
AMA StyleBabak Mohammadi, Roozbeh Moazenzadeh. Performance Analysis of Daily Global Solar Radiation Models in Peru by Regression Analysis. Atmosphere. 2021; 12 (3):389.
Chicago/Turabian StyleBabak Mohammadi; Roozbeh Moazenzadeh. 2021. "Performance Analysis of Daily Global Solar Radiation Models in Peru by Regression Analysis." Atmosphere 12, no. 3: 389.
Wetland risk assessment is a global concern especially in developing countries like Bangladesh. The present study explored the spatiotemporal dynamics of wetlands, prediction of wetland risk assessment. The wetland risk assessment was predicted based on ten selected parameters, such as fragmentation probability, distance to road, and settlement. We used M5P, random forest (RF), reduced error pruning tree (REPTree), and support vector machine (SVM) machine learning techniques for wetland risk assessment. The results showed that wetland areas at present are declining less than one-third of those in 1988 due to the construction of the dam at Farakka, which is situated at the upstream of the Padma River. The distance to the river and built-up area are the two most contributing drivers influencing the wetland risk assessment based on information gain ratio (InGR). The prediction results of machine learning models showed 64.48% of area by M5P, 61.75% of area by RF, 62.18% of area by REPTree, and 55.74% of area by SVM have been predicted as the high and very high-risk zones. The results of accuracy assessment showed that the RF outperformed than other models (area under curve: 0.83), followed by the SVM, M5P, and REPTree. Degradation of wetlands explored in this study demonstrated the negative effects on biodiversity. Therefore, to conserve and protect the wetlands, continuous monitoring of wetlands using high resolution satellite images, feeding with the ecological flow, confining built up area and agricultural expansion towards wetlands, and new wetland creation is essential for wetland management.
Abu Reza Md. Towfiqul Islam; Swapan Talukdar; Susanta Mahato; Sk Ziaul; Kutub Uddin Eibek; Shumona Akhter; Quoc Bao Pham; Babak Mohammadi; Firoozeh Karimi; Nguyen Thi Thuy Linh. Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh. Environmental Science and Pollution Research 2021, 1 -22.
AMA StyleAbu Reza Md. Towfiqul Islam, Swapan Talukdar, Susanta Mahato, Sk Ziaul, Kutub Uddin Eibek, Shumona Akhter, Quoc Bao Pham, Babak Mohammadi, Firoozeh Karimi, Nguyen Thi Thuy Linh. Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh. Environmental Science and Pollution Research. 2021; ():1-22.
Chicago/Turabian StyleAbu Reza Md. Towfiqul Islam; Swapan Talukdar; Susanta Mahato; Sk Ziaul; Kutub Uddin Eibek; Shumona Akhter; Quoc Bao Pham; Babak Mohammadi; Firoozeh Karimi; Nguyen Thi Thuy Linh. 2021. "Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh." Environmental Science and Pollution Research , no. : 1-22.
Precise monitoring of cyanobacteria concentration in water resources is a daunting task. The development of reliable tools to monitor this contamination is an important research topic in water resources management. Indirect methods such as chlorophyll-a determination, cell counting, and toxin measurement of the cyanobacteria are tedious, cumbersome, and often lead to inaccurate results. The quantity of phycocyanin (PC) pigment is considered more appropriate for cyanobacteria monitoring. Traditional approaches for PC estimation are time-consuming, expensive, and require high expertise. Recently, some studies have proposed the application of artificial intelligence (AI) techniques to predict the amount of PC concentration. Nonetheless, most of these researches are limited to standalone modeling schemas such as artificial neural network (ANN), multilayer perceptron (MLP), and support vector machine (SVM). The independent schema provides imprecise results when faced with highly nonlinear systems and data uncertainties resulting from environmental disturbances. To alleviate the limitations of the existing models, this study proposes the first application of a hybrid AI model that integrates the potentials of relevance vector machine (RVM) and flower pollination algorithm (RVM-FPA) to predict the PC concentration in water resources. The performance of the hybrid model is compared with the standalone RVM model. The prediction performance of the proposed models was evaluated at two stations (stations 508 and 478) using different statistical and graphical performance evaluation methods. The results showed that the hybrid models exhibited higher performance at both stations compared to the standalone RVM model. The proposed hybrid RVM-FPA can therefore serve as a reliable predictive tool for PC concentration in water resources.
Quoc Bao Pham; Saad Sh. Sammen; Sani Isa Abba; Babak Mohammadi; Shamsuddin Shahid; Rabiu Aliyu Abdulkadir. A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation. Environmental Science and Pollution Research 2021, 28, 32564 -32579.
AMA StyleQuoc Bao Pham, Saad Sh. Sammen, Sani Isa Abba, Babak Mohammadi, Shamsuddin Shahid, Rabiu Aliyu Abdulkadir. A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation. Environmental Science and Pollution Research. 2021; 28 (25):32564-32579.
Chicago/Turabian StyleQuoc Bao Pham; Saad Sh. Sammen; Sani Isa Abba; Babak Mohammadi; Shamsuddin Shahid; Rabiu Aliyu Abdulkadir. 2021. "A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation." Environmental Science and Pollution Research 28, no. 25: 32564-32579.
Precipitation deficit can affect different natural resources such as water, soil, rivers and plants, and cause meteorological, hydrological and agricultural droughts. Multivariate drought indexes can theoretically show the severity and weakness of various drought types simultaneously. This study introduces an approach for forecasting joint deficit index (JDI) and multivariate standardized precipitation index (MSPI) by using machine–learning methods and entropy theory. JDI and MSPI were calculated for the 1–12 months’ time window (JDI1–12 and MSPI1–12), using monthly precipitation data. The methods implemented for forecasting are group method of data handling (GMDH), generalized regression neural network (GRNN), least squared support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS) and ANFIS optimized with three heuristic optimization algorithms, differential evolution (DE), genetic algorithm (GA) and particle swarm optimization (PSO) as meta-innovative methods (ANFIS-DE, ANFIS-GA and ANFIS-PSO). Monthly precipitation, monthly temperature and previous amounts of the index’s values were used as inputs to the models. Data from 10 synoptic stations situated in the widest climatic zone of Iran (extra arid-cold climate) were employed. Optimal model inputs were selected by gamma test and entropy theory. The evaluation results, which were given using mean absolute error (MAE), root mean squared error (RMSE) and Willmott index (WI), show that the machine learning and meta-innovative models can present acceptable forecasts of general drought’s conditions. The algorithms DE, GA and PSO, could improve the ANFIS’s performance by 39.4%, 38.7% and 22.6%, respectively. Among all the applied models, the GMDH shows the best forecasting accuracy with MAE = 0.280, RMSE = 0.374 and WI = 0.955. In addition, the models could forecast MSPI better than JDI in the majority of cases (stations). Among the two methods used to select the optimal inputs, it is difficult to select one as a better input selector, but according to the results, more attention can be paid to entropy theory in drought studies.
Pouya Aghelpour; Babak Mohammadi; Seyed Mostafa Biazar; Ozgur Kisi; Zohreh Sourmirinezhad. A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods. ISPRS International Journal of Geo-Information 2020, 9, 701 .
AMA StylePouya Aghelpour, Babak Mohammadi, Seyed Mostafa Biazar, Ozgur Kisi, Zohreh Sourmirinezhad. A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods. ISPRS International Journal of Geo-Information. 2020; 9 (12):701.
Chicago/Turabian StylePouya Aghelpour; Babak Mohammadi; Seyed Mostafa Biazar; Ozgur Kisi; Zohreh Sourmirinezhad. 2020. "A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods." ISPRS International Journal of Geo-Information 9, no. 12: 701.
Reference evapotranspiration (ET0) is one of the most important parameters, which is required in many fields such as hydrological, agricultural, and climatological studies. Therefore, its estimation via reliable and accurate techniques is a necessity. The present study aims to estimate the monthly ET0 time series of six stations located in Iran. To achieve this objective, gene expression programming (GEP) and support vector regression (SVR) were used as standalone models. A novel hybrid model was then introduced through coupling the classical SVR with an optimization algorithm, namely intelligent water drops (IWD) (i.e., SVR−IWD). Two various types of scenarios were considered, including the climatic data- and antecedent ET0 data-based patterns. In the climatic data-based models, the effective climatic parameters were recognized by using two pre-processing techniques consisting of τ Kendall and entropy. It is worthy to mention that developing the hybrid SVR-IWD model as well as utilizing the τ Kendall and entropy approaches to discern the most influential weather parameters on ET0 are the innovations of current research. The results illustrated that the applied pre-processing methods introduced different climatic inputs to feed the models. The overall results of present study revealed that the proposed hybrid SVR-IWD model outperformed the standalone SVR one under both the considered scenarios when estimating the monthly ET0. In addition to the mentioned models, two types of empirical equations were also used including the Hargreaves−Samani (H−S) and Priestley−Taylor (P−T) in their original and calibrated versions. It was concluded that the calibrated versions showed superior performances compared to their original ones.
Farshad Ahmadi; Saeid Mehdizadeh; Babak Mohammadi; Quoc Bao Pham; Thi Ngoc Canh Doan; Ngoc Duong Vo. Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation. Agricultural Water Management 2020, 244, 106622 .
AMA StyleFarshad Ahmadi, Saeid Mehdizadeh, Babak Mohammadi, Quoc Bao Pham, Thi Ngoc Canh Doan, Ngoc Duong Vo. Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation. Agricultural Water Management. 2020; 244 ():106622.
Chicago/Turabian StyleFarshad Ahmadi; Saeid Mehdizadeh; Babak Mohammadi; Quoc Bao Pham; Thi Ngoc Canh Doan; Ngoc Duong Vo. 2020. "Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation." Agricultural Water Management 244, no. : 106622.
River suspended sediment load (SSL) estimation is of importance in water resources engineering and hydrological modeling. In this study, a novel hybrid approach is recommended for SSL estimation in which multi-layer perceptron (MLP) is hybridized with particle swarm optimization (PSO) and then, integrated with differential evolution algorithm (DE) called as MLP-PSODE. The hybrid MLP-PSODE model is implemented to model the SSL of Mahabad river located at northwest of Iran. For the sake of examination of the MLP-PSODE model performance, several techniques including multi-layer perceptron (MLP), multi-layer perceptron integrated with particle swarm optimization (MLP-PSO), radial basis function (RBF) and support vector machine (SVM) are selected as benchmarks. For this purpose, five different scenarios are considered for the modeling. The results indicated that the new hybrid model of MLP-PSODE is successful in estimating SSL by considering single input of discharge (Q) with high accuracy as compared to its alternatives with RMSE = 1794.4 ton·day−1, MAPE = 41.50% and RRMSE = 107.09%, which were much lower than those of MLP based model with RMSE = 3133.7 ton·day−1, MAPE = 121.40% and RRMSE = 187.03%. The developed MLP-PSODE model, not only outperforms its counterparts in terms of accuracy in extreme values estimation, but also it is found as a parsimonious model that incorporates lower number of input parameters in its structure for SSL estimation.
Babak Mohammadi; Yiqing Guan; Roozbeh Moazenzadeh; Mir Jafar Sadegh Safari. Implementation of hybrid particle swarm optimization-differential evolution algorithms coupled with multi-layer perceptron for suspended sediment load estimation. CATENA 2020, 198, 105024 .
AMA StyleBabak Mohammadi, Yiqing Guan, Roozbeh Moazenzadeh, Mir Jafar Sadegh Safari. Implementation of hybrid particle swarm optimization-differential evolution algorithms coupled with multi-layer perceptron for suspended sediment load estimation. CATENA. 2020; 198 ():105024.
Chicago/Turabian StyleBabak Mohammadi; Yiqing Guan; Roozbeh Moazenzadeh; Mir Jafar Sadegh Safari. 2020. "Implementation of hybrid particle swarm optimization-differential evolution algorithms coupled with multi-layer perceptron for suspended sediment load estimation." CATENA 198, no. : 105024.
Lakes have an important role in storing water for drinking, producing hydroelectric power, and environmental, agricultural, and industrial uses. In order to optimize the use of lakes, precise prediction of the lake water level (LWL) is a main issue in water resources management. Due to the existence of nonlinear relations, uncertainty, and characteristics of the time series variables, the exact prediction of the lake water level is difficult. In this study the hybrid support vector regression (SVR) and the grey wolf algorithm (GWO) are used to predict lake water level fluctuations. Also, three types of data preprocessing methods, namely Principal component analysis, Random forest, and Relief algorithm were used for finding the best input variables for prediction LWL by the SVR and SVR-GWO models. Before the LWL simulation on monthly time step using the hybrid model, an evolutionary approach based on different monthly lags was conducted for determining the best mask of the input variables. Results showed that based on the random forest method, the best scenario of the inputs was Xt−1, Xt−2, Xt−3, Xt−4 for the SVR-GWO model. Also, the performance of the SVR-GWO model indicated that it could simulate the LWL with acceptable accuracy (with RMSE = 0.08 m, MAE = 0.06 m, and R2 = 0.96).
Babak Mohammadi; Yiqing Guan; Pouya Aghelpour; Samad Emamgholizadeh; Ramiro Pillco Zolá; Danrong Zhang. Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer Algorithm. Water 2020, 12, 3015 .
AMA StyleBabak Mohammadi, Yiqing Guan, Pouya Aghelpour, Samad Emamgholizadeh, Ramiro Pillco Zolá, Danrong Zhang. Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer Algorithm. Water. 2020; 12 (11):3015.
Chicago/Turabian StyleBabak Mohammadi; Yiqing Guan; Pouya Aghelpour; Samad Emamgholizadeh; Ramiro Pillco Zolá; Danrong Zhang. 2020. "Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer Algorithm." Water 12, no. 11: 3015.
Snow is one of the essential factors in hydrology, freshwater resources, irrigation, travel, pastimes, floods, avalanches, and vegetation. In this study, the snow cover of the northern and southern slopes of Alborz Mountains in Iran was investigated by considering two issues: (1) Estimating the snow cover area and the (2) effects of droughts on snow cover. The snow cover data were monitored by images obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The meteorological data (including the precipitation, minimum and maximum temperature, global solar radiation, relative humidity, and wind velocity) were prepared by a combination of National Centers for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR) points and meteorological stations. The data scale was monthly and belonged to the 2000–2014 period. In the first part of the study, snow cover estimation was conducted by Multiple Linear Regression (MLR), Least Square Support Vector Machine (LSSVM), Group Method of Data Handling (GMDH), Multilayer Perceptron (MLP), and MLP with Grey Wolf Optimization (MLP-GWO) models. The most accurate estimations were produced by the MLP-GWO and GMDH models. The models produced better snow cover estimations for the northern slope compared to the southern slope. The GWO improved the MLP’s accuracy by 10.7%. In the second part, seven drought indices, including the Palmer Drought Severity Index (PDSI), Bahlme–Mooley Drought Index (BMDI), Standardized Precipitation Index (SPI), Multivariate Standardized Precipitation Index (MSPI), Modified Standardized Precipitation Index (SPImod), Joint Deficit Index (JDI), and Standardized Precipitation-Evapotranspiration Index (SPEI) were calculated for both slopes. The results showed that the effects of a drought event on the snow cover area would remain up to 5 (or 6) months in the region. The highest impact of drought appears after two months in the snow cover area, and the drought index most related to snow cover variations is the 2–month time window of SPI (SPI2). The results of both subjects were promising and the methods can be examined in other snowy areas of the world.
Pouya Aghelpour; Yiqing Guan; Hadigheh Bahrami-Pichaghchi; Babak Mohammadi; Ozgur Kisi; Danrong Zhang. Using the MODIS Sensor for Snow Cover Modeling and the Assessment of Drought Effects on Snow Cover in a Mountainous Area. Remote Sensing 2020, 12, 3437 .
AMA StylePouya Aghelpour, Yiqing Guan, Hadigheh Bahrami-Pichaghchi, Babak Mohammadi, Ozgur Kisi, Danrong Zhang. Using the MODIS Sensor for Snow Cover Modeling and the Assessment of Drought Effects on Snow Cover in a Mountainous Area. Remote Sensing. 2020; 12 (20):3437.
Chicago/Turabian StylePouya Aghelpour; Yiqing Guan; Hadigheh Bahrami-Pichaghchi; Babak Mohammadi; Ozgur Kisi; Danrong Zhang. 2020. "Using the MODIS Sensor for Snow Cover Modeling and the Assessment of Drought Effects on Snow Cover in a Mountainous Area." Remote Sensing 12, no. 20: 3437.
Air temperature is a vital meteorological variable required in many applications, such as agricultural and soil sciences, meteorological and climatological studies, etc. Given the importance of this variable, this study seeks to estimate minimum (Tmin), maximum (Tmax), and mean (T) air temperatures by applying a linear autoregressive (AR) time series model and then developing a hybrid model by means of coupling the AR and a non-linear time series model, namely autoregressive conditional heteroscedasticity (ARCH). Hence, the hybrid AR-ARCH model was tested. To that end, the Tmin, Tmax, and T data from 1986 to 2015 at two weather stations located in Northwestern Iran were used for both daily and monthly time scales. The results showed that the hybrid time series model (i.e., AR-ARCH) performed better than the single AR for estimating the air temperature parameters at the study sites. Multi-layer perceptron (MLP) was then employed to estimate the air temperatures using lagged temperature data as input predictors. Next, the single AR and hybrid AR-ARCH time series models were utilized to implement the hybrid MLP-AR and MLP-AR-ARCH models. It is worth noting that developing the hybrid MLP-AR and MLP-AR-ARCH models, as well as AR-ARCH one is the novelty of this study. Three statistical metrics including root mean square error (RMSE), mean absolute error (MAE), and normalized RMSE (NRMSE) were used to investigate the performance of whole the developed models. The hybrid MLP-AR and MLP-AR-ARCH models were found to perform better than the single MLP when estimating the daily and monthly Tmin, Tmax, and T; however, the MLP-AR models outperformed the MLP-AR-ARCH ones. At the end of this study, the performance of MLP was evaluated under an external condition (i.e., estimating the temperature components at any particular site using the temperature data of an adjacent location). The results indicated that the temperature data of a nearby station can be used for estimating the temperatures of a desired station. Most accurate results during the test stage were obtained under a local assessment through the hybrid MLP-AR(1) at the Tabriz station when estimating the monthly Tmax (RMSE = 0.199 °C, MAE = 0.159 °C, NRMSE = 1.012%) and hybrid MLP-AR(12) at the Urmia station when estimating the daily Tmax (RMSE = 0.364 °C, MAE = 0.277 °C, NRMSE = 1.911%).
Babak Mohammadi; Saeid Mehdizadeh; Farshad Ahmadi; Nguyen Thi Thuy Lien; Quoc Bao Pham. Developing hybrid time series and artificial intelligence models for estimating air temperatures. Stochastic Environmental Research and Risk Assessment 2020, 35, 1189 -1204.
AMA StyleBabak Mohammadi, Saeid Mehdizadeh, Farshad Ahmadi, Nguyen Thi Thuy Lien, Quoc Bao Pham. Developing hybrid time series and artificial intelligence models for estimating air temperatures. Stochastic Environmental Research and Risk Assessment. 2020; 35 (6):1189-1204.
Chicago/Turabian StyleBabak Mohammadi; Saeid Mehdizadeh; Farshad Ahmadi; Nguyen Thi Thuy Lien; Quoc Bao Pham. 2020. "Developing hybrid time series and artificial intelligence models for estimating air temperatures." Stochastic Environmental Research and Risk Assessment 35, no. 6: 1189-1204.
This paper presents a new hybrid model, called ENN-SA, for spatiotemporal drought prediction. In ENN-SA, an Elman neural network (ENN) is conjugated with simulated annealing (SA) optimization and support vector machine (SVM) classification algorithms for the standardized precipitation index (SPI) modeling at multiple stations. The proposed model could be applied to predict SPI at different time scales in a meteorology station with lack of data through the intelligent use of SPI series of the nearby stations as the model inputs. The capability of the hybrid model for multi-station prediction of meteorological drought was examined through the cross-validation technique for Kecioren station in Ankara Province, Turkey. To this end, the SPI-3, SPI-6, and SPI-12 at the station were modeled using the same indices of five nearby stations. In the first step, SVM was trained using different kernels in order to generate and classify a set of plausible multi-station prediction scenarios. Then, ENN was used to regress the SPI series at each scenario and finally, the SA component of the integrated model was utilized to improve the ENN efficiency. Various error and complexity measures were used to detect the models’ performance. The results showed the ENN-SA is promising and efficient for multi-station SPI prediction.
Ali Danandeh Mehr; Babak Vaheddoost; Babak Mohammadi. ENN-SA: A novel neuro-annealing model for multi-station drought prediction. Computers & Geosciences 2020, 145, 104622 .
AMA StyleAli Danandeh Mehr, Babak Vaheddoost, Babak Mohammadi. ENN-SA: A novel neuro-annealing model for multi-station drought prediction. Computers & Geosciences. 2020; 145 ():104622.
Chicago/Turabian StyleAli Danandeh Mehr; Babak Vaheddoost; Babak Mohammadi. 2020. "ENN-SA: A novel neuro-annealing model for multi-station drought prediction." Computers & Geosciences 145, no. : 104622.
Large-scale oceanic oscillations and their teleconnection with meteorological events are of great importance in macro-scale climatic studies. In this regard, this study investigates the spatiotemporal teleconnections between four oceanic oscillations, namely North Atlantic Oscillation (NAO), El Niño/Southern Oscillation (ENSO), Atlantic Multi-Decadal Oscillation (AMO), and Pacific Decadal Oscillation (PDO), against Peruvian precipitation patterns during the past 25 years (i.e., 1990-2015). The monthly, annual, and Standardized Precipitation Index (SPI) time series at 1-, 3-, 12-, and 48-month time scales were evaluated at 10 meteorology stations across Peru. Then, Pearson's correlation coefficient and mutual information between oceanic oscillations and precipitation-born signals were calculated and spatially interpolated using the Kriging method. The results indicated the presence of three major climatic regions in the country. The NAO has the maximum correlation with the monthly precipitation. However, the ENSO was found as the most effective climatic driver of extremely wet and extremely dry conditions in the country. The results also demonstrated that the PDO has a higher impact on the annual precipitation pattern and mostly affective in the southern and eastern parts of the country.
Babak Mohammadi; Babak Vaheddoost; Ali Danandeh Mehr. A spatiotemporal teleconnection study between Peruvian precipitation and oceanic oscillations. Quaternary International 2020, 565, 1 -11.
AMA StyleBabak Mohammadi, Babak Vaheddoost, Ali Danandeh Mehr. A spatiotemporal teleconnection study between Peruvian precipitation and oceanic oscillations. Quaternary International. 2020; 565 ():1-11.
Chicago/Turabian StyleBabak Mohammadi; Babak Vaheddoost; Ali Danandeh Mehr. 2020. "A spatiotemporal teleconnection study between Peruvian precipitation and oceanic oscillations." Quaternary International 565, no. : 1-11.
Inasmuch as channels are designed to mitigate continues sedimentation, sediment transport models have been developed to calculate flow velocity to keep sediment particles in motion. In order to promote the computation capability of sediment transport models, recently machine learning algorithms have attracted interests, extensively. However, accuracy of such a model is attributed to the range of data and applied technique for model construction. For this purpose, the current study scrutinizes the applicability of “non-deposition with deposited bed” (NDB) concept for design of large channels applying hybrid machine learning algorithms. Through the modeling, firstly, conventional adaptive neuro-fuzzy inference system (ANFIS) technique is applied to develop a stand-alone model. In furtherance of improving the model’s performance, the ANFIS is hybridized with invasive weed optimization (IWO) algorithm to construct the hybrid ANFIS-IWO model. As a benchmark, the ANFIS is further hybridized with classical genetic algorithm (GA) to compare with ANFIS-IWO outcomes. Furthermore, the developed machine learning models are compared to multigene genetic programming (MGP) and particle swarm optimization (PSO) stand-alone machine learning results reported in the literature and classical regression models by means of variety of statistical performance measurements. Hybridization of ANFIS with IWO, enhances its accuracy with a factor of 30%. Respecting to the models performance examination, the ANFIS-IWO model is found superior to its alternatives for sediment transport computation. The thickness of the deposited bed and deposited bed width are found as effective parameters for sediment transport modeling in open channels with a bed deposit.
Mir Jafar Sadegh Safari; Babak Mohammadi; Katayoun Kargar. Invasive weed optimization-based adaptive neuro-fuzzy inference system hybrid model for sediment transport with a bed deposit. Journal of Cleaner Production 2020, 276, 124267 .
AMA StyleMir Jafar Sadegh Safari, Babak Mohammadi, Katayoun Kargar. Invasive weed optimization-based adaptive neuro-fuzzy inference system hybrid model for sediment transport with a bed deposit. Journal of Cleaner Production. 2020; 276 ():124267.
Chicago/Turabian StyleMir Jafar Sadegh Safari; Babak Mohammadi; Katayoun Kargar. 2020. "Invasive weed optimization-based adaptive neuro-fuzzy inference system hybrid model for sediment transport with a bed deposit." Journal of Cleaner Production 276, no. : 124267.
Streamflow plays a major role in the optimal management and allocation of available water resources in each region. Reliable techniques are therefore needed to be developed for streamflow modeling. In the present study, the performance of streamflow modeling is improved via developing novel boosted models. The daily streamflows of four hydrometric stations comprising of the Brantford and Galt stations located on the Grand River, Canada, as well as Macon and Elkton stations respectively, located on the Ocmulgee and Umpqua rivers, United States, are used. Three different types of boosted models are implemented and proposed by coupling the classical multi-layer perceptron (MLP) with the optimization algorithms, including particle swarm optimization (PSO) and coupled particle swarm optimization-multi-verse optimizer (PSOMVO) and a time series model, namely the bi-linear (BL). So, the boosted MLP-PSO, MLP-PSOMVO, and MLP-BL models are developed. The accuracy of all the boosted models is compared with the classical MLP and BL by the statistical metrics used. It is concluded that all the boosted models developed at the studied stations lead to superior modeling results of the daily streamflows to the classical MLP; however, the boosted MLP-BL models generally outperformed the MLP-PSO and MLP-PSOMVO ones.
Babak Mohammadi; Farshad Ahmadi; Saeid Mehdizadeh; Yiqing Guan; Quoc Bao Pham; Nguyen Thi Thuy Linh; Doan Quang Tri. Developing Novel Robust Models to Improve the Accuracy of Daily Streamflow Modeling. Water Resources Management 2020, 34, 3387 -3409.
AMA StyleBabak Mohammadi, Farshad Ahmadi, Saeid Mehdizadeh, Yiqing Guan, Quoc Bao Pham, Nguyen Thi Thuy Linh, Doan Quang Tri. Developing Novel Robust Models to Improve the Accuracy of Daily Streamflow Modeling. Water Resources Management. 2020; 34 (10):3387-3409.
Chicago/Turabian StyleBabak Mohammadi; Farshad Ahmadi; Saeid Mehdizadeh; Yiqing Guan; Quoc Bao Pham; Nguyen Thi Thuy Linh; Doan Quang Tri. 2020. "Developing Novel Robust Models to Improve the Accuracy of Daily Streamflow Modeling." Water Resources Management 34, no. 10: 3387-3409.
Evaporation is one of the vital components of hydrological cycle. Precise estimation of pan evaporation (Epan) is essential for the sustainable water resources management. The current study proposed a novel approach to estimate daily Epan across the humid region of Iran using support vector regression (SVR) technique coupled with Krill Herd Algorithm (SVR-KHA). Meteorological data were collected from three stations (Bandar Abbas, Rudsar, and Osku) over a period from 2008 to 2018 and used for application. Of the data, 70% were used for training and remaining 30% were used for testing. The study considered seven different combinations of input variables for predicting daily Epan at each station. The influence of KHA hybridization is examined by comparing results of SVR-KHA algorithm with simple SVR through a multitude of statistical performance evaluation criteria such as coefficient of determination (R2), Wilmot’s index (WI), root-mean-square error (RMSE), Mean Absolute Error (MAE), Relative Root Mean Square Error (RRMSE), Mean Absolute Relative Error (MARE), and several graphical tools. Single input SVR1 model hybrid with KHA (SVR-KHA1) showed improved performance (R2 of 0.717 and RMSE of 1.032 mm/day) as compared with multi-input SVR models, e.g., SVR5 (with RMSE and MAE of 1.037 mm/day and 0.773 mm/day), while SVR7 model hybridized with KHA (SVR-KHA7), which considers seven meteorological variables as input, performed best as compared with other models considered in this study. Epan estimates at Bandar Abbas and Rudsar by SVR and SVR-KHA are similar (with R2 statistics values of 0.82 and 0.84 at Bandar Abbas station, and 0.88 and 0.9 at Rudsar station, respectively). However, better improvements in Epan estimates are observed at Osku station (with R2 of 0.91 and 0.86, respectively), which is situated at interior geographical location with a higher altitude than the other two coastal stations. Overall, the results showed consistent performance of SVR-KHA model with stable residuals of lower magnitude as compared with standalone SVR models.
Yiqing Guan; Babak Mohammadi; Quoc Bao Pham; S. Adarsh; Khaled S. Balkhair; Khalil Ur Rahman; Nguyen Thi Thuy Linh; Doan Quang Tri. A novel approach for predicting daily pan evaporation in the coastal regions of Iran using support vector regression coupled with krill herd algorithm model. Theoretical and Applied Climatology 2020, 142, 349 -367.
AMA StyleYiqing Guan, Babak Mohammadi, Quoc Bao Pham, S. Adarsh, Khaled S. Balkhair, Khalil Ur Rahman, Nguyen Thi Thuy Linh, Doan Quang Tri. A novel approach for predicting daily pan evaporation in the coastal regions of Iran using support vector regression coupled with krill herd algorithm model. Theoretical and Applied Climatology. 2020; 142 (1-2):349-367.
Chicago/Turabian StyleYiqing Guan; Babak Mohammadi; Quoc Bao Pham; S. Adarsh; Khaled S. Balkhair; Khalil Ur Rahman; Nguyen Thi Thuy Linh; Doan Quang Tri. 2020. "A novel approach for predicting daily pan evaporation in the coastal regions of Iran using support vector regression coupled with krill herd algorithm model." Theoretical and Applied Climatology 142, no. 1-2: 349-367.