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Dr. Rana Adnan
Hohai Univeristy, Nanjing, China

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0 MetaHeuristic Algorigthm
0 Hydrologic and Water Resource Modeling and Simulation
0 SVM Algorithm
0 Machine Learning Application

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Journal article
Published: 24 May 2021 in Sustainability
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The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.

ACS Style

Rana Adnan; Abolfazl Jaafari; Aadhityaa Mohanavelu; Ozgur Kisi; Ahmed Elbeltagi. Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm. Sustainability 2021, 13, 5877 .

AMA Style

Rana Adnan, Abolfazl Jaafari, Aadhityaa Mohanavelu, Ozgur Kisi, Ahmed Elbeltagi. Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm. Sustainability. 2021; 13 (11):5877.

Chicago/Turabian Style

Rana Adnan; Abolfazl Jaafari; Aadhityaa Mohanavelu; Ozgur Kisi; Ahmed Elbeltagi. 2021. "Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm." Sustainability 13, no. 11: 5877.

Journal article
Published: 30 April 2021 in Water
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Recent evidence of regional climate change impacts on hydrological cycle directed us to study the floods in a high elevated and rapidly urbanized river basin, the Kabul River basin (KRB), Pakistan, which is susceptible to frequent flooding. Therefore, we analyzed the changes in flood regime at various spatial and temporal scales and their possible causes, which is accomplished by using flood indicators, trend analysis, change point analysis, and hydrological modeling. The results showed that the northern and northwestern parts of the KRB were more exposed to flood hazard than the southern parts under long-term scenario (1961/64-2015). However, after the change points, the flood risk decreased in the northern and increased in the southern regions. This spatial shift increased the vulnerability of population to the flood hazard, because the majority of population resides in the southern region. The extreme precipitation has also increased, especially the maximum one-day rainfall and maximum five-day rainfall throughout the basin. Particularly, the major cause of the decrease in different flood indicators in the northern parts of the KRB is the corresponding decrease in the annual and monsoonal rainfall and corresponding positive mass balance of glaciers in the northern region after the occurrence of change point in flood regime. However, the major cause of the increase in flood hazard on the southern part of the KRB is associated with maximum five-day rainfall. A 68% variability of annual maximum flood for the Kabul River at Nowshera and an 84% variability of annual maximum flood for Bara River at Jhansi post are explained by maximum five-day rainfall. In addition, a considerable decrease in forests (–5.21%) and increase in the urban area (88.26%) from 1992–2015 also amplifies the risk of higher flood peaks. The results of hydrological modeling suggest that the six-hourly flood peak increased by 6.85% (1992–2010) and 4.81% (2010–2015) for the extreme flood of 2010 for the Kabul River at Nowshera. The flood peak per decade will increase by 8.6%, as compared to the flood peak under the land use scenario of 2010. Therefore, consideration of proper land use planning is crucial for sustainable flood management in the KRB.

ACS Style

Asif Mehmood; Shaofeng Jia; Aifeng Lv; Wenbin Zhu; Rashid Mahmood; Muhammad Saifullah; Rana Adnan. Detection of Spatial Shift in Flood Regime of the Kabul River Basin in Pakistan, Causes, Challenges, and Opportunities. Water 2021, 13, 1276 .

AMA Style

Asif Mehmood, Shaofeng Jia, Aifeng Lv, Wenbin Zhu, Rashid Mahmood, Muhammad Saifullah, Rana Adnan. Detection of Spatial Shift in Flood Regime of the Kabul River Basin in Pakistan, Causes, Challenges, and Opportunities. Water. 2021; 13 (9):1276.

Chicago/Turabian Style

Asif Mehmood; Shaofeng Jia; Aifeng Lv; Wenbin Zhu; Rashid Mahmood; Muhammad Saifullah; Rana Adnan. 2021. "Detection of Spatial Shift in Flood Regime of the Kabul River Basin in Pakistan, Causes, Challenges, and Opportunities." Water 13, no. 9: 1276.

Journal article
Published: 22 April 2021 in Sustainability
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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.

ACS Style

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 Style

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 (9):4648.

Chicago/Turabian Style

Rana 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.

Original paper
Published: 02 January 2021 in Natural Hazards
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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.

ACS Style

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 Style

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 (3):2987-3011.

Chicago/Turabian Style

Rana 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.

Journal article
Published: 31 December 2020 in Sustainability
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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.

ACS Style

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 Style

Rana 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 Style

Rana 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.

Original paper
Published: 30 October 2020 in Stochastic Environmental Research and Risk Assessment
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The applicability of four machine learning (ML) methods, ANFIS-PSO, ANFIS-FCM, MARS and M5Tree, together with multi model simple averaging (MM-SA) ensemble method, is investigated in rainfall-runoff modeling at hourly timescale. The results are compared with the conceptual EBA4SUB model using rainfall and runoff data from Samoggia River basin, Italy. The capability of the methods is measured using five statistics, Nash–Sutcliffe efficiency, root mean squared error, mean absolute error, scatter index, and adjusted index of agreement. Comparison of single ML reveals that the ANFIS-PSO, ANFIS-FCM and MARS produce similar accuracy which is better than the M5Tree model. MM-SA ensemble model improves the accuracy of ANFIS-PSO, ANFIS-FCM, MARS and M5Tree models with respect to RMSE by 8.5%, 5%, 7.4% and 28.8%, respectively. Comparison with the conceptual event-based method indicates that the ML methods generally performs superior to the EBA4SUB; however, latter method provides better accuracy than the M5Tree and MARS in some cases.

ACS Style

Rana Muhammad Adnan; Andrea Petroselli; Salim Heddam; Celso Augusto Guimarães Santos; Ozgur Kisi. Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model. Stochastic Environmental Research and Risk Assessment 2020, 35, 597 -616.

AMA Style

Rana Muhammad Adnan, Andrea Petroselli, Salim Heddam, Celso Augusto Guimarães Santos, Ozgur Kisi. Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model. Stochastic Environmental Research and Risk Assessment. 2020; 35 (3):597-616.

Chicago/Turabian Style

Rana Muhammad Adnan; Andrea Petroselli; Salim Heddam; Celso Augusto Guimarães Santos; Ozgur Kisi. 2020. "Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model." Stochastic Environmental Research and Risk Assessment 35, no. 3: 597-616.

Original article
Published: 21 July 2020 in Neural Computing and Applications
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Accurate estimation of streamflow has a vital importance in water resources engineering, management and planning. In the present study, the abilities of group method of data handling-neural networks (GMDH-NN), dynamic evolving neural-fuzzy inference system (DENFIS) and multivariate adaptive regression spline (MARS) methods are investigated for monthly streamflow prediction. Precipitation, temperature and streamflows from Kalam and Chakdara stations at Swat River basin (mountainous basin), Pakistan, are used as inputs to the applied models in the form of different input scenarios, and models’ performances are evaluated on the basis of root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE) and combined accuracy (CA) indexes. Test results of the Kalam Station show that the DENFIS model provides more accurate prediction results in comparison of GMDH-NN and MARS models with the lowest RMSE (18.9 m3/s), MAE (13.1 m3/s), CA (10.6 m3/s) and the highest NSE (0.941). For the Chakdara Station, the MARS outperforms the GMDH-NN and DENFIS models with the lowest RMSE (47.5 m3/s), MAE (31.6 m3/s), CA (26.1 m3/s) and the highest NSE (0.905). Periodicity (month number of the year) effect on models’ accuracies in predicting monthly streamflow is also examined. Obtained results demonstrate that the periodicity improves the models’ accuracies in general but not necessarily in every case. In addition, the results also show that the monthly streamflow could be successfully predicted using only precipitation and temperature variables as inputs.

ACS Style

Rana Muhammad Adnan; Zhongmin Liang; Kulwinder Singh Parmar; Kirti Soni; Ozgur Kisi. Modeling monthly streamflow in mountainous basin by MARS, GMDH-NN and DENFIS using hydroclimatic data. Neural Computing and Applications 2020, 33, 2853 -2871.

AMA Style

Rana Muhammad Adnan, Zhongmin Liang, Kulwinder Singh Parmar, Kirti Soni, Ozgur Kisi. Modeling monthly streamflow in mountainous basin by MARS, GMDH-NN and DENFIS using hydroclimatic data. Neural Computing and Applications. 2020; 33 (7):2853-2871.

Chicago/Turabian Style

Rana Muhammad Adnan; Zhongmin Liang; Kulwinder Singh Parmar; Kirti Soni; Ozgur Kisi. 2020. "Modeling monthly streamflow in mountainous basin by MARS, GMDH-NN and DENFIS using hydroclimatic data." Neural Computing and Applications 33, no. 7: 2853-2871.

Discussion
Published: 05 June 2020 in Science of The Total Environment
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In this study, some important mistakes related to model development process and missing information which should be carefully taken into account by the authors of the previous literature and other researchers are presented. Some important issues are presented to avoid propagation of similar mistakes in the scientific literature.

ACS Style

Rana Muhammad Adnan; Zhongmin Liang; Ozgur Kisi. Comments on “Predicting permeability changes with injecting CO2 in coal seams during CO2 geological sequestration: A comparative study among six SVM-based hybrid models” Science of the Total Environment, 705, 135941 (2020). Science of The Total Environment 2020, 744, 139486 .

AMA Style

Rana Muhammad Adnan, Zhongmin Liang, Ozgur Kisi. Comments on “Predicting permeability changes with injecting CO2 in coal seams during CO2 geological sequestration: A comparative study among six SVM-based hybrid models” Science of the Total Environment, 705, 135941 (2020). Science of The Total Environment. 2020; 744 ():139486.

Chicago/Turabian Style

Rana Muhammad Adnan; Zhongmin Liang; Ozgur Kisi. 2020. "Comments on “Predicting permeability changes with injecting CO2 in coal seams during CO2 geological sequestration: A comparative study among six SVM-based hybrid models” Science of the Total Environment, 705, 135941 (2020)." Science of The Total Environment 744, no. : 139486.

Journal article
Published: 13 May 2020 in Entropy
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The study investigates the potential of two new machine learning methods, least-square support vector regression with a gravitational search algorithm (LSSVR-GSA) and the dynamic evolving neural-fuzzy inference system (DENFIS), for modeling reference evapotranspiration (ETo) using limited data. The results of the new methods are compared with the M5 model tree (M5RT) approach. Previous values of temperature data and extraterrestrial radiation information obtained from three stations, in China, are used as inputs to the models. The estimation exactness of the models is measured by three statistics: root mean square error, mean absolute error, and determination coefficient. According to the results, the temperature or extraterrestrial radiation-based LSSVR-GSA models perform superiorly to the DENFIS and M5RT models in terms of estimating monthly ETo. However, in some cases, a slight difference was found between the LSSVR-GSA and DENFIS methods. The results indicate that better prediction accuracy may be obtained using only extraterrestrial radiation information for all three methods. The prediction accuracy of the models is not generally improved by including periodicity information in the inputs. Using optimum air temperature and extraterrestrial radiation inputs together generally does not increase the accuracy of the applied methods in the estimation of monthly ETo.

ACS Style

Rana Muhammad Adnan; Zhihuan Chen; Xiaohui Yuan; Ozgur Kisi; Ahmed El-Shafie; Alban Kuriqi; Misbah Ikram. Reference Evapotranspiration Modeling Using New Heuristic Methods. Entropy 2020, 22, 547 .

AMA Style

Rana Muhammad Adnan, Zhihuan Chen, Xiaohui Yuan, Ozgur Kisi, Ahmed El-Shafie, Alban Kuriqi, Misbah Ikram. Reference Evapotranspiration Modeling Using New Heuristic Methods. Entropy. 2020; 22 (5):547.

Chicago/Turabian Style

Rana Muhammad Adnan; Zhihuan Chen; Xiaohui Yuan; Ozgur Kisi; Ahmed El-Shafie; Alban Kuriqi; Misbah Ikram. 2020. "Reference Evapotranspiration Modeling Using New Heuristic Methods." Entropy 22, no. 5: 547.

Letter to the editor
Published: 01 May 2020 in Environmental Science and Pollution Research
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Hecht-Nielsen R (1987) Neurocomputing: picking the human brain. IEEE Spectr 25(3):36–41 Kisi O (2010) Discussion of comparative study of ANNs versus parametric methods in rainfall frequency analysis by J. He and C. Valeo. ASCE J Hydrol Eng 15(4):321–322 Kisi O (2014) Discussion of ‘Comparison of artificial neural network models for sediment yield prediction at single gauging station of watershed in Eastern India’ by Ajai Singh; Mohd Imtiyaz, R.K. Isaac, and D.M. Denis. J Hydrol Eng:661–662. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000843 Tian W, Liao Z, Wang X (2019) Transfer learning for neural network model in chlorophyll-a dynamics prediction. Environ Sci Pollut Res 26(29):29857–29871 Wu W, Dandy GC, Maier HR (2014) Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling. Environ Model Softw 54:108–127. https://doi.org/10.1016/j.envsoft.2013.12.016 Download references Correspondence to Rana Muhammad Adnan. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Responsible Editor: Philippe Garrigues Reprints and Permissions Adnan, R.M., Kisi, O. Transfer learning for neural network model in chlorophyll-a dynamics prediction by Wenchong Tian, Zhenliang Liao, and Xuan Wang. Environ Sci Pollut Res (2020). https://doi.org/10.1007/s11356-020-09009-3 Download citation Received: 05 February 2020 Accepted: 22 April 2020 Published: 01 May 2020 DOI: https://doi.org/10.1007/s11356-020-09009-3

ACS Style

Rana Muhammad Adnan; Ozgur Kisi. Transfer learning for neural network model in chlorophyll-a dynamics prediction by Wenchong Tian, Zhenliang Liao, and Xuan Wang. Environmental Science and Pollution Research 2020, 27, 30899 -30900.

AMA Style

Rana Muhammad Adnan, Ozgur Kisi. Transfer learning for neural network model in chlorophyll-a dynamics prediction by Wenchong Tian, Zhenliang Liao, and Xuan Wang. Environmental Science and Pollution Research. 2020; 27 (24):30899-30900.

Chicago/Turabian Style

Rana Muhammad Adnan; Ozgur Kisi. 2020. "Transfer learning for neural network model in chlorophyll-a dynamics prediction by Wenchong Tian, Zhenliang Liao, and Xuan Wang." Environmental Science and Pollution Research 27, no. 24: 30899-30900.

Preprint content
Published: 23 March 2020
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River runoff prediction plays a very vital role in water resources planning, hydropower designing and agricultural water management. In the current study, the prediction capability of three machine learning models, least square support vector regression (LSSVR), fuzzy genetic (FG) and M5 model tree (M5Tree), in modeling daily and monthly runoffs of Hunza River catchment (HRC) using own and nearby Gilgit climatic station data is examined. The prediction performances of three machine learning models are compared using three statistical indexes, namely, root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). Firstly, four previous time lagged values of runoff, rainfall and atmospheric temperature are used as inputs on basis of correlation analysis to validate and test the accuracy of three machine learning models. After analyzing the performance of various input combinations, optimal one is selected for each variable and then these optimal inputs are employed together to see the forecasting performance. In the first part of study, monthly runoff of HRC are predicted using inputs consisting of local previous monthly runoff values and monthly meteorological values of Gilgit station. The test results show that LSSVR provides more accurate prediction results than the other two machine learning models. In the second part, daily runoffs of HRC are predicted using own previous daily runoff and Gilgit station’s climatic values. In the test results, a better accuracy is obtained from LSSVR models in relative to the FG and M5Tree models. In the last part of study, daily runoffs of HRC are predicted using own runoff and climatic data of HRC. In the results, it is found that local climatic data slightly improved the all model’s prediction accuracy in comparison of other scenario which also uses nearby station’s climatic data. The LSSVR models again are found to be better than the FGA and M5Tree models. LSSVM generally performs superior to the FGA and M5Tree in forecasting daily stream flow of Hunza River using local stream flow and climatic inputs. Based on the results of study, LSSVR model is recommended for monthly and daily runoff prediction of HRC with or without local climatic data.

ACS Style

Rana Muhammad Adnan Ikram; Zhongmin Liang; Ozgur Kisi; Muhammad Adnan; Binquan Li; Kuppusamy Sathishkumar. River flow prediction of Hunza River by LSSVR, fuzzy genetic and M5 model tree using nearby station’s meteorological data. 2020, 1 .

AMA Style

Rana Muhammad Adnan Ikram, Zhongmin Liang, Ozgur Kisi, Muhammad Adnan, Binquan Li, Kuppusamy Sathishkumar. River flow prediction of Hunza River by LSSVR, fuzzy genetic and M5 model tree using nearby station’s meteorological data. . 2020; ():1.

Chicago/Turabian Style

Rana Muhammad Adnan Ikram; Zhongmin Liang; Ozgur Kisi; Muhammad Adnan; Binquan Li; Kuppusamy Sathishkumar. 2020. "River flow prediction of Hunza River by LSSVR, fuzzy genetic and M5 model tree using nearby station’s meteorological data." , no. : 1.

Preprint content
Published: 23 March 2020
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Biochar is extensively used in environmental pollutant remediation because of its diverse property, however the effect of biochar on microbial nitrate reduction and electrochemical behavior of biochar remain unknown. Also electron transfer from the microbial cells to electron donor or acceptor have been transport across the extracellular polymeric substances (EPS), however it was unclear whether extracellular polymeric substances captured or enhance the electrons.  Hence, aim of the present study is to investigate the electrochemical behavior of biochar and its effects on microbial nitrate reduction and elucidate the role of extracellular polymeric substances in extracellular electron transfer (EET).  The biochar was prepared at different pyrolysis temperatures (400 °C, 500 °C and 600 °C) and their electrochemical behavior was characterized by electrochemical analysis (cyclic voltammetry, electrochemical impedance spectrum, chronoamperometry). Results demonstrated that all the biochars could donate and accept the electrons, impact of biochar on microbial nitrate reduction was studied and the results showed that biochar prepared at 400 °C significantly enhances microbial nitrate reduction process. Phenol O-H and quinone C=O surface functional groups on the biochar contributes in the overall electron exchange which accelerated the nitrate reduction. The role of EPS in EET by electrochemical analysis results reveals that outer membrane c-type cytochrome and flavin protein from the biofilm was involved in electron transfer process, and EPS act as transient media for microbial EET. Overall, present study suggested that biochar could be used as eco-friendly material for the enhancement of microbial denitrification.

ACS Style

Kuppusamy Sathishkumar; Yi Li; Rana Muhammad Adnan Ikram. Wood derived biochar as electron donor and its influence on microbial denitrification: Role of extracellular polymeric substances in extracellular electron transfer. 2020, 1 .

AMA Style

Kuppusamy Sathishkumar, Yi Li, Rana Muhammad Adnan Ikram. Wood derived biochar as electron donor and its influence on microbial denitrification: Role of extracellular polymeric substances in extracellular electron transfer. . 2020; ():1.

Chicago/Turabian Style

Kuppusamy Sathishkumar; Yi Li; Rana Muhammad Adnan Ikram. 2020. "Wood derived biochar as electron donor and its influence on microbial denitrification: Role of extracellular polymeric substances in extracellular electron transfer." , no. : 1.

Journal article
Published: 01 March 2020 in Water
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This study evaluates the effect of climate change on reference evapotranspiration (ET0), which is one of the most important variables in water resources management and irrigation scheduling. For this purpose, daily weather data of 30 Iranian weather stations from 1981 and 2010 were used. The HadCM3 statistical model was applied to report the output subscale of LARS-WG and to predict the weather information by A1B, A2, and B1 scenarios in three periods: 2011–2045, 2046–2079, and 2080–2113. The ET0 values were estimated by the Ref-ET software. The results indicated that the ET0 will rise from 2011 to 2113 approximately in all stations under three scenarios. The ET0 changes percentages in the A1B scenario during three periods from 2011 to 2113 were found to be 0.98%, 5.18%, and 12.17% compared to base period, respectively, while for the B1 scenario, they were calculated as 0.67%, 4.07%, and 6.61% and for the A2 scenario, they were observed as 0.59%, 5.35%, and 9.38%, respectively. Thus, the highest increase of the ET0 will happen from 2080 to 2113 under the A1B scenario; however, the lowest will occur between 2046 and 2079 under the B1 scenario. Furthermore, the assessment of uncertainty in the ET0 calculated by the different scenarios showed that the ET0 predicted under the A2 scenario was more reliable than the others. The spatial distribution of the ET0 showed that the highest ET0 amount in all scenarios belonged to the southeast and the west of the studied area. The most noticeable point of the results was that the ET0 differs from one scenario to another and from a period to another.

ACS Style

Maryam Bayatvarkeshi; Binqiao Zhang; Rojin Fasihi; Rana Muhammad Adnan; Ozgur Kisi; Xiaohui Yuan. Investigation into the Effects of Climate Change on Reference Evapotranspiration Using the HadCM3 and LARS-WG. Water 2020, 12, 666 .

AMA Style

Maryam Bayatvarkeshi, Binqiao Zhang, Rojin Fasihi, Rana Muhammad Adnan, Ozgur Kisi, Xiaohui Yuan. Investigation into the Effects of Climate Change on Reference Evapotranspiration Using the HadCM3 and LARS-WG. Water. 2020; 12 (3):666.

Chicago/Turabian Style

Maryam Bayatvarkeshi; Binqiao Zhang; Rojin Fasihi; Rana Muhammad Adnan; Ozgur Kisi; Xiaohui Yuan. 2020. "Investigation into the Effects of Climate Change on Reference Evapotranspiration Using the HadCM3 and LARS-WG." Water 12, no. 3: 666.

Journal article
Published: 19 November 2019 in Journal of Hydrology
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Rana 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.

Original paper
Published: 09 October 2019 in Arabian Journal of Geosciences
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Modeling pan evaporation (Epan) estimation is a vital issue in water resources management because it directly affects water reservoir and water supply systems. In the developing countries (e.g., India), Epan data are generally limited, and in such a circumstance, theoretical estimates from available climatic data could be beneficial. The study investigates the capability of three adaptive neuro-fuzzy methods, adaptive neuro-fuzzy inference system (ANFIS)–embedded grid partition (GP), subtractive clustering (SC), and fuzzy c-means clustering (FCM), in estimation of monthly pan evaporation using climatic inputs of minimum and maximum air temperatures, wind speed, sunshine hours, and relative humidity obtained from two stations, Uttarakhand, India. Cross validation method is applied by dividing data into three equal parts, and methods are tested using each part. Methods are evaluated by applying various combinations of inputs and using root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and determination coefficient (R2) criteria. The ANFIS-FCM is found to be superior to the ANFIS-GP and ANFIS-SC methods in Epan modeling. Cluster-based proposed neuro-fuzzy method increases performance of the best ANFIS-GP and ANFIS-SC models with respect to RMSE by about 9–14% for the both stations. The three ANFIS methods are also compared with each other and Stephen Stewart (SS) method by dividing data into three stages, training, validation, and test. The results indicate the superior accuracy of the ANFIS methods to SS for the same input variables. The ANFIS-FCM generally produces better Epan estimates than the other two ANFIS methods.

ACS Style

Rana Muhammad Adnan; Anurag Malik; Anil Kumar; Kulwinder Singh Parmar; Ozgur Kisi. Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs. Arabian Journal of Geosciences 2019, 12, 606 .

AMA Style

Rana Muhammad Adnan, Anurag Malik, Anil Kumar, Kulwinder Singh Parmar, Ozgur Kisi. Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs. Arabian Journal of Geosciences. 2019; 12 (19):606.

Chicago/Turabian Style

Rana Muhammad Adnan; Anurag Malik; Anil Kumar; Kulwinder Singh Parmar; Ozgur Kisi. 2019. "Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs." Arabian Journal of Geosciences 12, no. 19: 606.

Journal article
Published: 02 October 2019 in Water
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Estimation of suspended sediments carried by natural rivers is essential for projects related to water resource planning and management. This study proposes a dynamic evolving neural fuzzy inference system (DENFIS) as an alternative tool to estimate the suspended sediment load based on previous values of streamflow and sediment. Several input scenarios of daily streamflow and suspended sediment load measured at two locations of China—Guangyuan and Beibei—were tried to assess the ability of this new method and its results were compared with those of the other two common methods, adaptive neural fuzzy inference system with fuzzy c-means clustering (ANFIS-FCM) and multivariate adaptive regression splines (MARS) based on three commonly utilized statistical indices, root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE). The data period covers 01/04/2007–12/31/2015 for the both stations. A comparison of the methods indicated that the DENFIS-based models improved the accuracy of the ANFIS-FCM and MARS-based models with respect to RMSE by 33% (32%) and 31% (36%) for the Guangyuan (Beibei) station, respectively. The NSE accuracy for ANFIS-FCM and MARS-based models were increased by 4% (36%) and 15% (19%) using DENFIS for the Guangyuan (Beibei) station, respectively. It was found that the suspended sediment load can be accurately estimated by DENFIS-based models using only previous streamflow data.

ACS Style

Rana Muhammad Adnan; Zhongmin Liang; Ahmed El-Shafie; Mohammad Zounemat-Kermani; Ozgur Kisi. Prediction of Suspended Sediment Load Using Data-Driven Models. Water 2019, 11, 2060 .

AMA Style

Rana Muhammad Adnan, Zhongmin Liang, Ahmed El-Shafie, Mohammad Zounemat-Kermani, Ozgur Kisi. Prediction of Suspended Sediment Load Using Data-Driven Models. Water. 2019; 11 (10):2060.

Chicago/Turabian Style

Rana Muhammad Adnan; Zhongmin Liang; Ahmed El-Shafie; Mohammad Zounemat-Kermani; Ozgur Kisi. 2019. "Prediction of Suspended Sediment Load Using Data-Driven Models." Water 11, no. 10: 2060.

Short communication
Published: 23 July 2019 in Journal of Hydrology
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Daily streamflow prediction is important for flood warning, navigation, sediment control, reservoir operations and environmental protection. The current paper examines the prediction and estimation capability of a new heuristic method, optimally pruned extreme learning machine (OP-ELM) model, for daily streamflows of Fujiangqiao and Shehang stations at Fujiang River. Prediction accuracy of OP-ELM method is compared with other soft computing models, i.e. adaptive neuro-fuzzy inference system- particle swarm optimization (ANFIS-PSO), multivariate adaptive regression splines (MARS) and M5 model tree (M5Tree) using cross validation technique. Prediction results of the both stations reported that the OP-ELM and ANFIS-PSO are the best in modeling daily streamflows of upstream and downstream, respectively. For improving prediction accuracy of the OP-ELM method, various kernel types are tried and the linear, linear+sigmoid+Gaussian and linear+sigmoid provide the best results for both stations. The OP-ELM outperforms the other methods during estimation of downstream streamflow using hydro climatic data as input. The OP-ELM reduces the prediction error of ANFIS-PSO by 12% in estimation of daily streamflow. It is also found that including local data considerably improves the prediction accuracy in estimation of downstream streamflows. The overall results indicate that the OP-ELM method could be successfully used in predicting and estimating daily streamflow by using hydro climatic data as inputs.

ACS Style

Rana Muhammad Adnan; Zhongmin Liang; Slavisa Trajkovic; Mohammad Zounemat-Kermani; Binquan Li; Ozgur Kisi. Daily streamflow prediction using optimally pruned extreme learning machine. Journal of Hydrology 2019, 577, 123981 .

AMA Style

Rana Muhammad Adnan, Zhongmin Liang, Slavisa Trajkovic, Mohammad Zounemat-Kermani, Binquan Li, Ozgur Kisi. Daily streamflow prediction using optimally pruned extreme learning machine. Journal of Hydrology. 2019; 577 ():123981.

Chicago/Turabian Style

Rana Muhammad Adnan; Zhongmin Liang; Slavisa Trajkovic; Mohammad Zounemat-Kermani; Binquan Li; Ozgur Kisi. 2019. "Daily streamflow prediction using optimally pruned extreme learning machine." Journal of Hydrology 577, no. : 123981.

Journal article
Published: 01 June 2019 in Proceedings of the Institution of Civil Engineers - Water Management
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The accuracy of five soft computing techniques was assessed for the prediction of monthly streamflow of the Gilgit river basin by a cross-validation method. The five techniques assessed were the feed-forward neural network (FFNN), the radial basis neural network (RBNN), the generalised regression neural network (GRNN), the adaptive neuro fuzzy inference system with grid partition (Anfis-GP) and the adaptive neuro fuzzy inference system with subtractive clustering (Anfis-SC). The interaction between temperature and streamflow was considered in the study. Two statistical indexes, mean square error (MSE) and coefficient of determination (R2), were used to evaluate the performances of the models. In all applications, RBNN and Anfis-SC were found to give more accurate results than the FFNN, GRNN and Anfis-GP models. The effect of periodicity was also examined by adding a periodicity component into the applied models and the results were compared with a statistical model (seasonal autoregressive integrated moving average (Sarima)) to check the prediction accuracy. The results of this comparison showed that periodicity inputs improved the prediction accuracy of the applied models and, in all cases, the soft computing models performed much better than the Sarima model. The periodic RBNN and Anfis-SC models increased the MSE accuracy of Sarima by 25·5–24·7%.

ACS Style

Rana Muhammad Adnan; Xiaohui Yuan; Ozgur Kisi; Yanbin Yuan; Muhammad Tayyab; Xiaohui Lei. Application of soft computing models in streamflow forecasting. Proceedings of the Institution of Civil Engineers - Water Management 2019, 172, 123 -134.

AMA Style

Rana Muhammad Adnan, Xiaohui Yuan, Ozgur Kisi, Yanbin Yuan, Muhammad Tayyab, Xiaohui Lei. Application of soft computing models in streamflow forecasting. Proceedings of the Institution of Civil Engineers - Water Management. 2019; 172 (3):123-134.

Chicago/Turabian Style

Rana Muhammad Adnan; Xiaohui Yuan; Ozgur Kisi; Yanbin Yuan; Muhammad Tayyab; Xiaohui Lei. 2019. "Application of soft computing models in streamflow forecasting." Proceedings of the Institution of Civil Engineers - Water Management 172, no. 3: 123-134.

Journal article
Published: 21 January 2019 in Energies
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Accurate predictions of wind speed and wind energy are essential in renewable energy planning and management. This study was carried out to test the accuracy of two different neuro fuzzy techniques (neuro fuzzy system with grid partition (NF-GP) and neuro fuzzy system with substractive clustering (NF-SC)), and two heuristic regression methods (least square support vector regression (LSSVR) and M5 regression tree (M5RT)) in the prediction of hourly wind speed and wind power using a cross-validation method. Fourfold cross-validation was employed by dividing the data into four equal subsets. LSSVR’s performance was superior to that of the M5RT, NF-SC, and NF-GP models for all datasets in wind speed prediction. The overall average root-mean-square errors (RMSE) of the M5RT, NF-GP, and NF-SC models decreased by 11.71%, 1.68%, and 2.94%, respectively, using the LSSVR model. The applicability of the four different models was also investigated in the prediction of one-hour-ahead wind power. The results showed that NF-GP’s performance was superior to that of LSSVR, NF-SC, and M5RT. The overall average RMSEs of LSSVR, NF-SC, and M5RT decreased by 5.52%, 1.30%, and 15.6%, respectively, using NF-GP.

ACS Style

Rana Muhammad Adnan; Zhongmin Liang; Xiaohui Yuan; Ozgur Kisi; Muhammad Akhlaq; Binquan Li. Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation. Energies 2019, 12, 329 .

AMA Style

Rana Muhammad Adnan, Zhongmin Liang, Xiaohui Yuan, Ozgur Kisi, Muhammad Akhlaq, Binquan Li. Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation. Energies. 2019; 12 (2):329.

Chicago/Turabian Style

Rana Muhammad Adnan; Zhongmin Liang; Xiaohui Yuan; Ozgur Kisi; Muhammad Akhlaq; Binquan Li. 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation." Energies 12, no. 2: 329.

Article
Published: 25 August 2018 in Water Resources Management
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Forecasting stream flow is a very importance issue in water resources planning and management. The ability of three soft computing methods, least square support vector machine (LSSVM), fuzzy genetic algorithm (FGA) and M5 model tree (M5T), in forecasting daily and monthly stream flows of poorly gauged mountainous watershed using nearby hydro-meteorological data is investigated in the current study. In the first application, monthly stream flows of Hunza river are forecasted using local stream flow data of Hunza and precipitation and temperature data of nearby station. LSSVM provides slightly better forecasts than the FGA and M5T models. Stream flow and temperature inputs generally give better forecasts compared to other inputs. In the second application, daily stream flows of Hunza river are forecasted using local stream flow data of Hunza and precipitation and temperature data of nearby station. Better results are obtained from the models comprising only stream flow inputs. In general, a better accuracy is obtained from LSSVM models in relative to the FGA and M5T. The results indicate that the monthly and daily stream flows of Hunza can be accurately forecasted by using only nearby climatic data. In the third application, daily stream flows of Hunza river are forecasted using local stream flow and climatic data and the models’ accuracy is slightly increased in relative to the previous applications. LSSVM generally performs superior to the FGA and M5T in forecasting daily stream flow of Hunza river using local stream flow and climatic inputs.

ACS Style

Rana Muhammad Adnan; Xiaohui Yuan; Ozgur Kisi; Muhammad Adnan; Asif Mehmood. Stream Flow Forecasting of Poorly Gauged Mountainous Watershed by Least Square Support Vector Machine, Fuzzy Genetic Algorithm and M5 Model Tree Using Climatic Data from Nearby Station. Water Resources Management 2018, 32, 4469 -4486.

AMA Style

Rana Muhammad Adnan, Xiaohui Yuan, Ozgur Kisi, Muhammad Adnan, Asif Mehmood. Stream Flow Forecasting of Poorly Gauged Mountainous Watershed by Least Square Support Vector Machine, Fuzzy Genetic Algorithm and M5 Model Tree Using Climatic Data from Nearby Station. Water Resources Management. 2018; 32 (14):4469-4486.

Chicago/Turabian Style

Rana Muhammad Adnan; Xiaohui Yuan; Ozgur Kisi; Muhammad Adnan; Asif Mehmood. 2018. "Stream Flow Forecasting of Poorly Gauged Mountainous Watershed by Least Square Support Vector Machine, Fuzzy Genetic Algorithm and M5 Model Tree Using Climatic Data from Nearby Station." Water Resources Management 32, no. 14: 4469-4486.