Efficient river water quality index prediction considering minimal number of inputs variables

References

• Adamowski, J., & Sun, K. (2010). Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. Journal of Hydrology, 390(1–2), 85–91. https://doi.org/10.1016/j.jhydrol.2010.06.033
   Web of Science ®Google Scholar
• Alizadeh, M. J., Kavianpour, M. R., Danesh, M., Adolf, J., Shamshirband, S., & Chau, K. W. (2018). Effect of river flow on the quality of estuarine and coastal waters using machine learning models. Engineering Applications of Computational Fluid Mechanics, 12(1), 810–823. https://doi.org/10.1080/19942060.2018.1528480
   Web of Science ®Google Scholar
• Anctil, F., Perrin, C., & Andréassian, V. (2004). Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models. Environmental Modelling and Software, 19(4), 357–368. https://doi.org/10.1016/S1364-8152(03)00135-X
   Web of Science ®Google Scholar
• Atasoy, M., Palmquist, R. B., & Phaneuf, D. J. (2006). Estimating the effects of urban residential development on water quality using microdata. Journal of Environmental Management, 79(4), 399–408. https://doi.org/10.1016/j.jenvman.2005.07.012
   PubMed Web of Science ®Google Scholar
• Avvannavar, S. M., & Shrihari, S. (2008). Evaluation of water quality index for drinking purposes for river Netravathi, Mangalore, South India. Environmental Monitoring and Assessment, 143(1–3), 279–290. https://doi.org/10.1007/s10661-007-9977-7
   PubMed Web of Science ®Google Scholar
• Bradley, R. M. (2010). Direct and indirect benefits of improving river quality: Quantifying benefits and a case study of the River Klang, Malaysia. The Environmentalist, 30(3), 228–241. https://doi.org/10.1007/s10669-010-9267-8
   Google Scholar
• Caddis, Ben, Nielsen, Chris, Hong, Wedge, Anun Tahir, Paridah, & Yenn Teo, Fang. (2012). Guidelines for floodplain development – a Malaysian case study. International Journal of River Basin Management, 10(2), 161–170. 10.1080/15715124.2012.688750
   Google Scholar
• Directors, A. D. B. B. of. (2002). Report and recommendation of the president to the Board of Directors on a proposed loan to Mongolia for the integrated development of basic urban services in provincial towns project (Vol. 31243). Asian Development Bank.
   Google Scholar
• Elzwayie, A., El-shafie, A., Yaseen, Z. M., Afan, H. A., & Allawi, M. F. (2017). RBFNN-based model for heavy metal prediction for different climatic and pollution conditions. Neural Computing and Applications, 28(8), 1991–2003. https://doi.org/10.1007/s00521-015-2174-7
   Web of Science ®Google Scholar
• Ghorbani, M. A., Khatibi, R., Karimi, V., Yaseen, Z. M., & Zounemat-Kermani, M. (2018). Learning from multiple models using artificial intelligence to improve model prediction accuracies: Application to river flows. Water Resources Management, 32(13), 4201–4215. https://doi.org/10.1007/s11269-018-2038-x
   Web of Science ®Google Scholar
• Hameed, M., Sharqi, S. S., Yaseen, Z. M., Afan, H. A., Hussain, A., & Elshafie, A. (2017). Application of artificial intelligence (AI) techniques in water quality index prediction: A case study in tropical region, Malaysia. Neural Computing and Applications, 28(1), 893–905. https://doi.org/10.1007/s00521-016-2404-7
   Web of Science ®Google Scholar
• Heddam, S., & Kisi, O. (2018). Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree. Journal of Hydrology, 559, 499–509. https://doi.org/10.1016/j.jhydrol.2018.02.061
   Web of Science ®Google Scholar
• Ho, J. Y., Afan, H. A., El-Shafie, A. H., Koting, S. B., Mohd, N. S., Jaafar, W. Z. B., Lai Sai, H., Malek, M. A., Ahmed, A. N., Mohtar, W. H. M. W., Elshorbagy, A., & El-Shafie, A. (2019). Towards a time and cost effective approach to water quality index class prediction. Journal of Hydrology, 575, 148–165. https://doi.org/10.1016/j.jhydrol.2019.05.016
   Web of Science ®Google Scholar
• Jiang, H., & Cotton, W. R. (2004). Soil moisture estimation using an artificial neural network: A feasibility study. Canadian Journal of Remote Sensing, 30(5), 827–839. https://doi.org/10.5589/m04-041
   Web of Science ®Google Scholar
• Karimi, S., Kisi, O., Shiri, J., & Makarynskyy, O. (2013). Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia. Computers and Geosciences, 52, 50–59. https://doi.org/10.1016/j.cageo.2012.09.015
   Web of Science ®Google Scholar
• Kido, M., Yustiawati, S., Sulastri, M. S., Hosokawa, T., Tanaka, S., Saito, T., Iwakuma, T., & Kurasaki, M. (2009). Comparison of general water quality of rivers in Indonesia and Japan. Environmental Monitoring and Assessment, 156(1-4), 317–329. https://doi.org/10.1007/s10661-008-0487-z
   PubMed Web of Science ®Google Scholar
• Letcher, R. A., Croke, B. F. W., & Jakeman, A. J. (2007). Integrated assessment modelling for water resource allocation and management: A generalised conceptual framework. Environmental Modelling & Software, 22(5), 733–742. https://doi.org/10.1016/j.envsoft.2005.12.014
   Web of Science ®Google Scholar
• Liu, W. C., & Chen, W. B. (2012). Prediction of water temperature in a subtropical subalpine lake using an artificial neural network and three-dimensional circulation models. Computers & Geosciences, 45, 13–25. doi: 10.1016/j.cageo.2012.03.010
   Web of Science ®Google Scholar
• Luo, P., He, B., Takara, K., Razafindrabe, B. H. N., Nover, D., & Yamashiki, Y. (2011). Spatiotemporal trend analysis of recent river water quality conditions in Japan. Journal of Environmental Monitoring, 13(10), 2819. https://doi.org/10.1039/c1em10339c
   PubMedGoogle Scholar
• Maier, H. R., Jain, A., Dandy, G. C., & Sudheer, K. P. (2010). Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environmental Modelling and Software, 25(8), 891–909. https://doi.org/10.1016/j.envsoft.2010.02.003
   Web of Science ®Google Scholar
• Makarynska, D., & Makarynskyy, O. (2008). Predicting sea-level variations at the Cocos (Keeling) Islands with artificial neural networks. Computers and Geosciences, 34(12), 1910–1917. https://doi.org/10.1016/j.cageo.2007.12.004
   Web of Science ®Google Scholar
• Malik, A., Kumar, A., Kisi, O., & Shiri, J. (2019). Evaluating the performance of four different heuristic approaches with Gamma test for daily suspended sediment concentration modeling. Environmental Science and Pollution Research, 26(22), 22670–22687. https://doi.org/10.1007/s11356-019-05553-9
   PubMed Web of Science ®Google Scholar
• May, R. J., Dandy, G. C., Maier, H. R., & Nixon, J. B. (2008). Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems. Environmental Modelling & Software, 23(10–11), 1289–1299. https://doi.org/10.1016/j.envsoft.2008.03.008
   Web of Science ®Google Scholar
• Mei, K., Zhu, Y., Liao, L., Dahlgren, R., Shang, X., & Zhang, M. (2011). Optimizing water quality monitoring networks using continuous longitudinal monitoring data: A case study of Wen-Rui Tang River, Wenzhou, China. Journal of Environmental Monitoring, 13(10), 2755. https://doi.org/10.1039/c1em10352k
   PubMedGoogle Scholar
• Olyaie, E., Banejad, H., Chau, K.-W., & Melesse, A. M. (2015). A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: A case study in United States. Environmental Monitoring and Assessment, 187(4), 189. https://doi.org/10.1007/s10661-015-4381-1
   PubMed Web of Science ®Google Scholar
• Othman, F., M E, A. E., & Mohamed, I. (2012). Trend analysis of a tropical urban river water quality in Malaysia. Journal of Environmental Monitoring, 14(12), 3164–3173. https://doi.org/10.1039/c2em30676j
   PubMedGoogle Scholar
• Rubio-Arias, H., Contreras-Caraveo, M., Quintana, R. M., Saucedo-Teran, R. A., & Pinales-Munguia, A. (2012). An overall water quality index (WQI) for a man-made aquatic reservoir in Mexico. International Journal of Environmental Research and Public Health, 9(5), 1687–1698. https://doi.org/10.3390/ijerph9051687
   PubMed Web of Science ®Google Scholar
• Sanikhani, H., Kisi, O., Maroufpoor, E., & Yaseen, Z. M. (2019). Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: Application of different modeling scenarios. Theoretical and Applied Climatology, 135(1-2), 449–462. https://doi.org/10.1007/s00704-018-2390-z
   Web of Science ®Google Scholar
• Sepahvand, A., Singh, B., Sihag, P., & Samani, A. N. (2019). Assessment of the various soft computing techniques to predict sodium absorption ratio (SAR). ISH Journal of Hydraulic Engineering, 5010. https://doi.org/10.1080/09715010.2019.1595185
   Google Scholar
• Sharma, A., Naidu, M., & Sargaonkar, A. (2013). Development of computer automated decision support system for surface water quality assessment. Computers & Geosciences, 51, 129–134. https://doi.org/10.1016/j.cageo.2012.09.007
   Web of Science ®Google Scholar
• Stambuk-Giljanovic, N. (1999). Water quality evaluation by index in Dalmatia. Water Research, 33(16), 3423–3440. https://doi.org/10.1016/s0043-1354(99)00063-9
   Web of Science ®Google Scholar
• Sudheer, K. P., Gosain, A. K., Mohana Rangan, D., & Saheb, S. M. (2002). Modelling evaporation using an artificial neural network algorithm. Hydrological Processes, 16(16), 3189–3202. https://doi.org/10.1002/hyp.1096
   Web of Science ®Google Scholar
• Tao, H., Bobaker, A. M., Ramal, M. M., Yaseen, Z. M., Hossain, M. S., & Shahid, S. (2019). Determination of biochemical oxygen demand and dissolved oxygen for semi-arid river environment: Application of soft computing models. Environmental Science and Pollution Research, (Zolnikov 2013) 26(1), 923–937. https://doi.org/10.1007/s11356-018-3663-x
   PubMed Web of Science ®Google Scholar
• Tung, T. M., & Yaseen, Z. M. (2020). A survey on river water quality modelling using artificial intelligence models: 2000-2020. Journal of Hydrology, 124670. https://doi.org/10.1016/j.jhydrol.2020.124670.
   Google Scholar
• Wan Mohtar, W. H. M., Abdul Maulud, K. N., Muhammad, N. S., Sharil, S., & Yaseen, Z. M. (2019). Spatial and temporal risk quotient based river assessment for water resources management. Environmental Pollution, 248, 133–144. https://doi.org/10.1016/j.envpol.2019.02.011
   PubMed Web of Science ®Google Scholar
• Yaseen, Z. M., Awadh, S. M., Sharafati, A., & Shahid, S. (2018). Complementary data-intelligence model for river flow simulation. Journal of Hydrology, 567, 180–190. https://doi.org/10.1016/j.jhydrol.2018.10.020
   Web of Science ®Google Scholar
• Yaseen, Z. M., Sulaiman, S. O., Deo, R. C., & Chau, K.-W. (2019). An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction. Journal of Hydrology, 569, 387–408. https://doi.org/10.1016/j.jhydrol.2018.11.069
   Web of Science ®Google Scholar