A novel approach for predicting the standardised precipitation index considering climatic factors
DOI:
https://doi.org/10.31185/ejuow.Vol10.Iss3.382Keywords:
Drought, SPI, ANN, SSA, PSO, IraqAbstract
Drought modelling is essential to managing water resources in arid regions to limit its impacts. Additionally, climate change has a significant effect on the frequency and intensity of drought. This research provides a novel approach to forecasting the standardised precipitation index (SPI 3), considering several climatic variables by employing hybrid methods including (i.e., data pre-processing represented by normalisation, cleaning (i.e., outliers and Singular Spectrum Analysis), and best model input (i.e., tolerance technique), in addition to, artificial neural network (ANN) combined with particle swarm optimisation (PSO)). The data on climatic factors were applied to build and evaluate the SPI 3 model from 1990 to 2020 for the Al-Kut region. The result revealed that data pre-processing techniques enhance the data quality by increasing the correlation coefficient between independent and dependent variables; and choosing the optimal input model scenario. Also, it was found that the PSO algorithm precisely predicts the parameters of the proposed model. Moreover, the finding confirmed that the supposed methodology precisely simulated the SPI 3 depending on several statistical criteria (i.e., R², RMSE, MAE).
References
Xu, D., et al., Application of a hybrid ARIMA-LSTM model based on the SPEI for drought forecasting. Environ Sci Pollut Res Int, 2022. 29(3): p. 4128-4144. DOI: https://doi.org/10.1007/s11356-021-15325-z
Pham, Q.B., et al., Coupling Singular Spectrum Analysis with Least Square Support Vector Machine to Improve Accuracy of SPI Drought Forecasting. Water Resources Management, 2021. 35(3): p. 847-868. DOI: https://doi.org/10.1007/s11269-020-02746-7
Banadkooki, F.B., V.P. Singh, and M. Ehteram, Multi-timescale drought prediction using new hybrid artificial neural network models. Natural Hazards, 2021. 106(3): p. 2461-2478. DOI: https://doi.org/10.1007/s11069-021-04550-x
McKee, T.B., N.J. Doesken, and J. Kleist. The relationship of drought frequency and duration to time scales. in 8th Conference on Applied Climatology. 1993.
Anshuka, A., F.F. van Ogtrop, and R. Willem Vervoort, Drought forecasting through statistical models using standardised precipitation index: a systematic review and meta-regression analysis. Natural Hazards, 2019. 97(2): p. 955-977. DOI: https://doi.org/10.1007/s11069-019-03665-6
Gumus, V. and H.M. Algin, Meteorological and hydrological drought analysis of the Seyhan−Ceyhan River Basins, Turkey. Meteorological Applications, 2017. 24(1): p. 62-73. DOI: https://doi.org/10.1002/met.1605
Adede, C., et al., A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya’s Operational Drought Monitoring. Remote Sensing, 2019. 11(9): p. 1099. DOI: https://doi.org/10.3390/rs11091099
Elbeltagi, A., et al., Estimating the Standardized Precipitation Evapotranspiration Index Using Data-Driven Techniques: A Regional Study of Bangladesh. Water, 2022. 14(11): p. 1764. DOI: https://doi.org/10.3390/w14111764
Xu, L., et al., An evaluation of statistical, NMME and hybrid models for drought prediction in China. Journal of Hydrology, 2018. 566: p. 235-249. DOI: https://doi.org/10.1016/j.jhydrol.2018.09.020
Soh, Y.W., et al., Application of artificial intelligence models for the prediction of standardized precipitation evapotranspiration index (SPEI) at Langat River Basin, Malaysia. Computers and Electronics in Agriculture, 2018. 144: p. 164-173. DOI: https://doi.org/10.1016/j.compag.2017.12.002
Agana, N.A. and A. Homaifar, EMD-Based Predictive Deep Belief Network for Time Series Prediction: An Application to Drought Forecasting. Hydrology, 2018. 5(1): p. 18. DOI: https://doi.org/10.3390/hydrology5010018
Nabipour, N., et al., Short-Term Hydrological Drought Forecasting Based on Different Nature-Inspired Optimization Algorithms Hybridized With Artificial Neural Networks. IEEE Access, 2020. 8: p. 15210-15222. DOI: https://doi.org/10.1109/ACCESS.2020.2964584
Zhang, Y., et al., Multi-models for SPI drought forecasting in the north of Haihe River Basin, China. Stochastic Environmental Research and Risk Assessment, 2017. 31(10): p. 2471-2481. DOI: https://doi.org/10.1007/s00477-017-1437-5
Belayneh, A. and J. Adamowski, Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector Regression. Applied Computational Intelligence and Soft Computing, 2012. 2012: p. 1-13. DOI: https://doi.org/10.1155/2012/794061
Dikshit, A., B. Pradhan, and A.M. Alamri, Short-Term Spatio-Temporal Drought Forecasting Using Random Forests Model at New South Wales, Australia. Applied Sciences, 2020. 10(12): p. 4254. DOI: https://doi.org/10.3390/app10124254
Altunkaynak, A. and A. Jalilzadnezamabad, Extended lead time accurate forecasting of palmer drought severity index using hybrid wavelet-fuzzy and machine learning techniques. Journal of Hydrology, 2021. 601: p. 126619. DOI: https://doi.org/10.1016/j.jhydrol.2021.126619
Ali, Z., et al., Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model. Advances in Meteorology, 2017. 5681308: p. 1-9. DOI: https://doi.org/10.1155/2017/5681308
Das, P., et al., Hybrid wavelet packet machine learning approaches for drought modeling. Environmental Earth Sciences, 2020. 79(10): p. 221. DOI: https://doi.org/10.1007/s12665-020-08971-y
Bari Abarghouei, H., M.R. Kousari, and M.A. Asadi Zarch, Prediction of drought in dry lands through feedforward artificial neural network abilities. Arabian Journal of Geosciences, 2011. 6(5): p. 1417-1433. DOI: https://doi.org/10.1007/s12517-011-0445-x
Apaydin, H., et al., Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions. Journal of Hydrology, 2021. 600: p. 126506. DOI: https://doi.org/10.1016/j.jhydrol.2021.126506
Tiu, E.S.K., et al., An evaluation of various data pre-processing techniques with machine learning models for water level prediction. Natural Hazards, 2022. 110: p. 121-153. DOI: https://doi.org/10.1007/s11069-021-04939-8
Ömer Faruk, D., A hybrid neural network and ARIMA model for water quality time series prediction. Engineering Applications of Artificial Intelligence, 2010. 23(4): p. 586-594. DOI: https://doi.org/10.1016/j.engappai.2009.09.015
Zubaidi, S.L., et al., A Method for Predicting Long-Term Municipal Water Demands Under Climate Change. Water Resources Management, 2020. 34(3): p. 1265-1279. DOI: https://doi.org/10.1007/s11269-020-02500-z
Khan, M.M.H., N.S. Muhammad, and A. El-Shafie, Wavelet based hybrid ANN-ARIMA models for meteorological drought forecasting. Journal of Hydrology, 2020. 590: p. 125380. DOI: https://doi.org/10.1016/j.jhydrol.2020.125380
Alawsi, M.A., et al., Drought Forecasting: A Review and Assessment of the Hybrid Techniques and Data Pre-Processing. Hydrology, 2022. 9(7): p. 115. DOI: https://doi.org/10.3390/hydrology9070115
Edan, M.H., R.M. Maarouf, and J. Hasson, Predicting the impacts of land use/land cover change on land surface temperature using remote sensing approach in Al Kut, Iraq. Physics and Chemistry of the Earth, Parts A/B/C, 2021. 123: p. 103012. DOI: https://doi.org/10.1016/j.pce.2021.103012
NOAA. National Oceanic and Atmospheric Administration. Data tools: Find a station. 2021 [cited 2021 1 December 2021]; Available from: https://www.ncdc.noaa.gov/cdo-web/datatools/findstation.
Aghelpour, P., H. Bahrami-Pichaghchi, and O. Kisi, Comparison of three different bio-inspired algorithms to improve ability of neuro fuzzy approach in prediction of agricultural drought, based on three different indexes. Computers and Electronics in Agriculture, 2020. 170: p. 105279. DOI: https://doi.org/10.1016/j.compag.2020.105279
Belayneh, A., J. Adamowski, and B. Khalil, Short-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet transforms and machine learning methods. Sustainable Water Resources Management, 2015. 2(1): p. 87-101. DOI: https://doi.org/10.1007/s40899-015-0040-5
Tigkas, D., H. Vangelis, and G. Tsakiris, DrinC: a software for drought analysis based on drought indices. Earth Science Informatics, 2014. 8(3): p. 697-709. DOI: https://doi.org/10.1007/s12145-014-0178-y
Alawsi, M.A., et al., Tuning ANN Hyperparameters by CPSOCGSA, MPA, and SMA for Short-Term SPI Drought Forecasting. Atmosphere, 2022. 13(9): p. 1436. DOI: https://doi.org/10.3390/atmos13091436
Tabachnick, B.G. and L.S. Fidell, Using Multivariate Statistics, ed. S. Edition. 2013: Pearson: Boston, MA, USA.
Zubaidi, S.L., et al., Urban Water Demand Prediction for a City That Suffers from Climate Change and Population Growth: Gauteng Province Case Study. Water, 2020. 12(7): p. 1885. DOI: https://doi.org/10.3390/w12071885
Golyandina, N. and A. Zhigljavsky, Singular Spectrum Analysis for Time Series. 2020, Springer. DOI: https://doi.org/10.1007/978-3-662-62436-4
Zubaidi, S.L., et al., A Novel approach for predicting monthly water demand by combining singular spectrum analysis with neural networks. Journal of Hydrology, 2018. 561: p. 136-145. DOI: https://doi.org/10.1016/j.jhydrol.2018.03.047
Ouyang, Q. and W. Lu, Monthly Rainfall Forecasting Using Echo State Networks Coupled with Data Preprocessing Methods. Water Resources Management, 2017. 32(2): p. 659-674. DOI: https://doi.org/10.1007/s11269-017-1832-1
Al-Bugharbee, H. and I. Trendafilova, A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modelling. Journal of Sound and Vibration, 2016. 369: p. 246-265. DOI: https://doi.org/10.1016/j.jsv.2015.12.052
Khan, M.A.R. and D.S. Poskitt, Forecasting stochastic processes using singular spectrum analysis: Aspects of the theory and application. International Journal of Forecasting, 2017. 33(1): p. 199-213. DOI: https://doi.org/10.1016/j.ijforecast.2016.01.003
Maier, H.R. and G.C. Dandy, Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling & Software, 2000. 15: p. 101–124. DOI: https://doi.org/10.1016/S1364-8152(99)00007-9
Pallant, J., SPSS Survival Manual: A Step by Step Guide to Data Analysis Using IBM SPSS. 6th Edition ed. 2016: McGraw-Hill Education.
Alharkan, I., et al., Tabu search and particle swarm optimization algorithms for two identical parallel machines scheduling problem with a single server. Journal of King Saud University - Engineering Sciences, 2020. 32(5): p. 330-338. DOI: https://doi.org/10.1016/j.jksues.2019.03.006
Dash, M., T. Panigrahi, and R. Sharma, Distributed parameter estimation of IIR system using diffusion particle swarm optimization algorithm. Journal of King Saud University - Engineering Sciences, 2019. 31(4): p. 345-354. DOI: https://doi.org/10.1016/j.jksues.2017.11.002
Jawad, H.M., et al., Accurate Empirical Path-Loss Model Based on Particle Swarm Optimization for Wireless Sensor Networks in Smart Agriculture. IEEE Sensors Journal, 2020. 20(1): p. 552-561. DOI: https://doi.org/10.1109/JSEN.2019.2940186
Banerjee, S. and V.V. Dwivedi, Effect of signal to interference ratio on adaptive beamforming techniques. Journal of King Saud University - Engineering Sciences, 2018. 30(4): p. 313-319. DOI: https://doi.org/10.1016/j.jksues.2016.08.002
Poli, R., Analysis of the Publications on the Applications of Particle Swarm Optimisation. Journal of Artificial Evolution and Applications, 2008. 2008: p. 1-10. DOI: https://doi.org/10.1155/2008/685175
Mohammadi, B. and S. Mehdizadeh, Modeling daily reference evapotranspiration via a novel approach based on support vector regression coupled with whale optimization algorithm. Agricultural Water Management, 2020. 237: p. 106145. DOI: https://doi.org/10.1016/j.agwat.2020.106145
Mohammadi, B., et al., Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series. Hydrological Sciences Journal, 2020. 65(10): p. 1738-1751. DOI: https://doi.org/10.1080/02626667.2020.1758703
Dawson, C.W., R.J. Abrahart, and L.M. See, HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environmental Modelling & Software, 2007. 22(7): p. 1034-1052. DOI: https://doi.org/10.1016/j.envsoft.2006.06.008
Valentini, M., G.B. dos Santos, and B. Muller Vieira, Multiple linear regression analysis (MLR) applied for modeling a new WQI equation for monitoring the water quality of Mirim Lagoon, in the state of Rio Grande do Sul—Brazil. SN Applied Sciences, 2021. 3(1): p. 70. DOI: https://doi.org/10.1007/s42452-020-04005-1
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