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Determination of wetting patterns’ dimensions is essential in designing and managing surface/subsurface drip irrigation systems. The laboratory experiments were conducted using physical model with dimensions of 3 × 1 × 0.5 m3 to evaluate the moisture redistribution process under continuous and pulse surface/subsurface irrigation systems. In the present study, the efficiency of a new machine learning method, named fuzzy c-means clustering- based adaptive neural-fuzzy inference system combined with a new meta-heuristic algorithm, hybrid particle swarm optimization – gravity search algorithm (ANFIS-FCM-PSOGSA), is investigated in order to model wetting front redistribution of drip irrigation systems (IS) using soil and system parameters as inputs under continuous and pulse surface/subsurface IS. The outcomes of the assessed method are compared with those of the ANFIS-FCM-PSO, generalized regression neural networks and multivariate adaptive regression splines. In assessing the implemented methods, four commonly used indices, root mean square errors (RMSE), mean absolute error (MAE), coefficient of determination (R2), Nash-Sutcliffe model efficiency (NSE) and graphical methods (e.g., scatter, box plot and Taylor diagrams) are utilized. The benchmark outcomes demonstrate the superiority of new method in estimating wetting front dimensions by improving the accuracy of the ANFIS-FCM-PSO by 29.6%, 18.5%, 6.1%, and 9.0% in estimating the diameter of horizontal redistribution with respect to RMSE, MAE, R2 and NSE, respectively. Furthermore, the ANFIS-FCM-PSOGSA respectively improves the RMSE, MAE, R2 and NSE accuracy of the ANFIS-FCM-PSO by 20.1%, 19.2%, 35.7% and 35.6% in estimating the diameter of downward vertical redistribution. The general outcomes recommend the use of new method in estimating wetting front dimensions of drip irrigation systems.
Ozgur Kisi; Payam Khosravinia; Salim Heddam; Bakhtiar Karimi; Nazir Karimi. Modeling wetting front redistribution of drip irrigation systems using a new machine learning method: Adaptive neuro- fuzzy system improved by hybrid particle swarm optimization – Gravity search algorithm. Agricultural Water Management 2021, 256, 107067 .
AMA StyleOzgur Kisi, Payam Khosravinia, Salim Heddam, Bakhtiar Karimi, Nazir Karimi. Modeling wetting front redistribution of drip irrigation systems using a new machine learning method: Adaptive neuro- fuzzy system improved by hybrid particle swarm optimization – Gravity search algorithm. Agricultural Water Management. 2021; 256 ():107067.
Chicago/Turabian StyleOzgur Kisi; Payam Khosravinia; Salim Heddam; Bakhtiar Karimi; Nazir Karimi. 2021. "Modeling wetting front redistribution of drip irrigation systems using a new machine learning method: Adaptive neuro- fuzzy system improved by hybrid particle swarm optimization – Gravity search algorithm." Agricultural Water Management 256, no. : 107067.
Accurate estimation of suspended sediment (SS) is very essential for planning and management of hydraulic structures. The study investigates the accuracy of four machine learning methods, dynamic evolving neural-fuzzy inference systems (DENFIS), fuzzy c-means based adaptive neuro fuzzy system (ANFIS-FCM), multivariate adaptive regression spline (MARS) and M5 model tree (M5Tree), in estimating suspended sediments. Several input scenarios including streamflow (Q) and sediment (S) data obtained from Ain Hamara Station in Wadi Abd basin, Algeria were constructed to find the most effective one. The research results indicate that the DENFIS model with current streamflow (Qt) and 1 previous sediment (St-1) values performs superior to the other alternatives in SS estimation; it increases the efficiency of the best ANFIS-FCM, MARS and M5Tree by 1.6%, 15.7% and 9.6% with respect to RMSE (root mean square error), respectively. Variation of Q and S data on models’ estimation ability was also investigated and it was found that the variation input considerably increase the prediction ability of MARS method; increments in RMSE and MAE (mean absolute error) are by 10.8 and 4.9% and decrement in NSE (Nash-Sutcliffe efficiency) is by 12.9%.
Achite Mohammed; Zaher Mundher Yaseen; Salim Heddam; Anurag Malik; Ozgur Kisi. Advanced machine learning models development for suspended sediment prediction: Comparative analysis study. Geocarto International 2021, 1 -25.
AMA StyleAchite Mohammed, Zaher Mundher Yaseen, Salim Heddam, Anurag Malik, Ozgur Kisi. Advanced machine learning models development for suspended sediment prediction: Comparative analysis study. Geocarto International. 2021; ():1-25.
Chicago/Turabian StyleAchite Mohammed; Zaher Mundher Yaseen; Salim Heddam; Anurag Malik; Ozgur Kisi. 2021. "Advanced machine learning models development for suspended sediment prediction: Comparative analysis study." Geocarto International , no. : 1-25.
Sixteen different sites from two provinces (Lorestan and Illam) in the western part of Iran were considered for the field data measurement of cumulative infiltration, infiltration rate, and other effective variables that affect infiltration process. Soil infiltration is recognized as a fundamental process of the hydrologic cycle affecting surface runoff, soil erosion, and groundwater recharge. Hence, accurate prediction of the infiltration process is one of the most important tasks in hydrological science. As direct measurement is difficult and costly, and empirical models are inaccurate, the current study proposed a standalone, and optimized deep learning algorithm of a convolutional neural network (CNN) using gray wolf optimization (GWO), a genetic algorithm (GA), and an independent component analysis (ICA) for cumulative infiltration and infiltration rate prediction. First, 154 raw datasets were collected including the time of measuring; sand, clay, and silt percent; bulk density; soil moisture percent; infiltration rate; and cumulative infiltration using field survey. Next, 70 % of the dataset were used for model building and the remaining 30 % was used for model validation. Then, based on the correlation coefficient between input variables and outputs, different input combinations were constructed. Finally, the prediction power of each developed algorithm was evaluated using different visually-based (scatter plot, box plot and Taylor diagram) and quantitatively-based [root mean square error (RMSE), mean absolute error (MAE), the Nash-Sutcliffe efficiency (NSE), and percentage of bias (PBIAS)] metrics. Finding revealed that the time of measurement is more important for cumulative infiltration, while soil characteristics (i.e. silt content) are more significant in infiltration rate prediction. This shows that in the study area, silt parameter, which is the dominant constituent parameter, can control infiltration process more effectively. Effectiveness of the variables in the present study, in the order of importance are time, silt, clay, moisture content, sand, and bulk density. This can be related to the fact that most of study area is rangeland and thus, overgrazing leads to compaction of the silt soil that can lead to a slow infiltration process. Soil moisture content and bulk density are not highly effective in our study because these two factors do not significantly change across the study area. Findings demonstrated that the optimum input variable combination, is the one in which all input variables are considered. The results illustrated that CNN algorithms have a very high performance, while a metaheuristic algorithm enhanced the performance of a standalone CNN algorithm (from 7% to 28 %). The results also showed that a CNN-GWO algorithm outperformed the other algorithms, followed by CNN-ICA, CNN-GA, and CNN for both cumulative infiltration and infiltration rate prediction. All developed algorithms underestimated cumulative infiltration, while overestimating infiltration rates.
Mahdi Panahi; Khabat Khosravi; Sajjad Ahmad; Somayeh Panahi; Salim Heddam; Assefa M Melesse; Ebrahim Omidvar; Chang-Wook Lee. Cumulative infiltration and infiltration rate prediction using optimized deep learning algorithms: A study in Western Iran. Journal of Hydrology: Regional Studies 2021, 35, 100825 .
AMA StyleMahdi Panahi, Khabat Khosravi, Sajjad Ahmad, Somayeh Panahi, Salim Heddam, Assefa M Melesse, Ebrahim Omidvar, Chang-Wook Lee. Cumulative infiltration and infiltration rate prediction using optimized deep learning algorithms: A study in Western Iran. Journal of Hydrology: Regional Studies. 2021; 35 ():100825.
Chicago/Turabian StyleMahdi Panahi; Khabat Khosravi; Sajjad Ahmad; Somayeh Panahi; Salim Heddam; Assefa M Melesse; Ebrahim Omidvar; Chang-Wook Lee. 2021. "Cumulative infiltration and infiltration rate prediction using optimized deep learning algorithms: A study in Western Iran." Journal of Hydrology: Regional Studies 35, no. : 100825.
The accurate estimation of suspended sediments (SSs) carries significance in determining the volume of dam storage, river carrying capacity, pollution susceptibility, soil erosion potential, aquatic ecological impacts, and the design and operation of hydraulic structures. The presented study proposes a new method for accurately estimating daily SSs using antecedent discharge and sediment information. The novel method is developed by hybridizing the multivariate adaptive regression spline (MARS) and the Kmeans clustering algorithm (MARS–KM). The proposed method’s efficacy is established by comparing its performance with the adaptive neuro-fuzzy system (ANFIS), MARS, and M5 tree (M5Tree) models in predicting SSs at two stations situated on the Yangtze River of China, according to the three assessment measurements, RMSE, MAE, and NSE. Two modeling scenarios are employed; data are divided into 50–50% for model training and testing in the first scenario, and the training and test data sets are swapped in the second scenario. In Guangyuan Station, the MARS–KM showed a performance improvement compared to ANFIS, MARS, and M5Tree methods in term of RMSE by 39%, 30%, and 18% in the first scenario and by 24%, 22%, and 8% in the second scenario, respectively, while the improvement in RMSE of ANFIS, MARS, and M5Tree was 34%, 26%, and 27% in the first scenario and 7%, 16%, and 6% in the second scenario, respectively, at Beibei Station. Additionally, the MARS–KM models provided much more satisfactory estimates using only discharge values as inputs.
Rana Adnan; Kulwinder Parmar; Salim Heddam; Shamsuddin Shahid; Ozgur Kisi. Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering. Sustainability 2021, 13, 4648 .
AMA StyleRana Adnan, Kulwinder Parmar, Salim Heddam, Shamsuddin Shahid, Ozgur Kisi. Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering. Sustainability. 2021; 13 (9):4648.
Chicago/Turabian StyleRana Adnan; Kulwinder Parmar; Salim Heddam; Shamsuddin Shahid; Ozgur Kisi. 2021. "Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering." Sustainability 13, no. 9: 4648.
Algerian climate is characterized by the transition between the subtropical climate in the north and the hot Saharan climate in the south. Understanding the spatiotemporal variability of rainfall patterns in such areas has significant implications for water resources management. To account for the spatial variation in the rainfall pattern of north Algeria, Tsallis entropy analysis, pattern recognition, and Precipitation Concentration Index (PCI) have been analyzed over a period of 33 years (1980–2013). The rainfall trend is identified by analyzing the results of the structural break test and Mann-Kendall (MK) trend tests. The Tsallis entropy produced spatial patterns for a better understanding of rainfall characteristics and the results show that entropy values were higher for higher rainfall values. The magnitude of rainfall change indicates that a large amount of rainfall occurs over the northern country boundary compared with the central part of Algeria. The rainfall record shows a structural break in the year 1992 during the selected time period. The MK trend analysis revealed a significant decreasing trend over the central and no trend in north and south of the study area. The PCI indicates a moderate rainfall concentration across the northern border region compared with a significant irregular rainfall distribution over the central region. The Wavelet Coherence Analysis (WCA) between El Nino Modoki (EMI) and Southern Oscillation Index (SOI) events on monthly rainfall data were also investigated to find a possible influence of global climatic indicators on the rainfall events. The results show a significant correlation of EMI and SOI with the rainfall pattern of north Algeria.
Mohammad Ali Ghorbani; Ercan Kahya; Thendiyath Roshni; Mahsa H. Kashani; Anurag Malik; Salim Heddam. Entropy analysis and pattern recognition in rainfall data, north Algeria. Theoretical and Applied Climatology 2021, 144, 317 -326.
AMA StyleMohammad Ali Ghorbani, Ercan Kahya, Thendiyath Roshni, Mahsa H. Kashani, Anurag Malik, Salim Heddam. Entropy analysis and pattern recognition in rainfall data, north Algeria. Theoretical and Applied Climatology. 2021; 144 (1-2):317-326.
Chicago/Turabian StyleMohammad Ali Ghorbani; Ercan Kahya; Thendiyath Roshni; Mahsa H. Kashani; Anurag Malik; Salim Heddam. 2021. "Entropy analysis and pattern recognition in rainfall data, north Algeria." Theoretical and Applied Climatology 144, no. 1-2: 317-326.
Accurate short-term rainfall–runoff prediction is essential for flood mitigation and safety of hydraulic structures and infrastructures. This study investigates the capability of four machine learning methods (MLM), optimal pruning extreme learning machine (OPELM), multivariate adaptive regression spline (MARS), M5 model tree (M5Tree, and hybridized MARS and Kmeans algorithm (MARS-Kmeans), in hourly rainfall–runoff modeling (considering 1-, 6- and 12-h horizons). Their results are compared with a conceptual method, Event-Based Approach for Small and Ungauged Basins (EBA4SUB) and multi-linear regression (MLR). Hourly rainfall and runoff data gathered from Ilme River watershed, Germany, were divided into two equal parts, and MLM were validated considering each part by swapping training and testing datasets. MLM were compared with EBA4SUB using four events and with respect to three statistics, root-mean-square errors (RMSE), mean absolute error (MAE) and Nash–Sutcliffe efficiency (NSE). Comparison results revealed that the newly developed hybridized MARS-Kmeans method performed superior to the OPELM, MARS, M5Tree and MLR methods in prediction of 1-, 6- and 12-h ahead runoff. Comparison with conceptual method showed that all the machine learning models outperformed the EBA4SUB and OPELM provided slightly better performance than the other three alternatives in event-based rainfall–runoff modeling.
Rana Muhammad Adnan; Andrea Petroselli; Salim Heddam; Celso Augusto Guimarães Santos; Ozgur Kisi. Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach. Natural Hazards 2021, 105, 2987 -3011.
AMA StyleRana Muhammad Adnan, Andrea Petroselli, Salim Heddam, Celso Augusto Guimarães Santos, Ozgur Kisi. Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach. Natural Hazards. 2021; 105 (3):2987-3011.
Chicago/Turabian StyleRana Muhammad Adnan; Andrea Petroselli; Salim Heddam; Celso Augusto Guimarães Santos; Ozgur Kisi. 2021. "Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach." Natural Hazards 105, no. 3: 2987-3011.
The potential or reference evapotranspiration (ET0) is considered as one of the fundamental variables for irrigation management, agricultural planning, and modeling different hydrological pr°Cesses, and therefore, its accurate prediction is highly essential. The study validates the feasibility of new temperature based heuristic models (i.e., group method of data handling neural network (GMDHNN), multivariate adaptive regression spline (MARS), and M5 model tree (M5Tree)) for estimating monthly ET0. The outcomes of the newly developed models are compared with empirical formulations including Hargreaves-Samani (HS), calibrated HS, and Stephens-Stewart (SS) models based on mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency. Monthly maximum and minimum temperatures (Tmax and Tmin) observed at two stations in Turkey are utilized as inputs for model development. In the applications, three data division scenarios are utilized and the effect of periodicity component (PC) on models’ accuracies are also examined. By importing PC into the model inputs, the RMSE accuracy of GMDHNN, MARS, and M5Tree models increased by 1.4%, 8%, and 6% in one station, respectively. The GMDHNN model with periodic input provides a superior performance to the other alternatives in both stations. The recommended model reduced the average error of MARS, M5Tree, HS, CHS, and SS models with respect to RMSE by 3.7–6.4%, 10.7–3.9%, 76–75%, 10–35%, and 0.8–17% in estimating monthly ET0, respectively. The HS model provides the worst accuracy while the calibrated version significantly improves its accuracy. The GMDHNN, MARS, M5Tree, SS, and CHS models are also compared in estimating monthly mean ET0. The GMDHNN generally gave the best accuracy while the CHS provides considerably over/under-estimations. The study indicated that the only one data splitting scenario may mislead the modeler and for better validation of the heuristic methods, more data splitting scenarios should be applied.
Rana Adnan; Salim Heddam; Zaher Yaseen; Shamsuddin Shahid; Ozgur Kisi; Binquan Li. Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches. Sustainability 2020, 13, 297 .
AMA StyleRana Adnan, Salim Heddam, Zaher Yaseen, Shamsuddin Shahid, Ozgur Kisi, Binquan Li. Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches. Sustainability. 2020; 13 (1):297.
Chicago/Turabian StyleRana Adnan; Salim Heddam; Zaher Yaseen; Shamsuddin Shahid; Ozgur Kisi; Binquan Li. 2020. "Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches." Sustainability 13, no. 1: 297.
In the present investigation, the spatial distribution of the nest of White Stork Ciconia ciconia was examined. Spearman’s rank-order correlations test and the principal component analysis (PCA) were applied to a total of 227 nests recorded in the Guerbes-Sanhadja wetland eco-complex, northeastern of Algeria, over seven sites, for which the percentage of occupied nests reaches 89% (202 nest were occupied). Our goals are twofold: to explore the variation and distribution of the structure supporting the nest and to explain their spatial variability. The Spearman’s rank-order correlation test show that steel electricity poles had non-significant correlations with tree, and only concrete electricity poles structure had statistically significant positive correlation with mobile phone antennas structure (R = 0.757; at p < .05), and the roofs of houses had statistically significant positive correlation with mobile phone antennas structure (R = 0.825; at p < .05). According to the PCA results, it was observed that the PC1, which explains 50.86% of the total inertia, further represents and synthesizes the dominant structure supporting the nest, i.e., tree, steel electricity poles, and concrete electricity poles, which were strongly correlated with PC1, having a component loading nearly equal to 0.766, 0.821, and − 0.929, respectively, while the PC2, which explains 30.30% of the total inertia, includes the structure rarely recorded in the studied region, i.e., wooden electricity poles and the roofs of houses.
Saddam Babouri; Sophia Metallaoui; Salim Heddam. Abundance and spatial distribution of the structure supporting the nest of White Stork Ciconia ciconia in Guerbes-Sanhadja wetland eco-complex, northeastern of Algeria. Environmental Science and Pollution Research 2020, 27, 45974 -45982.
AMA StyleSaddam Babouri, Sophia Metallaoui, Salim Heddam. Abundance and spatial distribution of the structure supporting the nest of White Stork Ciconia ciconia in Guerbes-Sanhadja wetland eco-complex, northeastern of Algeria. Environmental Science and Pollution Research. 2020; 27 (36):45974-45982.
Chicago/Turabian StyleSaddam Babouri; Sophia Metallaoui; Salim Heddam. 2020. "Abundance and spatial distribution of the structure supporting the nest of White Stork Ciconia ciconia in Guerbes-Sanhadja wetland eco-complex, northeastern of Algeria." Environmental Science and Pollution Research 27, no. 36: 45974-45982.
Electrical conductivity (EC), one of the most widely used indices for water quality assessment, has been applied to predict the salinity of the Babol-Rood River, the greatest source of irrigation water in northern Iran. This study uses two individual—M5 Prime (M5P) and random forest (RF)—and eight novel hybrid algorithms—bagging-M5P, bagging-RF, random subspace (RS)-M5P, RS-RF, random committee (RC)-M5P, RC-RF, additive regression (AR)-M5P, and AR-RF—to predict EC. Thirty-six years of observations collected by the Mazandaran Regional Water Authority were randomly divided into two sets: 70% from the period 1980 to 2008 was used as model-training data and 30% from 2009 to 2016 was used as testing data to validate the models. Several water quality variables—pH, HCO3−, Cl−, SO42−, Na+, Mg2+, Ca2+, river discharge (Q), and total dissolved solids (TDS)—were modeling inputs. Using EC and the correlation coefficients (CC) of the water quality variables, a set of nine input combinations were established. TDS, the most effective input variable, had the highest EC-CC (r = 0.91), and it was also determined to be the most important input variable among the input combinations. All models were trained and each model’s prediction power was evaluated with the testing data. Several quantitative criteria and visual comparisons were used to evaluate modeling capabilities. Results indicate that, in most cases, hybrid algorithms enhance individual algorithms’ predictive powers. The AR algorithm enhanced both M5P and RF predictions better than bagging, RS, and RC. M5P performed better than RF. Further, AR-M5P outperformed all other algorithms (R2 = 0.995, RMSE = 8.90 μs/cm, MAE = 6.20 μs/cm, NSE = 0.994 and PBIAS = −0.042). The hybridization of machine learning methods has significantly improved model performance to capture maximum salinity values, which is essential in water resource management.
Assefa M. Melesse; Khabat Khosravi; John P. Tiefenbacher; Salim Heddam; Sungwon Kim; Amir Mosavi; Binh Thai Pham. River Water Salinity Prediction Using Hybrid Machine Learning Models. Water 2020, 12, 2951 .
AMA StyleAssefa M. Melesse, Khabat Khosravi, John P. Tiefenbacher, Salim Heddam, Sungwon Kim, Amir Mosavi, Binh Thai Pham. River Water Salinity Prediction Using Hybrid Machine Learning Models. Water. 2020; 12 (10):2951.
Chicago/Turabian StyleAssefa M. Melesse; Khabat Khosravi; John P. Tiefenbacher; Salim Heddam; Sungwon Kim; Amir Mosavi; Binh Thai Pham. 2020. "River Water Salinity Prediction Using Hybrid Machine Learning Models." Water 12, no. 10: 2951.
Accurate estimation of dew point temperature (Tdew) has a crucial role in sustainable water resource management. This study investigates kernel extreme learning machine (KELM), boosted regression tree (BRT), radial basis function neural network (RBFNN), multilayer perceptron neural network (MLPNN), and multivariate adaptive regression spline (MARS) models for daily dew point temperature estimation at Durham and UC Riverside stations in the United States. Daily time scale measured hydrometeorological data, including wind speed (WS), maximum air temperature (TMAX), minimum air temperature (TMIN), maximum relative humidity (RHMAX), minimum relative humidity (RHMIN), vapor pressure (VP), soil temperature (ST), solar radiation (SR), and dew point temperature (Tdew) were utilized to investigate the applied predictive models. Results of the KELM model were compared with other models using eight different input combinations with respect to root mean square error (RMSE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE) statistical indices. Results showed that the KELM models, using three input parameters, VP, TMAX, and RHMIN, with RMSE = 0.419 °C, NSE = 0.995, and R2 = 0.995 at Durham station, and seven input parameters, VP, ST, RHMAX, TMIN, RHMIN, TMAX, and WS, with RMSE = 0.485 °C, NSE = 0.994, and R2 = 0.994 at UC Riverside station, exhibited better performance in the modeling of daily Tdew. Finally, it was concluded from a comparison of the results that out of the five models applied, the KELM model was found to be the most robust by improving the performance of BRT, RBFNN, MLPNN, and MARS models in the testing phase at both stations.
Meysam Alizamir; Sungwon Kim; Mohammad Zounemat-Kermani; Salim Heddam; Nam Won Kim; Vijay P. Singh. Kernel Extreme Learning Machine: An Efficient Model for Estimating Daily Dew Point Temperature Using Weather Data. Water 2020, 12, 2600 .
AMA StyleMeysam Alizamir, Sungwon Kim, Mohammad Zounemat-Kermani, Salim Heddam, Nam Won Kim, Vijay P. Singh. Kernel Extreme Learning Machine: An Efficient Model for Estimating Daily Dew Point Temperature Using Weather Data. Water. 2020; 12 (9):2600.
Chicago/Turabian StyleMeysam Alizamir; Sungwon Kim; Mohammad Zounemat-Kermani; Salim Heddam; Nam Won Kim; Vijay P. Singh. 2020. "Kernel Extreme Learning Machine: An Efficient Model for Estimating Daily Dew Point Temperature Using Weather Data." Water 12, no. 9: 2600.
This chapter designs intelligent data analytic approaches for predicting dissolved oxygen concentration in river utilizing extremely randomized tree versus random forest, MLPNN and MLR. Dissolved oxygen concentration (DO) in river, lake and stream can be measured directly in situ. However, mathematical models based on intelligent data analytic technique can provide a reasonably good alternative by linking several water quality variables to the concentration of DO at different time scale. Recent studies conducted worldwide have successfully demonstrated that models using intelligent data analytics contribute to accurately estimate dissolved oxygen with high precision. Here, we applied the extremely randomized tree (ERT) to develop a robust and computationally simple model for predicting dissolved oxygen concentration in river. Results obtained using the proposed ERT were compared to those obtained using the random forest (RF), the multilayer perceptron neural networks (MLPNN) and the standard multiple linear regression (MLR). The proposed models were developed using several inputs variables, e.g. water temperature, specific conductance, water pH and phycocyanin pigment concentration. Several inputs combinations were considered and compared to find the best inputs variables for predicting DO. All the proposed models were applied and compared using data collected from two rivers located in the USA. The accuracy of the models was evaluated using coefficient of correlation (R), Nash–Sutcliffe efficiency (NSE), root mean squared error (RMSE) and mean absolute error (MAE). Results were evaluated based on several input combinations and they showed that the RF provided the most effective estimation of DO concentration amongst the all the proposed models, while the ERT was ranked in the second place slightly less than the RF, the MLPNN ranked thirdly and the MLR model provided the worst accuracy.
Salim Heddam. Intelligent Data Analytics Approaches for Predicting Dissolved Oxygen Concentration in River: Extremely Randomized Tree Versus Random Forest, MLPNN and MLR. Understanding Built Environment 2020, 89 -107.
AMA StyleSalim Heddam. Intelligent Data Analytics Approaches for Predicting Dissolved Oxygen Concentration in River: Extremely Randomized Tree Versus Random Forest, MLPNN and MLR. Understanding Built Environment. 2020; ():89-107.
Chicago/Turabian StyleSalim Heddam. 2020. "Intelligent Data Analytics Approaches for Predicting Dissolved Oxygen Concentration in River: Extremely Randomized Tree Versus Random Forest, MLPNN and MLR." Understanding Built Environment , no. : 89-107.
This chapter proposes new method to estimate total dissolved gas (TDG) concentration, which is a critical factor causing gas bubble trauma in fish. Two kinds of data-driven approaches were applied: evolving connectionist systems (ECoS) and neuro-fuzzy systems (NFs). For the first group, we selected two ECoS models, namely (i) the off-line dynamic evolving neural-fuzzy inference system called DENFIS_OF and (ii) the on-line dynamic evolving neural-fuzzy inference system called DENFIS_ON. For the second group, three NFs models were selected, namely (i) adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-mean clustering (FC) algorithm called ANFIS_FC, (ii) adaptive neuro-fuzzy inference system with grid partition (GP) method called ANFIS_GP, and (iii) ANFIS with subtractive clustering (SC) called ANFIS_SC. In addition, results using the standard multiple linear regression (MLR) were provided for comparison. The proposed models were developed using several inputs variables, e.g., water temperature, barometric pressure, spill from dam, and discharge. Several inputs combinations were considered and compared to find the best inputs variables for estimating TDG, and several scenarios were developed and tested. Firstly, the proposed models were applied and compared for predicting TDG measured at the Tailwater of the dams using 70% of the data for training and 30% for validation (scenario 1). Secondly, using the same splitting ratio, the models were applied and compared for predicting TDG measured at the Forebay (scenario 2). Thirdly, the best models for the first two scenarios were selected and trained using validation data set and tested with the training data set (scenario 3). Fourthly, and finally, TDG is predicted without the well-known inputs variables, but rather, using the component of the Gregorian calendar as inputs variables (scenario 4). All the four scenarios were achieved using data collected from US Army Corps of Engineers and measured at hourly time step. The accuracy of the models was evaluated using coefficient of correlation (R), Nash-Sutcliffe efficiency (NSE), root mean squared error (RMSE), and mean absolute error (MAE). The applications, at Forebay and Tailwater of dam’s reservoirs, revealed that the proposed methods could be successfully utilized for estimation of TDG concentration using the component of the Gregorian calendar as input variable.
Salim Heddam; Ozgur Kisi. Evolving Connectionist Systems Versus Neuro-Fuzzy System for Estimating Total Dissolved Gas at Forebay and Tailwater of Dams Reservoirs. Understanding Built Environment 2020, 109 -126.
AMA StyleSalim Heddam, Ozgur Kisi. Evolving Connectionist Systems Versus Neuro-Fuzzy System for Estimating Total Dissolved Gas at Forebay and Tailwater of Dams Reservoirs. Understanding Built Environment. 2020; ():109-126.
Chicago/Turabian StyleSalim Heddam; Ozgur Kisi. 2020. "Evolving Connectionist Systems Versus Neuro-Fuzzy System for Estimating Total Dissolved Gas at Forebay and Tailwater of Dams Reservoirs." Understanding Built Environment , no. : 109-126.
This chapter aims to investigate the capabilities and usefulness of two new data-driven techniques: optimally pruned extreme learning machine (OPELM) and online sequential extreme learning machine (OSELM) newly applied and compared for predicting daily reference evapotranspiration (ET0) in the Mediterranean region of Algeria. Using large data sets from east to west regions of Algeria, the models were developed using several well-known climatic variables as inputs: daily maximum and minimum air temperatures, wind speed, and relative humidity. The proposed models were compared using several well-known statistical indexes: root mean square error (RMSE), mean absolute error (MAE), and coefficient of correlation (R). The obtained results have shown that all the proposed models present high prediction accuracy and the OPELM models provide better overall performances compared to the OSELM models
Salim Heddam; Ozgur Kisi; Abderrazek Sebbar; Larbi Houichi; Lakhdar Djemili. New Formulation for Predicting Daily Reference Evapotranspiration (ET0) in the Mediterranean Region of Algeria Country: Optimally Pruned Extreme Learning Machine (OPELM) Versus Online Sequential Extreme Learning Machine (OSELM). The Handbook of Environmental Chemistry 2020, 181 -199.
AMA StyleSalim Heddam, Ozgur Kisi, Abderrazek Sebbar, Larbi Houichi, Lakhdar Djemili. New Formulation for Predicting Daily Reference Evapotranspiration (ET0) in the Mediterranean Region of Algeria Country: Optimally Pruned Extreme Learning Machine (OPELM) Versus Online Sequential Extreme Learning Machine (OSELM). The Handbook of Environmental Chemistry. 2020; ():181-199.
Chicago/Turabian StyleSalim Heddam; Ozgur Kisi; Abderrazek Sebbar; Larbi Houichi; Lakhdar Djemili. 2020. "New Formulation for Predicting Daily Reference Evapotranspiration (ET0) in the Mediterranean Region of Algeria Country: Optimally Pruned Extreme Learning Machine (OPELM) Versus Online Sequential Extreme Learning Machine (OSELM)." The Handbook of Environmental Chemistry , no. : 181-199.
Evaporation (EP) from dams’ reservoirs measured using pans is one of the most important methods adopted for quantifying the loss of water through evaporation. Black box artificial intelligence techniques (AI) have been developed as alternative approaches for quantifying evaporation, and several kinds of models have been proposed worldwide. The present study uses the measurement of several climatic variables such as air temperature, wind speed, and relative humidity to test the performances of new AI techniques called evolving connectionist systems (ECoS), applied for predicting daily evaporation from several dam reservoirs located in Algeria country. Two ECoS models, namely, (1) offline-based dynamic evolving neural-fuzzy inference systems named DENFIS_OF and (2) online-based dynamic evolving neural-fuzzy inference systems named DENFIS_ON, were applied and compared for predicting daily evaporation. The results using ECoS models were compared to multiple linear regression (MLR) and artificial neural network (ANN) models. From the results obtained, it is seen that the ECoS models could predict daily evaporation from dam reservoirs with better accuracy than the ANN and MLR models.
Abderrazek Sebbar; Salim Heddam; Ozgur Kisi; Lakhdar Djemili; Larbi Houichi. Comparison of Evolving Connectionist Systems (ECoS) and Neural Networks for Modelling Daily Pan Evaporation from Algerian Dam Reservoirs. The Handbook of Environmental Chemistry 2020, 161 -179.
AMA StyleAbderrazek Sebbar, Salim Heddam, Ozgur Kisi, Lakhdar Djemili, Larbi Houichi. Comparison of Evolving Connectionist Systems (ECoS) and Neural Networks for Modelling Daily Pan Evaporation from Algerian Dam Reservoirs. The Handbook of Environmental Chemistry. 2020; ():161-179.
Chicago/Turabian StyleAbderrazek Sebbar; Salim Heddam; Ozgur Kisi; Lakhdar Djemili; Larbi Houichi. 2020. "Comparison of Evolving Connectionist Systems (ECoS) and Neural Networks for Modelling Daily Pan Evaporation from Algerian Dam Reservoirs." The Handbook of Environmental Chemistry , no. : 161-179.
Prediction of rivers and lakes water temperature plays an important role in hydrology, ecology, and water resources planning and management. Recently, machines learning approaches have been widely used for modelling water temperature, and the obtained results vary depending on the kind of models and the selections of the appropriates predictors. In the present paper, a new family of machines learning are proposed and compared to the famous air2stream model, using a large data set collected at 25 lakes in the northern part of Poland. The proposed models were: (i) the extremely randomized trees (ERT), (ii) the multivariate adaptive regression splines (MARS), (iii) the M5 Model tree (M5Tree), (iv) the random forest (RF), and (v) the multilayer perceptron neural network (MLPNN). The models were developed using the air temperature as input variables and the component of the Gregorian calendar (year, month and day) number. Results obtained were evaluated using several statistical indices: the root mean square error (RMSE), the mean absolute error (MAE), correlation coefficient (R) and Nash-Sutcliffe efficiency coefficient (NSE). Obtained results reveals that the air2stream model outperformed all other machines learning models and worked best with high accuracy at all the 25 lakes, and none of the ERT, MARS, M5Tree, RF and MLPNN models was able to provides an improvement of the water temperature prediction compared to the air2stream.
Salim Heddam; Mariusz Ptak; Senlin Zhu. Modelling of daily lake surface water temperature from air temperature: Extremely randomized trees (ERT) versus Air2Water, MARS, M5Tree, RF and MLPNN. Journal of Hydrology 2020, 588, 125130 .
AMA StyleSalim Heddam, Mariusz Ptak, Senlin Zhu. Modelling of daily lake surface water temperature from air temperature: Extremely randomized trees (ERT) versus Air2Water, MARS, M5Tree, RF and MLPNN. Journal of Hydrology. 2020; 588 ():125130.
Chicago/Turabian StyleSalim Heddam; Mariusz Ptak; Senlin Zhu. 2020. "Modelling of daily lake surface water temperature from air temperature: Extremely randomized trees (ERT) versus Air2Water, MARS, M5Tree, RF and MLPNN." Journal of Hydrology 588, no. : 125130.
Appropriate input selection for the estimation matrix is essential when modeling non-linear progression. In this study, the feasibility of the Gamma test (GT) was investigated to extract the optimal input combination as the primary modeling step for estimating monthly pan evaporation (EPm). A new artificial intelligent (AI) model called the co-active neuro-fuzzy inference system (CANFIS) was developed for monthly EPm estimation at Pantnagar station (located in Uttarakhand State) and Nagina station (located in Uttar Pradesh State), India. The proposed AI model was trained and tested using different percentages of data points in scenarios one to four. The estimates yielded by the CANFIS model were validated against several well-established predictive AI (multilayer perceptron neural network (MLPNN) and multiple linear regression (MLR)) and empirical (Penman model (PM)) models. Multiple statistical metrics (normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC), Willmott index (WI), and relative error (RE)) and graphical interpretation (time variation plot, scatter plot, relative error plot, and Taylor diagram) were performed for the modeling evaluation. The results of appraisal showed that the CANFIS-1 model with six input variables provided better NRMSE (0.1364, 0.0904, 0.0947, and 0.0898), NSE (0.9439, 0.9736, 0.9703, and 0.9799), PCC (0.9790, 0.9872, 0.9877, and 0.9922), and WI (0.9860, 0.9934, 0.9927, and 0.9949) values for Pantnagar station, and NRMSE (0.1543, 0.1719, 0.2067, and 0.1356), NSE (0.9150, 0.8962, 0.8382, and 0.9453), PCC (0.9643, 0.9649, 0.9473, and 0.9762), and WI (0.9794, 0.9761, 0.9632, and 0.9853) values for Nagina stations in all applied modeling scenarios for estimating the monthly EPm. This study also confirmed the supremacy of the proposed integrated GT-CANFIS model under four different scenarios in estimating monthly EPm. The results of the current application demonstrated a reliable modeling methodology for water resource management and sustainability.
Anurag Malik; Priya Rai; Salim Heddam; Ozgur Kisi; Ahmad Sharafati; Sinan Salih; Nadhir Al-Ansari; Zaher Yaseen. Pan Evaporation Estimation in Uttarakhand and Uttar Pradesh States, India: Validity of an Integrative Data Intelligence Model. Atmosphere 2020, 11, 553 .
AMA StyleAnurag Malik, Priya Rai, Salim Heddam, Ozgur Kisi, Ahmad Sharafati, Sinan Salih, Nadhir Al-Ansari, Zaher Yaseen. Pan Evaporation Estimation in Uttarakhand and Uttar Pradesh States, India: Validity of an Integrative Data Intelligence Model. Atmosphere. 2020; 11 (6):553.
Chicago/Turabian StyleAnurag Malik; Priya Rai; Salim Heddam; Ozgur Kisi; Ahmad Sharafati; Sinan Salih; Nadhir Al-Ansari; Zaher Yaseen. 2020. "Pan Evaporation Estimation in Uttarakhand and Uttar Pradesh States, India: Validity of an Integrative Data Intelligence Model." Atmosphere 11, no. 6: 553.
The purpose of this study is to investigate the possibility of applying multilayer perceptron neural network (MLPNN) and cluster analysis approaches for rebuilding non-cored lithofacies. These techniques are carried out to predict missing lithofacies intervals from the reservoir of Trias Argileux Grèseux Inférieur in the Sif Fatima oil field (Berkine basin- Southern Algeria). The performances of the suggested models were evaluated using root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R). The MLPNN model was developed using four input variables of nuclear well logging data including: Gamma-Ray, Density, Potassium and Thorium. MLPNN model shows lower RMSE and MAE with 0.39 and 0.23 respectively, together with strong R values (training = 0.87; validation = 0.78; test = 0.92). The cluster analysis model displays lower performances (R = 0.68, RMSE = 1.04 and MAE = 0.54). This quantitative comparison between real and predicted electrofacies using the two methods indicates that MLPNN model is more recommended in rebuilding non-cored lithofacies than cluster analysis. The MLPNN model makes possible to estimate lithofacies in 333 m of non-cored lithofacies allowing an important economic benefit.
Ouafi Ameur-Zaimeche; Aziez Zeddouri; Salim Heddam; Rabah Kechiched. Lithofacies prediction in non-cored wells from the Sif Fatima oil field (Berkine basin, southern Algeria): A comparative study of multilayer perceptron neural network and cluster analysis-based approaches. Journal of African Earth Sciences 2020, 166, 103826 .
AMA StyleOuafi Ameur-Zaimeche, Aziez Zeddouri, Salim Heddam, Rabah Kechiched. Lithofacies prediction in non-cored wells from the Sif Fatima oil field (Berkine basin, southern Algeria): A comparative study of multilayer perceptron neural network and cluster analysis-based approaches. Journal of African Earth Sciences. 2020; 166 ():103826.
Chicago/Turabian StyleOuafi Ameur-Zaimeche; Aziez Zeddouri; Salim Heddam; Rabah Kechiched. 2020. "Lithofacies prediction in non-cored wells from the Sif Fatima oil field (Berkine basin, southern Algeria): A comparative study of multilayer perceptron neural network and cluster analysis-based approaches." Journal of African Earth Sciences 166, no. : 103826.
Sophia Metallaoui; Hamdi Dziri; Abderazzak Bousseheba; Salim Heddam; Haroun Chenchouni. Breeding ecology of the Cattle Egret (Bubulcus ibis) in Guerbes-Sanhadja wetlands of Algeria. Regional Studies in Marine Science 2020, 33, 1 .
AMA StyleSophia Metallaoui, Hamdi Dziri, Abderazzak Bousseheba, Salim Heddam, Haroun Chenchouni. Breeding ecology of the Cattle Egret (Bubulcus ibis) in Guerbes-Sanhadja wetlands of Algeria. Regional Studies in Marine Science. 2020; 33 ():1.
Chicago/Turabian StyleSophia Metallaoui; Hamdi Dziri; Abderazzak Bousseheba; Salim Heddam; Haroun Chenchouni. 2020. "Breeding ecology of the Cattle Egret (Bubulcus ibis) in Guerbes-Sanhadja wetlands of Algeria." Regional Studies in Marine Science 33, no. : 1.
The goal was to predict seepage flow (Q) through concrete face rockfill and embankment dams, using three artificial intelligence models, i.e., multivariate adaptive regression splines (MARS), least squares support vector machine (LSSVM), and M5 model tree (M5Tree). The three models were constructed exclusively using in situ measured data from two dams: El Agrem dam located at Jijel province, and Fontaine Gazelles dam located at Biskra province. The obtained results using artificial intelligence models were compared to those obtained using the multiple linear regression (MLR) models. We used two different input variables for developing the models: (i) the daily reservoir water level (WL) and the piezometer elevation (PL) measured at seven different piezometers (PZ1 to PZ7). The results show that the estimation accuracy for Fontaine Gazelles dam is much better than those obtained for El Agrem. All the models performed reasonably well, but the LSSVM was the most consistent predictor of seepage flow for the two data sets. The validation results showed that the LSSVM model has showed significantly better accuracy of seepage flow prediction with root mean square error (RMSE) of 0.432 L/s, mean absolute error (MAE) of 0.302 L/s and correlation coefficient R of 0.952 for Fontaine Gazelles, and RMSE of 0.544 L/s, MAE of 0.344 L/s and correlation coefficient R of 0.731 for El Agrem dam. From this study we conclude that, seepage flow is likely to vary considerably, depending on the reservoir water level, and that the proposed model can be very helpful in estimation of seepage flow, while limitations of the prediction using a standard regression model are illustrated.
Issam Rehamnia; Bachir Benlaoukli; Salim Heddam. Modeling of Seepage Flow Through Concrete Face Rockfill and Embankment Dams Using Three Heuristic Artificial Intelligence Approaches: a Comparative Study. Environmental Processes 2019, 7, 367 -381.
AMA StyleIssam Rehamnia, Bachir Benlaoukli, Salim Heddam. Modeling of Seepage Flow Through Concrete Face Rockfill and Embankment Dams Using Three Heuristic Artificial Intelligence Approaches: a Comparative Study. Environmental Processes. 2019; 7 (1):367-381.
Chicago/Turabian StyleIssam Rehamnia; Bachir Benlaoukli; Salim Heddam. 2019. "Modeling of Seepage Flow Through Concrete Face Rockfill and Embankment Dams Using Three Heuristic Artificial Intelligence Approaches: a Comparative Study." Environmental Processes 7, no. 1: 367-381.
Monthly streamflow prediction is very important for many hydrological applications in providing information for optimal use of water resources. In this study, the prediction accuracy of new heuristic methods, optimally pruned extreme learning machine (OP-ELM), least square support vector machine (LSSVM), multivariate adaptive regression splines (MARS) and M5 model tree (M5Tree), is examined in modeling monthly streamflows using precipitation and temperature inputs. Data collected from Kalam and Chakdara stations at a mountainous basin, Swat River Basin, Pakistan are utilized as case study. The prediction accuracy of all four methods are validated and tested using four different input scenarios and evaluated using combined accuracy (CA), a newly used criterion in addition to root-mean-square error (RMSE), normalized RMSE, mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE). The test results of both stations show that the LSSVM and MARS-based models provide more accurate prediction results compared to OP-ELM and M5Tree models. LSSVM decreases the RMSE of the MARS, OP-ELM and M5Tree by 9.12%, 25.64% and 35.15% for the Kalam station while the RMSEs of the LSSVM, OP-ELM and M5Tree is decreased by 2.12%, 34.81% and 32.52% using MARS, for the Chakdara Station, respectively. It is observed that the monthly streamflows of Kalam Station can be successfully predicted using only temperature data. Only precipitation inputs also provide good accuracy for Kalam Station while they produce inaccurate predictions for the Chakdara Station. The prediction capabilities of the applied methods are also examined in estimating streamflow of downstream station using upstream data. The results prove the dominancy of LSSVM and MARS-based models over OP-ELM and M5Tree in prediction streamflow data without local input data. Heuristic methods are also compared with stochastic method of seasonal auto regressive moving average (SARIMA). The OP-ELM, LSSVM, MARS perform superior to the SARIMA in monthly streamflow prediction. Based on the overall results, the LSSVM and MARS are recommended for monthly streamflow prediction with or without local data.
Rana Muhammad Adnan; Zhongmin Liang; Salim Heddam; Mohammad Zounemat-Kermani; Ozgur Kisi; Binquan Li. Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. Journal of Hydrology 2019, 586, 124371 .
AMA StyleRana Muhammad Adnan, Zhongmin Liang, Salim Heddam, Mohammad Zounemat-Kermani, Ozgur Kisi, Binquan Li. Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. Journal of Hydrology. 2019; 586 ():124371.
Chicago/Turabian StyleRana Muhammad Adnan; Zhongmin Liang; Salim Heddam; Mohammad Zounemat-Kermani; Ozgur Kisi; Binquan Li. 2019. "Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs." Journal of Hydrology 586, no. : 124371.