Variable selection methods for water demand forecasting in Ethiopia: Case study Gondar town

References

• Al-Musaylh, M. S., Deo, R. C., Adamowski, J. F., & Li, Y. (2018). Advanced engineering informatics short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia. Advanced Engineering Informatics, 35, 1–16. doi:10.1016/j.aei.2017.11.002
   Web of Science ®Google Scholar
• Almutaz, I., Ajbar, A., Khalid, Y., & Ali, E. (2012). A probabilistic forecast of water demand for a tourist and desalination dependent city : Case of Mecca, Saudi Arabia. DES, 294, 53–59. doi:10.1016/j.desal.2012.03.010
   Google Scholar
• Anele, A. O., Todini, E., Hamam, Y., & Abu-Mahfouz, A. M. (2018). Predictive uncertainty estimation in water demand. 12–15. doi:10.3390/w10040475
   Google Scholar
• Araujo, F., Peres, P., & Fogliatto, F. S. (2017). Variable selection methods in multivariate statistical process control: A systematic literature review. Computation Industrial Engineering. doi:10.1016/j.cie.2017.12.006
   Web of Science ®Google Scholar
• Bakker, M., Duist, H., Van Schagen, K., Van Vreeburg, J., & Rietveld, L. (2014). Improving the performance of water demand forecasting models by using weather input. Procedia Engineering, 70, 93–102. doi:10.1016/j.proeng.2014.02.012
   Google Scholar
• Britz, W., Ferris, M., & Kuhn, A. (2013). Environmental modelling & software modeling water allocating institutions based on multiple optimization problems with equilibrium constraints. Environmental Modelling and Software, 46, 196–207. doi:10.1016/j.envsoft.2013.03.010
   Web of Science ®Google Scholar
• Candelieri, A., Giordani, I., Archetti, F., Barkalov, K., Meyerov, I., Polovinkin, A., … Zolotykh, N. (2018). Tuning hyperparameters of a SVM-based water demand forecasting system through parallel global optimization. Computers and Operations Research, 1–8. doi:10.1016/j.cor.2018.01.013.
   Web of Science ®Google Scholar
• Duan, F., Fu, X., Jiang, J., Huang, T., Ma, L., & Zhang, C. (2018). Spectrochimica Acta Part B automatic variable selection method and a comparison for quantitative analysis in laser-induced breakdown spectroscopy. Spectrochim Acta Particle B Atomic Spectroscopic, 143, 12–17. doi:10.1016/j.sab.2018.02.010
   Web of Science ®Google Scholar
• Figueiredo, M., De, Cordella, C. B. Y., Bouveresse, D. J., Rutledge, D. N., & Archer, X. (2018). Chemometrics and intelligent laboratory systems a variable selection method for multiclass classi fi cation problems using two-class ROC analysis. Experimental Eye Research, 177, 35–46. doi:10.1016/j.exer.2018.07.029
   PubMed Web of Science ®Google Scholar
• Garedew, L., Hagos, Z., Zegeye, B., & Addis, Z. (2015). The detection and antimicrobial susceptibility profile of Shigella isolates from meat and swab samples at butchers ’ shops in Gondar town, Northwest. Journal Infection Public Health. doi:10.1016/j.jiph.2015.10.015
   PubMed Web of Science ®Google Scholar
• Ghalehkhondabi, I., Ardjmand, E., Ii, W. A. Y., & Weckman, G. R. (2017). Water demand forecasting : Review of soft computing methods. Environmental Monitoring and Assessment, 189. doi:10.1007/s10661-017-6030-3
   PubMed Web of Science ®Google Scholar
• Gilhodes, J., Zemmour, C., Martinez, A., Delord, J., Leconte, E., Boher, J., & Filleron, T. (2017). Comparison of variable selection methods for high-dimensional survival data with competing events. 91, 159–167. doi:10.1016/j.compbiomed.2017.10.021.
   Google Scholar
• Gustin, M., Mcleod, R. S., & Lomas, K. J. S. C. (2018). Build Environment. doi:10.1016/j.buildenv.2018.07.045
   Web of Science ®Google Scholar
• Haque, M., Rahman, A., Hagare, D., & Comparative, A. (2018). Assessment of variable selection methods in urban water demand forecasting. 1–15. doi:10.3390/w10040419
   Google Scholar
• Herrera, M. (2010). Predictive models for forecasting hourly urban water demand, 387, 141–150. doi:10.1016/j.jhydrol.2010.04.005.
   Google Scholar
• Liu, J., Savenije, H. H. G., & Xu, J. (2003). Forecast of water demand in Weinan City in China using WDF-ANN model. 28, 219–224. doi:10.1016/S1474-7065(03)00026-3.
   Google Scholar
• Look, A. C. (2010). Variable selection methods in regression : Ignorable problem, outing. 18, 65–75. doi:10.1057/jt.2009.26.
   Google Scholar
• Mekasha, A., Tesfaye, K., & Duncan, A. J. (2014). Trends in daily observed temperature and precipitation extremes over three Ethiopian eco-environments. International Journal Climatology, 34, 1990–1999. doi:10.1002/joc.2014.34.issue-6
   Web of Science ®Google Scholar
• Mohamed, M. M., & Al-Mualla, A. A. (2010). Water demand forecasting in Umm Al-Quwain using the constant rate model. DES, 259, 161–168. doi:10.1016/j.desal.2010.04.014
   Google Scholar
• Objectives, E. (2017). Optimal use of agricultural water and land resources through reconfiguring crop planting structure under socioeconomic and. doi:10.3390/w9070488
   Google Scholar
• Quilty, J., & Adamowski, J. (2018). Addressing the incorrect usage of wavelet-based hydrological and water resources forecasting models for real-world applications with best practices and a new forecasting framework. Journal of Hydrology, 563, 336–353. doi:10.1016/j.jhydrol.2018.05.003
   Web of Science ®Google Scholar
• Rahman, M., Imtiaz, S. A., & Hawboldt, K. (2016). Chemometrics and intelligent laboratory systems a hybrid input variable selection method for building soft sensor from correlated process variables. Chemom Intelligent Laboratory Systems, 157, 67–77. doi:10.1016/j.chemolab.2016.06.015
   Web of Science ®Google Scholar
• Russo, T., Alfredo, K., & Fisher, J. (2014). Sustainable water management in urban, agricultural, and natural systems. 3934–3956. doi:10.3390/w6123934
   Google Scholar
• Sebri, M. (2016). Forecasting urban water demand : A meta-regression analysis. Journal of Environmental Management, 183, 777–785. doi:10.1016/j.jenvman.2016.09.032
   PubMed Web of Science ®Google Scholar
• Sheng, C., Miaw, W., Assis, C., Rangel, A., Sales, C., Cunha, M. L., … Carvalho, S. V. 2018. Determination of main fruits in adulterated nectars by ATR-FTIR spectroscopy combined with multivariate calibration and variable selection methods. Food Chemistry. doi:10.1016/j.foodchem.2018.02.015
   Web of Science ®Google Scholar
• Tiwari, M. K., & Adamowski, J. (2013). Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models. 49, 6486–6507. doi:10.1002/wrcr.20517.
   Google Scholar
• Wang, X., Lei, X., Guo, X., You, J., & Wang, H. A. O. (2015). Forecast of irrigation water demand considering multiple factors. 331–336. doi:10.5194/piahs-368-331-2015
   Google Scholar
• Worqlul, A. W., Collick, A. S., Rossiter, D. G., Langan, S., & Steenhuis, T. S. (2015). Catena assessment of surface water irrigation potential in the Ethiopian highlands : The Lake Tana Basin. Catena, 129, 76–85. doi:10.1016/j.catena.2015.02.020
   Web of Science ®Google Scholar
• Xinping, X. (2009). The research on forecasting water demand methods for Changzhi City in Shanxi Province, (4), 5261–5264.
   Google Scholar
• Yesuf, M., Kassie, M., & Köhlin, G. (2009). Environment for development risk implications of farm technology adoption in the Ethiopian Highlands.
   Google Scholar
• Zubaidi, S. L., Dooley, J., Alkhaddar, R. M., Abdellatif, M., Al-Bugharbee, H., & Ortega-Martorell, S. (2018). A novel approach for predicting monthly water demand by combining singular spectrum analysis with neural networks. Journal of Hydrology, 561, 136–145. doi:10.1016/j.jhydrol.2018.03.047
   Web of Science ®Google Scholar