Group method of data handling to forecast the daily water flow at the Cahora Bassa Dam

Acta Geophysica - Tập 70 - Trang 1871-1883 - 2022
Danilo P. M. Souza1,2, Alfeu D. Martinho1,2, Caio C. Rocha3,2, Eliane da S. Christo4, Leonardo Goliatt1,2
1Computational Modeling Program, UFJF, Juiz de Fora, Brazil
2Federal University of Juiz de Fora, Juiz de Fora, Brazil
3Computational Engineering Program, UFJF, Juiz de Fora, Brazil
4Department of Production Engineering, Fluminense Federal University, Volta Redonda, Rio de Janeiro, Brazil

Tóm tắt

The Zambezi watershed is essential for water supply, irrigation, fishing activities, and river transport of the populations of Southern Africa. The importance and variability of these water resources make it necessary to develop studies that may help understand and manage them. Despite this need, water resources studies for this region are still scarce. Therefore, the present work aims to present a strategy for forecasting the daily water flow of the Zambezi River in the Cahora Bassa dam, located in Mozambique, an important energy producer in the country and the fourth largest dam in Africa. Historical rainfall, evaporation, and humidity records collected from 2003 to 2011 are used for training and testing a model that forecasts water flow using the Group Method of Data Handling algorithm. The results achieved were compared, through error metrics, with those of other models to prove the effectiveness of the assembled model. They revealed that the proposed model achieves a satisfactory performance for the forecast horizon and could become a helpful tool in monitoring hydrographic basins and forecasting their daily streamflow values.

Tài liệu tham khảo

Abdullah S, Ismail M, Fong SY (2017) Multiple linear regression (MLR) models for long term pm10 concentration forecasting during different monsoon seasons. J Sustain Sci Manag 12(1):60–69 Almeida L, Serra JCV (2017) Modelos hidrológicos, tipos e aplicações mais utilizadas. Revista da FAE 20(1):129–137 Box GE, Jenkins GM, Reinsel GC et al (2015) Time series analysis: forecasting and control. John Wiley & Sons, New Jersey Chong XY, Vericat D, Batalla RJ et al (2021) A review of the impacts of dams on the hydromorphology of tropical rivers. Sci Total Environ 794:148686 Ebtehaj I, Sammen SS, Sidek LM et al (2021) Prediction of daily water level using new hybridized GS-GMDH and ANFIS-FCM models. Eng Appl Comput Fluid Mech 15(1):1343–1361 Eivani Z, Ahmadi MM, Qaderi K (2016) Estimation of suspended sediment load concentration in river system using Group Method of Data Handling (GMDH). J Watershed Manag Res 7(13):218–229 Ekandjo M, Makurira H, Mwelwa E et al (2018) Impacts of hydropower dam operations in the Mana Pools national park floodplains. Phys Chem Earth Parts A B C 106:11–16 Elkurdy M, Binns AD, Bonakdari H et al (2021) Early detection of riverine flooding events using the group method of data handling for the Bow river, Alberta, Canada. Int J River Basin Manag 29:1–12 Farlow SJ (1981) The GMDH algorithm of Ivakhnenko. Am Stat 35(4):210–215 Garzanti E, Bayon G, Dinis P et al (2022) The segmented Zambezi sedimentary system from source to sink: 2. Geochemistry, clay minerals, and detrital geochronology. J Geol 130:171–208 Gilvear DJ, Spray CJ, Casas-Mulet R (2013) River rehabilitation for the delivery of multiple ecosystem services at the river network scale. J Environ Manag 126:30–43 Goliatt L, Sulaiman SO, Khedher KM et al (2021) Estimation of natural streams longitudinal dispersion coefficient using hybrid evolutionary machine learning model. Eng Appl Comput Fluid Mech 15(1):1298–1320 Hughes D, Mantel S, Farinosi F (2020) Assessing development and climate variability impacts on water resources in the Zambezi river basin: initial model calibration, uncertainty issues and performance. J Hydrol Reg Stud 32(100):765 Hulsman P, Savenije HH, Hrachowitz M (2021) Satellite-based drought analysis in the Zambezi river basin: was the 2019 drought the most extreme in several decades as locally perceived? J Hydrol Reg Stud 34(100):789 Hussain D, Khan AA (2020) Machine learning techniques for monthly river flow forecasting of Hunza river, Pakistan. Earth Sci Inform 13(3):939–949 Ikeda S, Ochiai M, Sawaragi Y (1976) Sequential GMDH algorithm and its application to river flow prediction. IEEE Trans Syst Man Cybern 7:473–479 Isaacman A (2021) Cahora Bassa dam & the delusion of development. Dædalus 150(4):103–123 Ivakhnenko AG (1971) Polynomial theory of complex systems. IEEE Trans Syst Man Cybern 4:364–378 Jensen KM, Lange RB (2013) The Zambezi. https://www.jstor.org/stable/resrep13303.10?seq=2. Accessed 12 Apr 2021 Kling H, Stanzel P, Preishuber M (2014) Impact modelling of water resources development and climate scenarios on Zambezi river discharge. J Hydrol Reg Stud 1:17–43 Kondo T (1998) The learning algorithms of the GMDH neural network and their application to the medical image recognition. In: Proceedings of the 37th SICE Annual Conference. International Session Papers, IEEE, pp 1109–1114 Kunz MJ (2011) Effect of large dams in the Zambezi river basin: changes in sediment, carbon and nutrient fluxes. PhD thesis, ETH Zurich Li RYM, Fong S, Chong KWS (2017) Forecasting the reits and stock indices: group method of data handling neural network approach. Pac Rim Prop Res J 23(2):123–160 Liu Z, Zhou P, Chen X et al (2015) A multivariate conditional model for streamflow prediction and spatial precipitation refinement. J Geophys Res Atmos 120(19):10–116 Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. In: Proceedings of the 31st international conference on neural information processing systems, pp. 4768–4777 Manjoro A, Ferreira PA (2016) Desafios de Moçambique após os ciclones IDAI e Kenneth. Estratégia 465 Manyari WV (2007) Impactos ambientais a jusante de hidrelétricas: o caso da usina de Tucuruí-PA. Master’s thesis, Universidade Federal do Rio de Janeiro, Rio de Janeiro Martinho AD, Ribeiro CB, Gorodetskaya Y et al (2020) Extreme learning machine with evolutionary parameter tuning applied to forecast the daily natural flow at Cahora Bassa dam, Mozambique. In: International Conference on Bioinspired Methods and Their Applications. Springer, pp 255–267 Moosavi V, Talebi A, Hadian MR (2017) Development of a hybrid wavelet packet-group method of data handling (WPGMDH) model for runoff forecasting. Water Resour Manag 31(1):43–59 Muzzammil M, Alam J, Zakwan M (2015) An optimization technique for estimation of rating curve parameters. In: National Symposium on Hydrology Nishikawa T, Shimizu S (1982) Identification and forecasting in management systems using the GMDH method. Appl Math Model 6(1):7–15 Onwubolu GC (2016) GMDH-methodology and implementation in MATLAB. World Scientific Parsaie A, Azamathulla HM, Haghiabi AH (2020) Physical and numerical modeling of performance of detention dams. J Hydrol 581(121):757 Rajaee T, Jafari H (2020) Two decades on the artificial intelligence models advancement for modeling river sediment concentration: state-of-the-art. J Hydrol 588(125):011 Ribeiro LS, Wilhelm VE, Faria ÉF et al (2019) A comparative analysis of long-term concrete deformation models of a buttress dam. Eng Struct 193:301–307 Ronco P, Fasolato G, Nones M et al (2010) Morphological effects of damming on lower Zambezi river. Geomorphology 115(1–2):43–55 Ronco P, Fasolato D, Di-Silvio G (2006) The case of the Zambezi river in Mozambique: Some investigations on solid transport phenomena downstream Cahora Bassa dam. Proceedings of the International Conference on Fluvial Hydraulogy: Lisbon, Portugal (Taylor & Francis) Shaofu M, Al-Juboori AM, Alwan AH, et al (2021) On the investigation of monthly river flow generation complexity using the applicability of machine learning models. Complexity 2021 Teutschbein C, Grabs T, Laudon H et al (2018) Simulating streamflow in ungauged basins under a changing climate: the importance of landscape characteristics. J Hydrol 561:160–178 Tikhamarine Y, Souag-Gamane D, Ahmed AN et al (2020) Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey wolf optimization (GWO) algorithm. J Hydrol 582(124):435 Vörösmarty CJ, Meybeck M, Fekete B et al (2003) Anthropogenic sediment retention: major global impact from registered river impoundments. Global and planetary change 39(1–2):169–190 Wang WC, Chau KW, Cheng CT et al (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J hydrol 374(3–4):294–306 Wilk P (2022) Expanding the sediment transport tracking possibilities in a river basin through the development of a digital Platform-DNS/SWAT. Appl Sci 12(8):3848 Yonesi HA, Parsaie A, Arshia A et al (2022) Discharge modeling in compound channels with non-prismatic floodplains using GMDH and MARS models. Water Supply 22:4400–21 Zhang XY, Trame MN, Lesko LJ et al (2015) Sobol sensitivity analysis: a tool to guide the development and evaluation of systems pharmacology models. CPT Pharmacomet Syst Pharmacol 4(2):69–79