Streamflow forecast model using ANN

References

• Basha EA, Ravela S, Rus D. 2008. Model-based monitoring for early warning flood detection. Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems SenSys’08: ACM. p. 5–7.
   Google Scholar
• Boruah S. 1999. India against itself: Assam and the politics of nationality. Political Science Quarterly. 115(2):320–322. https://doi.org/10.2307/2657927.
   Web of Science ®Google Scholar
• Boryan CG, Yang Z, Sandborn A, Willis P, Haack B. 2018. Operational agricultural flood monitoring With sentinel-1 synthetic aperture radar. Proceedings of IGARSS. IEEE International Geoscience and Remote Sensing Symposium; Valencia, Spain: IEEE. p. 5831–5834.
   Google Scholar
• Chang LC, Amin MZM, Yang SN, Chang FJ. 2018. Building ANN-based regional multi-step-ahead flood inundation forecast models. Water. 10:1283. doi:10.3390/w10091283.
   Web of Science ®Google Scholar
• Chu H, Wu W, Wang QJ, Nathan R, Wei J. 2020. An ANN-based emulation modelling framework for flood inundation modelling: application, challenges and future directions. Environ Model Softw. 124:104587. doi:10.1016/j.envsoft.2019.104587.
   Web of Science ®Google Scholar
• Cruz FRG, Binag MG, Ga MRG, Uy FAA. 2018. Flood prediction using multi-layer artificial neural network in monitoring system with rain gauge, water level, soil moisture sensors. Proceedings of TENCON 2018; Jeju, Korea. p. 2499–2503.
   Google Scholar
• Cunge JA. 1969. On the subject of a flood propagation computation method (Musklngum method). J Hydraul Res. 7(2):205–230. doi:10.1080/00221686909500264.
   Google Scholar
• Dazzi S, Vacondio R, Mignosa P. 2021. Flood stage forecasting using machine-learning methods: a case study on the Parma River (Italy). Water. 13(12):1612. doi:10.3390/w13121612.
   Web of Science ®Google Scholar
• Directorate of economics and statistics: State Profile of Assam. 2020 Assam (India): Government of Assam; [accessed Jan 2020]. https://des.assam.gov.in/.
   Google Scholar
• Ditthakit P, Pinthong S, Salaeh N, Weekaew J, Tran TT, Pham QB. 2023. Comparative study of machine learning methods and GR2M model for monthly runoff prediction. Ain Shams Eng J. 14(4):101941. doi:10.1016/j.asej.2022.101941.
   Web of Science ®Google Scholar
• Feng D, Fang K, Shen C. 2020. Enhancing streamflow forecast and extracting insights using long-short term memory networks with data integration at continental scales. Water Resour Res. 56:e2019WR026793. doi:10.1029/2019WR026793.
   Web of Science ®Google Scholar
• Fletcher JE. 1949. Some properties of water solutions that influence infiltration. Eos Trans Am Geophys Union. 30:548–554. doi:10.1029/TR030i004p00548.
   Google Scholar
• Fletcher JE. 1969. A theoretical study of infiltration into forest and range soils. Progress Report, grant number 1-4040 (4000) Utah Water Research Laboratory, Utah State University, Logan, Utah.
   Google Scholar
• Fok Y-S, Hansen VE. 1966. One-dimensional infiltration into homogeneous soil. J Irrig Drain Div. 92:35–47. doi:10.1061/JRCEA4.0000432.
   Google Scholar
• Frame JM, Kratzert F, Klotz D, Gauch M, Shelev G, Gilon O, Qualls LM, Gupta HV, Nearing GS. 2022. Deep learning rainfall–runoff predictions of extreme events. Hydrol Earth Syst Sci. 26:3377–3392. doi:10.5194/hess-26-3377-2022.
   Web of Science ®Google Scholar
• Gauch M, Kratzert F, Klotz D, Nearing G, Lin J, Hochreiter S. 2021. Rainfall–runoff prediction at multiple timescales with a single long short-term memory network. Hydrol Earth Syst Sci. 25:2045–2062. doi:10.5194/hess-25-2045-2021.
   Web of Science ®Google Scholar
• Green WH, Ampt GA. 1911. Studies on soil physics. J Agric Sci. 4:1–24. doi:10.1017/S0021859600001441.
   Google Scholar
• Hansen VE. 1955. Infiltration and soil water movement during irrigation. Soil Science. 79:93–106. doi:10.1097/00010694-195502000-00002.
   Google Scholar
• Hashi AO, Abdirahman AA, Elmi A, Hashi ZMH, Octavio ERR. 2021. A real-time flood detection system based on machine learning algorithms with emphasis on deep learning. Int J Eng Trends Technol. 69(5):249–256. doi:10.14445/22315381/IJETT-V69I5P232.
   Google Scholar
• Horton RE. 1939. Analysis of runoff-plat experiments with varying infiltration capacity. Trans Am Geophys Union. 20(4):693–711. https://doi.org/10.1029/TR020i004p00693.
   Google Scholar
• Kabir S, Patidar S, Xia X, Liang Q, Neal J, Pender G. 2020. A deep convolutional neural network model for rapid prediction of fluvial flood inundation. J Hydrol. 590:125481. doi:10.1016/j.jhydrol.2020.125481.
   Web of Science ®Google Scholar
• Klotz D, Kratzert F, Gauch M, Keefe SA, Brandstetter J, Klambauer G, Hochreiter S, Nearing G. 2022. Uncertainty estimation with deep learning for rainfall–runoff modeling. Hydrol Earth Syst Sci. 26:1673–1693. doi:10.5194/hess-26-1673-2022.
   Web of Science ®Google Scholar
• Kostiakov AN. 1932. On the dynamics of the coefficient of water percolation in soils and on the necessity for studying it from a dynamic point of view for purposes of amelioration. Transactions of the Sixth Committee International Society of Soil Science, Russian, Part A. p. 17–21.
   Google Scholar
• Kuller M, Schoenholzer K, Lienert J. 2021. Creating effective flood warnings: a framework from a critical review. J Hydrol. 602:126708. doi:10.1016/j.jhydrol.2021.126708.
   Web of Science ®Google Scholar
• Kumar V, Kedam N, Sharma KV, Mehta DJ, Caloiero T. 2023. Advanced machine learning techniques to improve hydrological prediction: A comparative analysis of streamflow prediction models. Water. 15(14):2572. doi:10.3390/w15142572.
   Web of Science ®Google Scholar
• Lewis MR, Milne WE. 1938. Analysis of border irrigation. Agric Eng. 19:267–272.
   Google Scholar
• Moshe Z, Metzger A, Elidan G, Kratzert F, Nevo S, El-Yaniv R. 2020. Hydronets: Leveraging river structure for hydrologic modeling. arXiv [preprint], arXiv:2007.00595.
   Google Scholar
• Mousselli A. 2022. Investigating technologies and techniques for flood monitoring and detecting [Ph.D thesis]. Norman: University of Oklahoma.
   Google Scholar
• Nevo S, Morin E, Gerzi RA, Metzger A, et al. 2022. Flood forecasting with machine learning models in an operational framework. Hydrol Earth Syst Sci. 26:4013–4032. doi:10.5194/hess-26-4013-2022.
   Web of Science ®Google Scholar
• Noar AZ, Kamal M. 2017. The development of smart flood monitoring system using ultrasonic sensor with Blynk applications. 4th IEEE International Conference on Smart Instrumentation, Measurement and Applications; Putrajaya, Malaysia: IEEE. p. 28–30.
   Google Scholar
• Pagatpat JC, Arellano AC, Gerasta OJ. 2015. GSM &web-based flood monitoring system. IOP Conference Series: Materials Science and Engineering. Vol. 79, p. 1–8.
   Google Scholar
• Paul A, Das P. 2014. Flood prediction model using artificial neural network. Int J Comput Appl Technol Res. 3(7):473–478. doi:10.7753/IJCATR0307.1016.
   Google Scholar
• Perera D, Agnihotri J, Seidou O, Djalante R. 2020. Identifying societal challenges in flood early warning systems. Int J Disast Risk Re. 51:101794. doi:10.1016/j.ijdrr.2020.101794.
   Web of Science ®Google Scholar
• Philip JR. 1957. The theory of infiltration: 1. The infiltration equation and its solution. Soil Sci. 83:345–358. doi:10.1097/00010694-195705000-00002.
   Google Scholar
• Saharia T, Baruah RK, Goswami R, Sonowal D. 2021. Validation and implementation of a smart flood surveillance system based on wireless sensor network. MNDCS-2021; Jan 30-31; NIT Silchar.
   Google Scholar
• Sanders W, Li D, Li W, Fang ZN. 2022. Data-driven flood alert system (FAS) using extreme gradient boosting (XGBoost) to forecast flood stages. Water. 14(5):747. doi:10.3390/w14050747.
   Web of Science ®Google Scholar
• Shpakov T. 2021. ‘Bayesian Deep Learning for Flood Detection’, ETH Zurich [Bachelor Thesis], December 12.
   Google Scholar
• Tabbussum R, Dar A. 2020. Comparative analysis of neural network training algorithms for the flood forecast modeling of an alluvial Himalayan River. J Flood Risk Manag. 13(4):1–18.
   Web of Science ®Google Scholar
• Tanim AH, McRae CB, Tavakol DH, Goharian E. 2022. Flood detection in urban areas using satellite imagery and machine learning. Water. 14(7):1140. doi:10.3390/w14071140.
   Web of Science ®Google Scholar
• Thekkil TM, Prabakaran N. 2017. Real-time WSN based early flood detection and control monitoring system. International Conference on Intelligent Computing, Instrumentation and Control Technologies. p.1709–1713.
   Google Scholar
• World Meteorological Organization. 2019. World meteorological organization: Global Climate in 2015-2019: Climate change accelerates. accessed in Sep 2019. https://wmo.int/media/news/global-climate-2015-2019-climate-change-accelerates.
   Google Scholar
• World meteorological organization: Global Climate in 2015-2019: Climate change accelerates. accessed in Sep 2019. https://wmo.int/media/news/global-climate-2015-2019-climate-change-accelerates.
   Google Scholar
• Yang L, Cervone G. 2019. Analysis of remote sensing imagery for disaster assessment using deep learning: a case study of flooding event. Soft Comput. 23:13393–13408. doi:10.1007/s00500-019-03878-8.
   Web of Science ®Google Scholar
• Yang ZR, Yang Z. 2014. 6.01 - Artificial Neural Networks. In: Comprehensive Biomedical Physics. Vol. 6. Elsevier; p. 1–17. https://doi.org/10.1016/B978-0-444-53632-7.01101-1.
   Google Scholar
• Yuliandoko H, Subono VA, SholehHadiPramono PS. 2018. Design of flood warning system based IoT and water characteristics. Telkomnika. 16(5):2101–2110. doi:10.12928/telkomnika.v16i5.7636.
   Google Scholar
• Zheng W. 2019. Algorithm of flood monitoring and analysis based on SMAP data. International Information Technology and Artificial Intelligence Conference; Chongqing, China. p. 911–914.
   Google Scholar
• Zhou Y, Cui Z, Lin K, Sheng S, Chen H, Guo S, Xu CY. 2022. Short-term flood probability density forecasting using a conceptual hydrological model with machine learning techniques. J Hydrol. 604:127255. doi:10.1016/j.jhydrol.2021.127255.
   Web of Science ®Google Scholar