Citations (41)
Keep up to date with the latest research on this topic with citation updates for this article.
Read on this site (2)
Sameer Arora & Ashok K. Keshari. (2022) Modelling re-aeration of rivers using predictive models and developed ANN models under varying hydrodynamic conditions. Hydrological Sciences Journal 67:15, pages 2294-2309.
Read now
Read now
Liuxiao Chen, Tangzhi Liu, Boming Tang, Hao Xiang & Qijin Sheng. (2021) Modelling traffic noise in a wide gradient interval using artificial neural networks. Environmental Technology 42:23, pages 3561-3571.
Read now
Read now
Articles from other publishers (39)
Jiahao Yang. (2023) Predicting water quality through daily concentration of dissolved oxygen using improved artificial intelligence. Scientific Reports 13:1.
Crossref
Crossref
Sameer Arora & Ashok K. Keshari. (2023) Implementing Machine Learning Algorithm to Model Reaeration Coefficient of Urbanized Rivers. Environmental Modeling & Assessment 28:4, pages 535-546.
Crossref
Crossref
Huan Wu, Feng Jiang, Zehua Zhao, Liang Tao, Lin Peng, Wenxun Han & Min Gao. (2022) Water Quality Prediction Based on Multi-Indicator Relation Learning and Knowledge Transfer. Water Quality Prediction Based on Multi-Indicator Relation Learning and Knowledge Transfer.
Huan Wu, Shuiping Cheng, Kunlun Xin, Nian Ma, Jie Chen, Liang Tao & Min Gao. (2022) Water Quality Prediction Based on Multi-Task Learning. International Journal of Environmental Research and Public Health 19:15, pages 9699.
Crossref
Crossref
Bemah Ibrahim, Anthony Ewusi, Isaac Ahenkorah & Yao Yevenyo Ziggah. (2022) Modelling of arsenic concentration in multiple water sources: A comparison of different machine learning methods. Groundwater for Sustainable Development 17, pages 100745.
Crossref
Crossref
Rana Muhammad Adnan, Hong-Liang Dai, Reham R. Mostafa, Kulwinder Singh Parmar, Salim Heddam & Ozgur Kisi. (2022) Modeling Multistep Ahead Dissolved Oxygen Concentration Using Improved Support Vector Machines by a Hybrid Metaheuristic Algorithm. Sustainability 14:6, pages 3470.
Crossref
Crossref
Na Zhao, Zemeng Fan & Miaomiao Zhao. (2021) A New Approach for Estimating Dissolved Oxygen Based on a High-Accuracy Surface Modeling Method. Sensors 21:12, pages 3954.
Crossref
Crossref
Mohammad Hafez Ahmed & Lian-Shin Lin. (2021) Dissolved oxygen concentration predictions for running waters with different land use land cover using a quantile regression forest machine learning technique. Journal of Hydrology 597, pages 126213.
Crossref
Crossref
George Iordache, Mihaela Balanescu, George Suciu, Andrei Birdici, Adrian Pasat, Ionel Zatreanu, Mirel Paun & Stefania Bucuci. (2021) The PIMEO AI project – a Cloud based platform for water quality monitoring. The PIMEO AI project – a Cloud based platform for water quality monitoring.
Sakshi Khullar & Nanhey Singh. (2021) Machine learning techniques in river water quality modelling: a research travelogue. Water Supply 21:1, pages 1-13.
Crossref
Crossref
Sinan NACAR, Betül METE & Adem BAYRAM. (2020) GÜNLÜK ÇÖZÜNMÜŞ OKSİJEN KONSANTRASYONUNUN ÇOK DEĞİŞKENLİ UYARLANABİLİR REGRESYON EĞRİLERİ İLE TAHMİN EDİLMESİ. Uludağ University Journal of The Faculty of Engineering, pages 1479-1498.
Crossref
Crossref
Taher Rajaee, Salar Khani & Masoud Ravansalar. (2020) Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: A review. Chemometrics and Intelligent Laboratory Systems 200, pages 103978.
Crossref
Crossref
Sinan Nacar, Adem Bayram, Osman Tugrul Baki, Murat Kankal & Egemen Aras. (2020) Spatial Forecasting of Dissolved Oxygen Concentration in the Eastern Black Sea Basin, Turkey. Water 12:4, pages 1041.
Crossref
Crossref
Senlin Zhu & Salim Heddam. (2020) Prediction of dissolved oxygen in urban rivers at the Three Gorges Reservoir, China: extreme learning machines (ELM) versus artificial neural network (ANN). Water Quality Research Journal 55:1, pages 106-118.
Crossref
Crossref
Anita Csábrági, Sándor Molnár, Péter Tanos, József Kovács, Márk Molnár, István Szabó & István Gábor Hatvani. (2019) Estimation of dissolved oxygen in riverine ecosystems: Comparison of differently optimized neural networks. Ecological Engineering 138, pages 298-309.
Crossref
Crossref
Xudong Sun, Junbin Liu, Ke Zhu, Jun Hu, Xiaogang Jiang & Yande Liu. (2019) Generalized regression neural network association with terahertz spectroscopy for quantitative analysis of benzoic acid additive in wheat flour. Royal Society Open Science 6:7, pages 190485.
Crossref
Crossref
Arnab Banerjee, Moitreyee Chakrabarty, Nabyendu Rakshit, Amiya Ranjan Bhowmick & Santanu Ray. (2019) Environmental factors as indicators of dissolved oxygen concentration and zooplankton abundance: Deep learning versus traditional regression approach. Ecological Indicators 100, pages 99-117.
Crossref
Crossref
Behrooz Keshtegar, Salim Heddam & Hamidreza Hosseinabadi. (2019) The employment of polynomial chaos expansion approach for modeling dissolved oxygen concentration in river. Environmental Earth Sciences 78:1.
Crossref
Crossref
Behrooz Keshtegar & Salim Heddam. (2017) Modeling daily dissolved oxygen concentration using modified response surface method and artificial neural network: a comparative study. Neural Computing and Applications 30:10, pages 2995-3006.
Crossref
Crossref
Salim Heddam & Ozgur Kisi. (2018) Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree. Journal of Hydrology 559, pages 499-509.
Crossref
Crossref
Aleksandra Šiljić Tomić, Davor Antanasijević, Mirjana Ristić, Aleksandra Perić-Grujić & Viktor Pocajt. (2018) A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: Inter- and extrapolation performance with inputs' significance analysis. Science of The Total Environment 610-611, pages 1038-1046.
Crossref
Crossref
Z. Sekuli?D. Antanasijevi?S. Stevanovi?K. Trivunac. (2017) Application of artificial neural networks for estimating Cd, Zn, Pb removal efficiency from wastewater using complexation-microfiltration process. International Journal of Environmental Science and Technology 14:7, pages 1383-1396.
Crossref
Crossref
Salim Heddam & Ozgur Kisi. (2017) Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors. Environmental Science and Pollution Research 24:20, pages 16702-16724.
Crossref
Crossref
Xiaoliang Ji, Xu Shang, Randy A. Dahlgren & Minghua Zhang. (2017) Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China. Environmental Science and Pollution Research 24:19, pages 16062-16076.
Crossref
Crossref
Usman Khan & Caterina Valeo. (2017) Optimising Fuzzy Neural Network Architecture for Dissolved Oxygen Prediction and Risk Analysis. Water 9:6, pages 381.
Crossref
Crossref
Anita Csábrági, Sándor Molnár, Péter Tanos & József Kovács. (2017) Application of artificial neural networks to the forecasting of dissolved oxygen content in the Hungarian section of the river Danube. Ecological Engineering 100, pages 63-72.
Crossref
Crossref
Salim Heddam. (2016) Generalized Regression Neural Network Based Approach as a New Tool for Predicting Total Dissolved Gas (TDG) Downstream of Spillways of Dams: a Case Study of Columbia River Basin Dams, USA. Environmental Processes 4:1, pages 235-253.
Crossref
Crossref
Salar Khani & Taher Rajaee. (2017) Modeling of Dissolved Oxygen Concentration and Its Hysteresis Behavior in Rivers Using Wavelet Transform-Based Hybrid Models. CLEAN - Soil, Air, Water 45:2.
Crossref
Crossref
Salim Heddam. 2017. Intelligence Systems in Environmental Management: Theory and Applications. Intelligence Systems in Environmental Management: Theory and Applications
231
253
.
Salim Heddam. (2016) New modelling strategy based on radial basis function neural network (RBFNN) for predicting dissolved oxygen concentration using the components of the Gregorian calendar as inputs: case study of Clackamas River, Oregon, USA. Modeling Earth Systems and Environment 2:4, pages 1-5.
Crossref
Crossref
Salim Heddam. (2016) Use of Optimally Pruned Extreme Learning Machine (OP-ELM) in Forecasting Dissolved Oxygen Concentration (DO) Several Hours in Advance: a Case Study from the Klamath River, Oregon, USA. Environmental Processes 3:4, pages 909-937.
Crossref
Crossref
Salim Heddam. (2016) Simultaneous modelling and forecasting of hourly dissolved oxygen concentration (DO) using radial basis function neural network (RBFNN) based approach: a case study from the Klamath River, Oregon, USA. Modeling Earth Systems and Environment 2:3.
Crossref
Crossref
Usman T. Khan & Caterina Valeo. (2016) Dissolved oxygen prediction using a possibility theory based fuzzy neural network. Hydrology and Earth System Sciences 20:6, pages 2267-2293.
Crossref
Crossref
Salim Heddam, Hilal Lamda & Samir Filali. (2016) Predicting Effluent Biochemical Oxygen Demand in a Wastewater Treatment Plant Using Generalized Regression Neural Network Based Approach: A Comparative Study. Environmental Processes 3:1, pages 153-165.
Crossref
Crossref
U. T. Khan & C. Valeo. (2015) Dissolved oxygen prediction using a possibility-theory based fuzzy neural network. Hydrology and Earth System Sciences Discussions 12:11, pages 12311-12376.
Crossref
Crossref
Davor Antanasijević, Viktor Pocajt, Mirjana Ristić & Aleksandra Perić-Grujić. (2015) Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks. Energy 84, pages 816-824.
Crossref
Crossref
Adem Bayram, Ergun Uzlu, Murat Kankal & Tayfun Dede. (2014) Modeling stream dissolved oxygen concentration using teaching–learning based optimization algorithm. Environmental Earth Sciences 73:10, pages 6565-6576.
Crossref
Crossref
Aleksandra ?ilji?Davor Antanasijevi?Aleksandra Peri?-Gruji?Mirjana Risti?Viktor Pocajt. (2014) Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations. Environmental Science and Pollution Research 22:6, pages 4230-4241.
Crossref
Crossref
Davor Antanasijević, Viktor Pocajt, Aleksandra Perić-Grujić & Mirjana Ristić. (2014) Modelling of dissolved oxygen in the Danube River using artificial neural networks and Monte Carlo Simulation uncertainty analysis. Journal of Hydrology 519, pages 1895-1907.
Crossref
Crossref