Uncertainty Assessment of Surface Water Salinity Using Standalone, Ensemble, and Deep Machine Learning Methods: A Case Study of Lake Urmia
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Abdi A, Hassanzadeh Y, Talatahari S, Fakheri-Fard A, Mirabbasi R (2017) Regional drought frequency analysis using L-moments and adjusted charged system search. J Hydroinf 19:426–442
Ahmed U, Mumtaz R, Anwar H, Shah AA, Irfan R, García-Nieto JJW (2019) Efficient water quality prediction using supervised machine learning. Water 11:2210
Alharbi T (2023) Assessment of the Biyadh groundwater quality and geochemical process in Saudi Arabia using statistical, modelling, and WQI methods. J King Saud Univ-Sci 35:102847
Alqahtani A, Shah MI, Aldrees A, Javed MF (2022) Comparative assessment of individual and ensemble machine learning models for efficient analysis of river water quality. Sustainability 14:1183
Alsubih M, Mallick J, Islam ARMT, Almesfer MK, Kahla NB, Talukdar S, Ahmed MJW (2022) Assessing surface water quality for irrigation purposes in some dams of Asir Region Saudi Arabia using multi-statistical modeling approaches. Water 14:1439
Antanasijević D, Pocajt V, Perić-Grujić A, Ristić MJJOH (2014) Modelling of dissolved oxygen in the danube river using artificial neural networks and Monte Carlo simulation uncertainty analysis. J Hydrol 519:1895–1907
Banerjee A, Chakrabarty M, Rakshit N, Bhowmick AR, Ray S (2019) Environmental factors as indicators of dissolved oxygen concentration and zooplankton abundance: deep learning versus traditional regression approach. Ecol Ind 100:99–117
Belgiu M, Drăguţ L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24–31
Chen J-C, Chang N, Shieh W (2003) Assessing wastewater reclamation potential by neural network model. Eng Appl Artif Intell 16:149–157
el Bilali A, Taleb A, Brouziyne Y (2021) Groundwater quality forecasting using machine learning algorithms for irrigation purposes. Agric Water Manag 245:106625
Eldan R, Shamir O (2016) The power of depth for feedforward neural networks. In: Conference on learning theory, 2016. PMLR, pp 907–940
Farooq F, Nasir Amin M, Khan K, Rehan Sadiq M, Javed MF, Aslam F, Alyousef R (2020) A comparative study of random forest and genetic engineering programming for the prediction of compressive strength of high strength concrete (HSC). Appl Sci 10:7330
Feizi Z, Keshtkar AR, Afzali AJD (2019) Using geostatistical and deterministic modelling to identify spatial variability of groundwater quality. Desert 24:143–151
Govindaraju RS (2000) Artificial neural networks in hydrology. II: hydrologic applications. J Hydrol Eng 5(2):124–137. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(124)
Grant EL, Leavenworth RS (1980) Statistical quality control. McGraw-Hill, New York
Huang F, Huang J, Jiang S, Zhou C (2017) Landslide displacement prediction based on multivariate chaotic model and extreme learning machine. Eng Geol 218:173–186
Islam MA, Rahman MM, Bodrud-Doza M, Muhib MI, Shammi M, Zahid A, Akter Y, Kurasaki MJAG (2018) A study of groundwater irrigation water quality in South-Central Bangladesh: a geo-statistical model approach using GIS and multivariate statistics. Acta Geochimica 37:193–214
Jensen BA (1994) Expert systems—neural networks. Process Control. Elsevier, Netherlands
Jiang Y, Nan Z, Yang S (2013) Risk assessment of water quality using Monte Carlo simulation and artificial neural network method. J Environ Manage 122:130–136
Juna A, Umer M, Sadiq S, Karamti H, Eshmawi AA, Mohamed A, Ashraf IJW (2022) Water quality prediction using KNN imputer and multilayer perceptron. Water 14:2592
Kasiviswanathan K, Cibin R, Sudheer K, Chaubey I (2013) Constructing prediction interval for artificial neural network rainfall runoff models based on ensemble simulations. J Hydrol 499:275–288
Khosravi A, Nahavandi S, Creighton D (2010) A prediction interval-based approach to determine optimal structures of neural network metamodels. Expert Syst Appl 37:2377–2387
Khosravi A, Nahavandi S, Creighton D, Atiya AF (2011) Comprehensive review of neural network-based prediction intervals and new advances. IEEE Trans Neural Networks 22:1341–1356
Kim Y-J, Yura E, Kim T-W, Yoon J-S (2019) Development of disaster prevention system based on deep neural network using deep learning with dropout. J Coastal Res 91:186–190
Krupnick A, Morgenstern R, Batz M, Nelson P, Burtraw D, Shih JS, McWilliams M (2006) Not a sure thing: making regulatory choices under uncertainty, Resources for the Future Washington, DC
Kurunç A, Yürekli K, Cevik O (2005) Performance of two stochastic approaches for forecasting water quality and streamflow data from Yeşilιrmak River, Turkey. Environ Model Softw 20:1195–1200
Ma J, Niu X, Tang H, Wang Y, Wen T, Zhang J (2020) Displacement prediction of a complex landslide in the Three Gorges Reservoir Area (China) using a hybrid computational intelligence approach. Complexity 2020:1–15
Misra D, Oommen T, Agarwal A, Mishra SK, Thompson AM (2009) Application and analysis of support vector machine based simulation for runoff and sediment yield. Biosys Eng 103:527–535
Mohanty S, Jha MK, Kumar A, Panda D (2013) Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in Kathajodi-Surua Inter-basin of Odisha, India. J Hydrol 495:38–51
Morgan MG, Henrion M (1990) Uncertainty: a Guide to dealing with uncertainty in quantitative risk and policy analysis Cambridge University Press. New York, USA, New York
Noori R, Karbassi A, Ashrafi K, Ardestani M, Mehrdadi N (2013a) Development and application of reduced-order neural network model based on proper orthogonal decomposition for BOD 5 monitoring: Active and online prediction. Environ Prog Sustain Energy 32:120–127
Noori R, Safavi S, Shahrokni SAN (2013b) A reduced-order adaptive neuro-fuzzy inference system model as a software sensor for rapid estimation of five-day biochemical oxygen demand. J Hydrol 495:175–185
Noori R, Yeh H-D, Abbasi M, Kachoosangi FT, Moazami S (2015) Uncertainty analysis of support vector machine for online prediction of five-day biochemical oxygen demand. J Hydrol 527:833–843
Noori R, Deng Z, Kiaghadi A, Kachoosangi FT (2016) How reliable are ANN, ANFIS, and SVM techniques for predicting longitudinal dispersion coefficient in natural rivers? J Hydraul Eng 142:04015039
Nourani V, Paknezhad NJ, Sharghi E, Khosravi A (2019) Estimation of prediction interval in ANN-based multi-GCMs downscaling of hydro-climatologic parameters. J Hydrol 579:124226
Nourani V, Khodkar K, Gebremichael M (2022) Uncertainty assessment of LSTM based groundwater level predictions. Hydrol Sci J 67:773–790
Nouri H, Mason RJ, Moradi N (2017) Land suitability evaluation for changing spatial organization in Urmia County towards conservation of Lake Urmia. Appl Geogr 81:1–12
Othman F, Sadeghian MS, Heydari M, Sohrabi MS (2013) Prediction of water level and salinity of lakes by using artificial neural networks, case study: Lake Uremia. In: 35th international association for hydro-environmental engineering and research (IAHR), pp 8–13
Panahi H, Genikomsou AJSD, Engineering E (2023) A machine-learning-based model for seismic performance assessment of interior slab-column connections. Soil Dyn Earthq Eng 171:107943
Quan H, Srinivasan D, Khosravi A (2014) Incorporating wind power forecast uncertainties into stochastic unit commitment using neural network-based prediction intervals. IEEE Trans Neural Netw Learn Syst 26:2123–2135
Raheli B, Aalami MT, El-Shafie A, Ghorbani MA, Deo RC (2017) Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River. Environ Earth Sci 76:1–16
Refsgaard JC, van der Sluijs JP, Brown J, van der Keur P (2006) A framework for dealing with uncertainty due to model structure error. Adv Water Resour 29:1586–1597
Roy C, Motamedi S, Hashim R, Shamshirband S, Petković D (2016) A comparative study for estimation of wave height using traditional and hybrid soft-computing methods. Environ Earth Sci 75:590
Seifi A, Dehghani M, Singh VP (2020) Uncertainty analysis of water quality index (WQI) for groundwater quality evaluation: application of Monte-Carlo method for weight allocation. Ecol Ind 117:106653
Shah MI, Javed MF, Abunama T (2020) Proposed formulation of surface water quality and modelling using gene expression, machine learning, and regression techniques. Environ Sci Pollut Res 28:13202–13220
Sharghi E, Paknezhad NJ, Najafi H (2021) Assessing the effect of emotional unit of emotional ANN (EANN) in estimation of the prediction intervals of suspended sediment load modeling. Earth Sci Inf 14:201–213
Shen R, Huang A, Li B, Guo J (2019) Construction of a drought monitoring model using deep learning based on multi-source remote sensing data. Int J Appl Earth Obs Geoinf 79:48–57
Shrestha DL, Solomatine DP (2008) Data-driven approaches for estimating uncertainty in rainfall-runoff modelling. Int J River Basin Manag 6:109–122
Srivastav RK, Sudheer KP, Chaubey I (2007) A simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models. Water Resour Res. https://doi.org/10.1029/2006WR005352
Sun Z, Long D, Yang W, Li X, Pan Y (2020) Reconstruction of GRACE data on changes in total water storage over the global land surface and 60 basins. Water Resourc Res 56:e2019WR026250
Tang G, Long D, Behrangi A, Wang C, Hong Y (2018) Exploring deep neural networks to retrieve rain and snow in high latitudes using multisensor and reanalysis data. Water Resour Res 54:8253–8278
Tibshirani RJ, Efron B (1993) An introduction to the bootstrap. Monographs Stat Appl Probab 57:158
Trabelsi F, Bel Hadj Ali S (2022) Exploring machine learning models in predicting irrigation groundwater quality indices for effective decision making in Medjerda River basin, Tunisia. Sustainability 14:2341
Tut Haklidir FS, Haklidir M (2020) Prediction of reservoir temperatures using hydrogeochemical data, Western Anatolia geothermal systems (Turkey): a machine learning approach. Nat Resour Res 29:2333–2346
Tyralis H, Papacharalampous G, Langousis AJW (2019) A brief review of random forests for water scientists and practitioners and their recent history in water resources. Water 11:910
Uddin MG, Nash S, Rahman A, Olbert AI (2022) A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches. Water Res 229:119422
Vapnik VN (1995) The nature of statistical learning theory. Springer New York, New York. https://doi.org/10.1007/978-1-4757-2440-0
Voyant C, Notton G, Duchaud J-L, Almorox J, Yaseen ZM (2020) Solar irradiation prediction intervals based on Box-Cox transformation and univariate representation of periodic autoregressive model. Renewable Energy Focus 33:43–53
Wang N, Zhang D, Chang H, Li H (2020) Deep learning of subsurface flow via theory-guided neural network. J Hydrol 584:124700
Wu W, Zucca C, Muhaimeed AS, Al-Shafie WM, Fadhil Al-Quraishi AM, Nangia V, Zhu M, Liu G (2018) Soil salinity prediction and mapping by machine learning regression in C entral M esopotamia, I raq. Land Degrad Dev 29:4005–4014
Yarahmadi D (2014) Hydroclimatology analysis of water level fluctuations in Lake Urmia. Phys Geograp Res Quart 46:77–92
Yim I, Shin J, Lee H, Park S, Nam G, Kang T, Cho KH, Cha Y (2020) Deep learning-based retrieval of cyanobacteria pigment in inland water for in-situ and airborne hyperspectral data. Ecol Ind 110:105879
Zhang F, O’Donnell LJ (2020) Support vector regression. Machine learning. Elsevier, pp 123–140. https://doi.org/10.1016/B978-0-12-815739-8.00007-9