References
- X. Ding, Q. Zhu, A. Zhai, L. Liu, Water quality safety prediction
model for drinking water source areas in Three Gorges
Reservoir and its application, Ecol. Indic., 101 (2019) 734–741.
- K.W. Abdelmalik, Role of statistical remote sensing for Inland
water quality parameters prediction, Egypt. J. Remote Sens.
Space Sci., 21 (2018) 193–200.
- D.J. Booker, R.A. Woods, Comparing and combining physicallybased
and empirically-based approaches for estimating the
hydrology of ungauged catchments, J. Hydrol., 508 (2014)
227–239.
- U. Seeboonruang, A statistical assessment of the impact of land
uses on surface water quality indexes, J. Environ. Manage.,
101 (2012) 134–142.
- M. Gevrey, L. Comte, D. de Zwart, E. de Deckere, S. Lek,
Modeling the chemical and toxic water status of the Scheldt
basin (Belgium), using aquatic invertebrate assemblages and
an advanced modeling method, Environ. Pollut., 158 (2010)
3209–3218.
- X. Xin, K. Li, B. Finlayson, W. Yin, Evaluation, prediction, and
protection of water quality in Danjiangkou Reservoir, China,
Water Sci. Eng., 8 (2015) 30–39.
- H. Runtti, S. Tuomikoski, T. Kangas, T. Kuokkanen, J. Rämö,
U. Lassi, Sulphate removal from water by carbon residue
from biomass gasification: effect of chemical modification
methods on sulphate removal efficiency, Bioresources, 11 (2016)
3136–3152.
- C. Koschmann, A.-A. Calinescu, F.J. Nunez, A. Mackay,
J. Fazal-Salom, D. Thomas, F. Mendez, N. Kamran, M. Dzaman,
L. Mulpuri, ATRX loss promotes tumor growth and impairs
nonhomologous end joining DNA repair in glioma, Sci. Transl.
Med., 8 (2016) 328ra28, doi: 10.1126/scitranslmed.aac8228.
- D. Guimarães, V.A. Leão, Batch and fixed-bed assessment
of sulphate removal by the weak base ion exchange resin
Amberlyst A21, J. Hazard. Mater., 280 (2014) 209–215.
- W. Chen, R. Zheng, P.D. Baade, S. Zhang, H. Zeng, F. Bray,
A. Jemal, X.Q. Yu, J. He, Cancer statistics in China, 2015,
Cancer J. Clin., 66 (2016) 115–132.
- A. El Hmaidi, H. El Badaoui, A. Abdallaoui, B. El Moumni,
Application des réseaux de neurones artificiels de type PMC
pour la prédiction des teneurs en carbone organique dans les
dépôts du quaternaire terminal de la mer d’Alboran, Eur. J. Sci.
Res., 107 (2013) 400–413.
- T. Rajaee, S. Khani, M. Ravansalar, Artificial intelligence-based
single and hybrid models for prediction of water quality in
rivers: a review, Chemom. Intell. Lab. Syst., 200 (2020) 1–25,
doi: 10.1016/j.chemolab.2020.103978.
- W. Deng, G. Wang, X. Zhang, A novel hybrid water quality
time series prediction method based on cloud model and fuzzy
forecasting, Chemom. Intell. Lab. Syst. 149 (2015) 39–49.
- A.H. Haghiabi, A.H. Nasrolahi, A. Parsaie, Water quality
prediction using machine learning methods, Water Qual. Res.
J., 53 (2018) 3–13.
- Z. Li, F. Peng, B. Niu, G. Li, J. Wu, Z. Miao, Water quality
prediction model combining sparse auto-encoder and LSTM
network, IFAC-PapersOnLine, 51 (2018) 831–836.
- H. Lu, X. Ma, Hybrid decision tree-based machine learning
models for short-term water quality prediction, Chemosphere,
249 (2020) 1–12, doi: 10.1016/j.chemosphere.2020.126169.
- H.J. Mayfield, E. Bertone, C. Smith, O. Sahin, Use of a structure
aware discretisation algorithm for Bayesian networks applied
to water quality predictions, Math. Comput. Simul., 175 (2020)
192–201.
- A.N. Ahmed, F.B. Othman, H.A. Afan, R.K. Ibrahim,
C.M. Fai, M.S. Hossain, M. Ehteram, A. Elshafie, Machine
learning methods for better water quality prediction, J. Hydrol.,
578 (2019) 1–18, doi: 10.1016/j.jhydrol.2019.124084.
- D. Wu, H. Wang, R. Seidu, Smart data driven quality prediction
for urban water source management, Future Gener. Comput.
Syst., 107 (2020) 418–432.
- K. Chen, H. Chen, C. Zhou, Y. Huang, X. Qi, R. Shen, F. Liu,
M. Zuo, X. Zou, J. Wang, Comparative analysis of surface
water quality prediction performance and identification of key
water parameters using different machine learning models
based on big data, Water Res., 171 (2020) 1–10, doi: 10.1016/j.
watres.2019.115454.
- R. Avila, B. Horn, E. Moriarty, R. Hodson, E. Moltchanova,
Evaluating statistical model performance in water quality
prediction, J. Environ. Manage., 206 (2018) 910–919.
- E. Farahani, M.R. Mosaddeghi, A.A. Mahboubi, A.R. Dexter,
Prediction of soil hard-setting and physical quality using water
retention data, Geoderma, 338 (2019) 343–354.
- M. Khadr, M. Elshemy, Data-driven modeling for water quality
prediction case study: the drains system associated with
Manzala Lake, Egypt, Ain Shams Eng. J., 8 (2017) 549–557.
- H. Ousmana, A.E. Hmaidi, M. Berrada, B. Damnati, I. Etabaai,
A. Essahlaoui, Development of a neural network approach
for predicting nitrate and sulfate concentration in three lakes:
Ifrah, Iffer and Afourgagh, Middle Atlas Morocco, Moroccan
J. Chem., 6 (2018) 245–255.
- L.-M.L. He, Z.-L. He, Water quality prediction of marine
recreational beaches receiving watershed baseflow and
stormwater runoff in southern California, USA, Water Res.,
42 (2008) 2563–2573.
- W.-C. Liu, W.-B. Chen, Prediction of water temperature in a
subtropical subalpine lake using an artificial neural network
and three-dimensional circulation models, Comput. Geosci.,
45 (2012) 13–25.
- A. Clementking, C.J. Venkateswaran, Prediction of Water
Quality Attributes Variations Using Back Propagation Neural
Network (BPNN) Model, International Conference on Technology
and Business Management (ICTBM-15), American
University in the Emirates, 2015, pp. 128–138.
- M. Heydari, E. Olyaie, H. Mohebzadeh, Ö. Kisi, Development
of a neural network technique for prediction of water quality
parameters in the Delaware River, Pennsylvania, Middle-East J.
Sci. Res., 13 (2013) 1367–1376.
- H. Banejad, E. Olyaie, Application of an artificial neural network
model to rivers water quality indexes prediction—a case study,
J. Am. Sci., 7 (2011) 60–65.
- Y.R. Ding, Y.J. Cai, P.D. Sun, B. Chen, The use of combined
neural networks and genetic algorithms for prediction of river
water quality, J. Appl. Res. Technol., 12 (2014) 493–499.
- N.S. Jaddi, S. Abdullah, A cooperative-competitive masterslave
global-best harmony search for ANN optimization and
water-quality prediction, Appl. Soft Comput., 51 (2017) 209–224.
- A. Beucher, R. Siemssen, S. Fröjdö, P. Österholm, A. Martinkauppi,
P. Edén, Artificial neural network for mapping and
characterization of acid sulfate soils: application to Sirppujoki
River catchment, southwestern Finland, Geoderma, 247 (2015)
38–50.
- N. Noori, L. Kalin, S. Isik, Water quality prediction using
SWAT-ANN coupled approach, J. Hydrol., 590 (2020) 1–10,
doi: 10.1016/j.jhydrol.2020.125220.
- S.S. Panda, V. Garg, I. Chaubey, Artificial neural networks
application in lake water quality estimation using satellite
imagery, J. Environ. Inf., 4 (2004) 65–74.
- H.Z. Abyaneh, Evaluation of multivariate linear regression
and artificial neural networks in prediction of water quality
parameters, J. Environ. Health Sci. Eng., 12 (2014) 1–8,
doi: 10.1186/2052-336X-12-40.
- J.-P. Suen, J.W. Eheart, Evaluation of neural networks for
modeling nitrate concentrations in rivers, J. Water Resour.
Plann. Manage., 129 (2003) 505–510.
- K. Ostad-Ali-Askari, M. Shayannejad, H. Ghorbanizadeh-
Kharazi, Artificial neural network for modeling nitrate
pollution of groundwater in marginal area of Zayandeh-rood
River, Isfahan, Iran, KSCE J. Civ. Eng., 21 (2017) 134–140.
- S. Azimi, M.A. Moghaddam, S.H. Monfared, Prediction of
annual drinking water quality reduction based on groundwater
resource index using the artificial neural network and fuzzy
clustering, J. Contam. Hydrol., 220 (2019) 6–17.
- F. Qaderi, E. Babanezhad, Prediction of the groundwater
remediation costs for drinking use based on quality of water
resource, using artificial neural network, J. Cleaner Prod.,
161 (2017) 840–849.
- M.J. Diamantopoulou, D.M. Papamichail, V.Z. Antonopoulos,
The use of a neural network technique for the prediction of
water quality parameters, Oper. Res., 5 (2005) 115–125.
- J. Rodier, B. Legube, N. Merlet, R. Brunet, L’analyse de L’eau,
9e éd., Eaux Naturelles, Eaux Résiduaires, Eau de Mer,
Dunod, 2009. Available at: https://books.google.dz/books?
id=qUEGsUBZkL0C
- D. Jamin, Recherche du Boson de Higgs du Modèle Standard
Dans le Canal de Désintégration ZH > nu nu bb Sur le
Collisionneur Tevatron dans L’expérience D0. Développement
D’une Méthode D’étiquetage des Jets de Quark b Avec des
Muons de Basses Impulsions Transverses, 2010. Available
at: https://tel.archives-ouvertes.fr/tel-00557839.
- M. Naoual, A. Abdelaziz, E.H. Abdellah, Use of artificial
neural networks type MLP for the prediction of phosphorus
level from the physicochemical parameters of sediments, IOSR
J. Comput. Eng., 18 (2016) 61–70.
- A. Schmitt, B. Le Blanc, M.-M. Corsini, C. Lafond, J. Bruzek,
Les reseaux de neurones artificiels. Un outil de traitement de
données prometteur pour l’anthropologie, Bull. Mém. Soc.
D’Anthropol. Paris, 13 (2001) 1–2.
- S. Huo, Z. He, J. Su, B. Xi, C. Zhu, Using artificial neural network
models for eutrophication prediction, Procedia Environ. Sci.,
18 (2013) 310–316.
- N.D. Kaushika, R.K. Tomar, S.C. Kaushik, Artificial neural
network model based on interrelationship of direct, diffuse and
global solar radiations, Sol. Energy, 103 (2014) 327–342.
- K.D. Fausch, C.L. Hawkes, M.G. Parsons, Models That Predict
Standing Crop of Stream Fish from Habitat Variables: 1950-
85, Gen. Tech. Rep. PNW-GTR-213 Portland US Department
of Agriculture, Forest Service, Pacific, Northwest Research
Station, 1988, 52 p.
- H. El Badaoui, A. Abdallaoui, I. Manssouri, L. Lancelot,
Elaboration de modèles mathématiques stochastiques pour la
prédiction des teneurs en métaux lourds des eaux superficielles
en utilisant les réseaux de neurones artificiels et la régression
linéaire multiple, J. Hydrocarbon Mines Environ. Res., 3 (2012)
31–36.
- E.M. Brakni, Réseaux de Neurones Artificiels Appliqués à la
Méthode Electromagnétique Transitoire InfiniTEM, Université
du Québec en Abitibi-Témiscamingue, 2011. Available at:
https://depositum.uqat.ca/id/eprint/32
- R.P. Lippmann, An introduction to computing with neural nets,
IEEE ASSP Mag., 4 (1987) 4–22.
- N. Samani, M. Gohari-Moghadam, A.A. Safavi, A simple neural
network model for the determination of aquifer parameters,
J. Hydrol., 340 (2007) 1–11.
- M. Sediri, S. Hanini, H. Cherifi, M. Laidi, S.A. Turki, Dynamic
adsorption modelling of P-nitrophenol in aqueous solution
using artificial neural network, J. Mater. Environ. Sci., 8 (2017)
2282–2287.
- R. Yacef, A. Mellit, S. Belaid, Z. Şen, New combined models
for estimating daily global solar radiation from measured air
temperature in semi-arid climates: application in Ghardaïa,
Algeria, Energy Convers. Manage., 79 (2014) 606–615.
- H. El Badaoui, A. Abdallaoui, L. Lancelot, Application des
réseaux de neurones artificiels et des régressions linéaires
multiples pour la prédiction des concentrations des métaux
lourds dans les sédiments fluviaux marocains, Eur. J. Sci. Res.,
107 (2013) 400–413.
- D.A. Belsley, E. Kuh, R.E. Welsch, Regression Diagnostics:
Identifying Influential Data and Sources of Collinearity,
Wiley, 1980. Available at: https://books.google.dz/
books?id=ALjuAAAAMAAJ
- S.H. Hong, M.W. Lee, D.S. Lee, J.M. Park, Monitoring of
sequencing batch reactor for nitrogen and phosphorus removal
using neural networks, Biochem. Eng. J., 35 (2007) 365–370.
- I. Manssouri, M. Manssouri, B. El Kihel, Fault detection by
K-NN algorithm and MLP neural networks in a distillation
column: comparative study, J. Inf. Intell. Knowl., 3 (2011)
201–215.
- I. Manssouri, A. El Hmaidi, T.E. Manssouri, B. El Moumni,
Prediction levels of heavy metals (Zn, Cu and Mn) in current
Holocene deposits of the eastern part of the Mediterranean
Moroccan margin (Alboran Sea), IOSR J. Comput. Eng.,
16 (2014) 117–123.
- O.R. Dolling, E.A. Varas, Artificial neural networks for
streamflow prediction, J. Hydraul. Res., 40 (2002) 547–554.
- S. Lefnaoui, N. Moulai-Mostefa, Investigation and optimization
of formulation factors of a hydrogel network based
on kappa carrageenan–pregelatinized starch blend using an
experimental design, Colloids Surf., A, 458 (2014) 117–125.
- C. Voyant, M. Muselli, C. Paoli, M.-L. Nivet, Optimization
of an artificial neural network dedicated to the multivariate
forecasting of daily global radiation, Energy, 36 (2011) 348–359.
- M. Bélanger, N. El-Jabi, D. Caissie, F. Ashkar, J. Ribi, Estimation
de la température de l’eau de rivière en utilisant les réseaux
de neurones et la régression linéaire multiple, J. Water Sci.,
18 (2005) 403–421.