Application of artificial neural networks to the modeling of rain-runoff in the Chancay Lambayeque river basin
DOI:
https://doi.org/10.24850/j-tyca-2024-06-03Palabras clave:
Basin, precipitation, flow, artificial neural networks, hydrometeorological stationsResumen
Between the months of December to April, regions of northern Peru, including Lambayeque, are affected by maximum extreme events, wreaking havoc on homes, flooding crop fields, collapsing hydraulic works, and the most irreparable loss of human lives. In this line, the objective of this research was to apply Artificial Neural Networks to rain-runoff modeling in a basin in northern Peru, namely, the Chancay Lambayeque river basin belonging to the Pacific slope. For this purpose, records of precipitation and flows of 30 years (hydrological normal) were collected from 12 hydrometeorological stations belonging to the basin and neighboring it. Thus, applying a model of Long and Short Term Memory Networks (LSTM) we proceeded to model the rain, seeking to follow the behavior of the flows observed in the Racarrumi hydrometric station, with 80 % of the information the model was trained and with 20 % it was validated. In short, it was obtained that in the modeling validation stage, the Nash coefficient was 0.93, corresponding to the qualifier "very good".
Citas
Abbot, J., & Marohasy, J. (2014). Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks. Atmospheric Research, 138, 166-178. DOI: 10.1016/j.atmosres.2013.11.002
Adamowski, J., Chan, H., Prasher, S., Ozga-Zielinski, B., & Sliusarieva, A. (2012). Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada. Water Resources Research, 48(1), 1528-1541. DOI: 10.1029/2010WR009945
Afzaal, H., Farooque, A., Abbas, F., Acharya, B., & Esau, T. (2020). Computation of evapotranspiration with artificial intelligence for precision water resource management. Applied Sciences, 10(5). DOI: 10.3390/app10051621
Asurza, F. A., Ramos, C. L., & Lavado W, S. (2018). Evaluación de los productos tropical rainfall measuring missin (TRMM) y global precipitation measurement (GPM) en el modelamiento hidrológico de la cuenca del río Huancané, Perú. Scientia Agropecuaria, 9(1), 53-62. DOI: 10.17268/sci.agropecu.2018.01.06
Basagaoglu, H., Chakraborty, D., & Winterle, J. (2021). Reliable evapotranspiration predictions with a probabilistic machine learning framework. Water, 13(4). DOI: 10.3390/w13040557
Béjar, W., Valeriano, K., Ilachoque, J., & Sulla, J. (2016). Predicción de caudales medios diarios en la cuenca del Amazonas aplicando redes neuronales artificiales y el modelo neurodifuso ANFIS. Research in Computing Science, 113(1), 23-35. DOI: 10.13053/rcs-113-1-2
Choubin, B., Khalighi-Sigaroodi, S., Malekian, A., & Kişi, Ö. (2014). Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for the prediction of precipitation based on large-scale climate signals. Hydrological Sciences Journal, 61(6), 1001-1009. DOI: 10.1080/02626667.2014.966721
Cromwell, E., Shuai, P., Jiang, P., Coon, E. T., Painter, S. L., Moulton, J. D., Lin, N., & Chen, X. (2021). Estimating watershed subsurface permeability from stream discharge data using deep neural networks. Frontiers in Earth Science, 9. DOI: 10.3389/feart.2021.613011
Fan, H., Jiang, M., Xu, L., Zhu, H., Cheng, J., & Jiang, J. (2020). Comparison of long short term memory networks and the hydrological model in runoff simulation. Water, 175-190. DOI: 10.3390/w12010175
Farfán, J., Palacios, K., Ulloa, J., & Avilés, A. (2020). A hybrid neural network-based technique to improve the flow forecasting of physical and data-driven models: Methodology and case studies in Andean watersheds. Journal of Hydrology: Regional Studies, 27. DOI: 10.1016/j.ejrh.2019.100652
French, M., Krajewski, W., & Cuykendall, R. (1992). Rainfall forecasting in space and time using a neural network. Journal of Hydrology, 137(1), 1-31. DOI: 10.1016/0022-1694(92)90046-X
Fu, M., Fan, T., Ding, Z., Salih, S. Q., Al-Ansari, N., & Yaseen, Z. M. (2020). Deep learning data-intelligence model based on adjusted forecasting window scale: Application in daily streamflow simulation. IEEE Access, 8, 32632-32651. DOI: 10.1109/ACCESS.2020.2974406
Han, H., Choi, C., Jung, J., & Kim, H. S. (2021). Deep learning with long short term memory based sequence-to-sequence model for rainfall-runoff simulation. Water, 13(437).
Hu, C., Wu, Q., Li, H., Jian, S., Li, N., & Lou, Z. (2018). Deep learning with a long short-term memory networks approach for rainfall-runoff simulation. Water, 10(11), 1543-1558. DOI: 10.3390/w10111543
Jimeno, P., Senent, J., Pérez, J., Pulido, D., & Cecilia, J. (2017). Estimation of instantaneous peak flow using machine-learning models and empirical formula in peninsular Spain. Water, 9(5), 347-359. DOI: 10.3390/w9050347
Kim, T., Yang, T., Gao, S., Zhang, L., Ding, Z., Wen, X., Gourley, J. J., & Hong, Y. (2021). Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS. Journal of Hydrology, 598. DOI: 10.1016/j.jhydrol.2021.126423
Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A., Hochreiter, S., & Nearing, G. (2019). Toward improved predictions in ungauged basins: Exploiting the power of machine learning. Water Resources Research, 55(12), 11344-11354. DOI: 10.1029/2019WR026065
Laqui-Vilca, W. F. (2010). Aplicación de redes neuronales artificiales a la modelización y previsión de caudales medios mensuales del río Huancané. Revista Peruana Geo-Atmosférica RPGA, 2, 30-44. Recovered from https://web2.senamhi.gob.pe/rpga/pdf/2010_vol02/art3.pdf
Lujano, E., Lujano, A., Pitágoras, J., & Lujano, R. (2014). Pronóstico de caudales medios mensuales del río Ilave usando modelos de redes neuronales artificiales. Revista de Investigaciones Altoandina, 16(1), 89-100.
Mosavi, A., Ozturk, P., & Chau, K.-W. (2018). Flood prediction using machine learning models: Literature review. Water, 10(11), 1536-1576. DOI: 10.3390/w10111536
Nabipour, N., Dehghani, M., Shamshirband, S., & Mosavi, A. (2020). Short-term hydrological drought forecasting based on different nature-inspired optimization algorithms hybridized with artificial neural networks. IEEE Access, 8, 15210-15222. DOI: 10.1109/ACCESS.2020.2964584
Qin, J., Liang, J., Chen, T., Lei, X., & Kang, A. (2019). Simulating and predicting of hydrological time series based on tensorflow deep learning. Polish Journal of Environmental Studies, 28(2), 795-802. DOI: 10.15244/pjoes/81557
Rezaeianzadeh, M., Tabari, H., & Yazdi, A. (2014). Flood flow forecasting using ANN, ANFIS and regression models. Neural Computing & Applications, 25, 25-37. DOI: 10.1007/s00521-013-1443-6
Rodríguez, C., Díaz, H., Ballesteros, J., Rohrer, M., & Stoffel, M. (2019). The anomalous 2017 coastal El Niño event in Peru. Climate Dynamics, 52, 5605-5622. DOI: 10.1007/s00382-018-4466-y
Sattari, M., Apaydin, H., Band, S., Mosavi, A., & Prasad, R. (2021). Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation. Hydrology and Earth System Sciences, 25(1), 603-618. DOI: 10.5194/hess-25-603-2021
Shi, L., Feng, P., Wang, B., Liu, D., Cleverly, J., & Fang, Q. (2020). Projecting potential evapotranspiration change and quantifying its uncertainty under future climate scenarios: A case study in southeastern Australia. Journal of Hydrology, 584. DOI: 10.1016/j.jhydrol.2020.124756
Tineo-Pongo, P. (2018). Aplicación del modelo hidrológico distribuido TETIS para estimar la variabilidad hidrológica en la cuenca del río Chancay Lambayeque. Recovered from https://alicia.concytec.gob.pe/vufind/Record/UCVV_2c32a071628a390e3387d909a4a4f14f
Valderrama-Purizaca, F. J., Chávez-Barturen, D. A., Muñoz-Pérez, S. P., Tuesta-Monteza, V., & Mejía-Cabrera, H. I. (2021). Importancia de las redes neuronales artificiales en la ingeniería civil: Una revisión sistemática de la literatura. Iteckne, 18(1), 71-83. DOI: 10.15332/iteckne
Wang, J., Li, H. P., Lu, H. Y., Zhang, R. Q., Cao, X. S., Tong, C. F., & Zheng, H. X. (2019). Estimation of evapotranspiration for irrigated artificial grasslands in typical steppe areas using the METRIC model. Applied Ecology and Environmental Research, 17(6), 13759-13776. DOI: 10.15666/aeer/1706_1375913776
Yaseen, Z., Naghshara, S., Salih, S., Kim, S., Malik, A., & Ghorbani, M. (2020). Lake water level modeling using newly developed hybrid data intelligence model. Theoretical and Applied Climatology, 141, 1285-1300. DOI: 10.1007/s00704-020-03263-8
Young, C.-C., Liu, W.-C., & Chung, C.-E. (2015). Genetic algorithm and fuzzy neural networks combined with the hydrological modeling system for forecasting watershed runoff discharge. Neural Computing and Application, 26, 1631-1643. DOI: 10.1007/s00521-015-1832-0
Zhang, Y., Zhao, Z., & Zheng, J. (2020). CatBoost: A new approach for estimating daily reference crop evapotranspiration in arid and semi-arid regions of Northern China. Journal of Hydrology, 588. DOI: 10.1016/j.jhydrol.2020.125087
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