Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Improving the Muskingum flood routing method using a hybrid of particle swarm optimization and bat algorithm

Flood prediction and control are among the major tools for decision makers and water resources planners to avoid flood disasters. The Muskingum model is one of the most widely used methods for flood routing prediction. The Muskingum model contains four parameters that must be determined for accurate flood routing. In this context, an optimization process that self-searches for the optimal values of these four parameters might improve the traditional Muskingum model. In this study, a hybrid of the bat algorithm (BA) and the particle swarm optimization (PSO) algorithm, i.e., the hybrid bat-swarm algorithm (HBSA), was developed for the optimal determination of these four parameters. Data for the three different case studies from the USA and the UK were utilized to examine the suitability of the proposed HBSA for flood routing. Comparative analyses based on the sum of squared deviations (SSD), sum of absolute deviations (SAD), error of peak discharge, and error of time to peak showed that the proposed HBSA based on the Muskingum model achieved excellent flood routing accuracy compared to that of other methods while requiring less computational time

Real Time Flow Forecasting in a Mountain River Catchment Using Conceptual Models with Simple Error Correction Scheme

[EN] Methods in operational hydrology for real-time flash-flood forecasting need to be simple enough to match requirements of real-time system management. For this reason, hydrologic routing methods are widely used in river engineering. Among them, the popular Muskingum method is the most extended one, due to its simplicity and parsimonious formulation involving only two parameters. In the present application, two simple conceptual models with an error correction scheme were used. They were applied in practice to a mountain catchment located in the central Pyrenees (North of Spain), where occasional flash flooding events take place. Several relevant historical flood events have been selected for calibration and validation purposes. The models were designed to produce real-time predictions at the downstream gauge station, with variable lead times during a flood event. They generated accurate estimates of forecasted discharges at the downstream end of the river reach. For the validation data set and 2 h lead time, the estimated Nash-Sutcliffe coefficient was 0.970 for both models tested. The quality of the results, together with the simplicity of the formulations proposed, suggests an interesting potential for the practical use of these schemes for operational hydrology purposes.The authors wish to acknowledge support from Confederacion Hidrografica del Ebro.Montes, N.; Aranda Domingo, JÁ.; García-Bartual, R. (2020). Real Time Flow Forecasting in a Mountain River Catchment Using Conceptual Models with Simple Error Correction Scheme. Water. 12(5):1-18. https://doi.org/10.3390/w12051484S118125MORAMARCO, T., BARBETTA, S., MELONE, F., & SINGH, V. P. (2006). A real-time stage Muskingum forecasting model for a site without rating curve. Hydrological Sciences Journal, 51(1), 66-82. doi:10.1623/hysj.51.1.66Perumal, M., Moramarco, T., Barbetta, S., Melone, F., & Sahoo, B. (2011). Real-time flood stage forecasting by Variable Parameter Muskingum Stage hydrograph routing method. Hydrology Research, 42(2-3), 150-161. doi:10.2166/nh.2011.063Clark, C. O. (1945). Storage and the Unit Hydrograph. Transactions of the American Society of Civil Engineers, 110(1), 1419-1446. doi:10.1061/taceat.0005800Cunge, J. A. (1969). On The Subject Of A Flood Propagation Computation Method (Musklngum Method). Journal of Hydraulic Research, 7(2), 205-230. doi:10.1080/00221686909500264Dooge, J. C. I., Strupczewski, W. G., & Napiórkowski, J. J. (1982). Hydrodynamic derivation of storage parameters of the Muskingum model. Journal of Hydrology, 54(4), 371-387. doi:10.1016/0022-1694(82)90163-9Ponce, V. M., & Changanti, P. V. (1994). Variable-parameter Muskingum-Cunge method revisited. Journal of Hydrology, 162(3-4), 433-439. doi:10.1016/0022-1694(94)90241-0Ponce, V. M., & Theurer, F. D. (1983). Closure to « Accuracy Criteria in Diffusion Routing » by Victor Miguel Ponce and Fred D. Theurer (June, 1982). Journal of Hydraulic Engineering, 109(5), 806-807. doi:10.1061/(asce)0733-9429(1983)109:5(806)KUNDZEWICZ, Z. W. (1986). Physically based hydrological flood routing methods. Hydrological Sciences Journal, 31(2), 237-261. doi:10.1080/02626668609491042Singh, V. P., & Scarlatos, P. D. (1987). Analysis of Nonlinear Muskingum Flood Routing. Journal of Hydraulic Engineering, 113(1), 61-79. doi:10.1061/(asce)0733-9429(1987)113:1(61)Perumal, M. (1992). Multilinear muskingum flood routing method. Journal of Hydrology, 133(3-4), 259-272. doi:10.1016/0022-1694(92)90258-wTang, X., Knight, D. W., & Samuels, P. G. (1999). Variable parameter Muskingum-Cunge method for flood routing in a compound channel. Journal of Hydraulic Research, 37(5), 591-614. doi:10.1080/00221689909498519Al-Humoud, J. M., & Esen, I. I. (2006). Approximate Methods for the Estimation of Muskingum Flood Routing Parameters. Water Resources Management, 20(6), 979-990. doi:10.1007/s11269-006-9018-2Todini, E. (2007). A mass conservative and water storage consistent variable parameter Muskingum-Cunge approach. Hydrology and Earth System Sciences, 11(5), 1645-1659. doi:10.5194/hess-11-1645-2007Brakensiek, D. L. (1963). Estimating coefficients for storage flood routing. Journal of Geophysical Research, 68(24), 6471-6474. doi:10.1029/jz068i024p06471Birkhead, A. L., & James, C. S. (2002). Muskingum river routing with dynamic bank storage. Journal of Hydrology, 264(1-4), 113-132. doi:10.1016/s0022-1694(02)00068-9Perumal, M., & Price, R. K. (2013). A fully mass conservative variable parameter McCarthy–Muskingum method: Theory and verification. Journal of Hydrology, 502, 89-102. doi:10.1016/j.jhydrol.2013.08.023O’DONNELL, T. (1985). A direct three-parameter Muskingum procedure incorporating lateral inflow. Hydrological Sciences Journal, 30(4), 479-496. doi:10.1080/02626668509491013Kshirsagar, M. M., Rajagopalan, B., & Lal, U. (1995). Optimal parameter estimation for Muskingum routing with ungauged lateral inflow. Journal of Hydrology, 169(1-4), 25-35. doi:10.1016/0022-1694(94)02670-7Yadav, B., Perumal, M., & Bardossy, A. (2015). Variable parameter McCarthy–Muskingum routing method considering lateral flow. Journal of Hydrology, 523, 489-499. doi:10.1016/j.jhydrol.2015.01.068PERUMAL, M. (1994). Hydrodynamic derivation of a variable parameter Muskingum method: 1. Theory and solution procedure. Hydrological Sciences Journal, 39(5), 431-442. doi:10.1080/02626669409492766Perumal, M., E., O., & Raju, K. G. R. (2001). Field Applications of a Variable-Parameter Muskingum Method. Journal of Hydrologic Engineering, 6(3), 196-207. doi:10.1061/(asce)1084-0699(2001)6:3(196)Perumal, M., & Sahoo, B. (2007). Applicability criteria of the variable parameter Muskingum stage and discharge routing methods. Water Resources Research, 43(5). doi:10.1029/2006wr004909Gill, M. A. (1978). Flood routing by the Muskingum method. Journal of Hydrology, 36(3-4), 353-363. doi:10.1016/0022-1694(78)90153-1Tung, Y. (1985). River Flood Routing by Nonlinear Muskingum Method. Journal of Hydraulic Engineering, 111(12), 1447-1460. doi:10.1061/(asce)0733-9429(1985)111:12(1447)Yoon, J., & Padmanabhan, G. (1993). Parameter Estimation of Linear and Nonlinear Muskingum Models. Journal of Water Resources Planning and Management, 119(5), 600-610. doi:10.1061/(asce)0733-9496(1993)119:5(600)Mohan, S. (1997). Parameter Estimation of Nonlinear Muskingum Models Using Genetic Algorithm. Journal of Hydraulic Engineering, 123(2), 137-142. doi:10.1061/(asce)0733-9429(1997)123:2(137)Luo, J., & Xie, J. (2010). Parameter Estimation for Nonlinear Muskingum Model Based on Immune Clonal Selection Algorithm. Journal of Hydrologic Engineering, 15(10), 844-851. doi:10.1061/(asce)he.1943-5584.0000244Kang, L., & Zhang, S. (2016). Application of the Elitist-Mutated PSO and an Improved GSA to Estimate Parameters of Linear and Nonlinear Muskingum Flood Routing Models. PLOS ONE, 11(1), e0147338. doi:10.1371/journal.pone.0147338Geem, Z. W. (2006). Parameter Estimation for the Nonlinear Muskingum Model Using the BFGS Technique. Journal of Irrigation and Drainage Engineering, 132(5), 474-478. doi:10.1061/(asce)0733-9437(2006)132:5(474)Chu, H.-J., & Chang, L.-C. (2009). Applying Particle Swarm Optimization to Parameter Estimation of the Nonlinear Muskingum Model. Journal of Hydrologic Engineering, 14(9), 1024-1027. doi:10.1061/(asce)he.1943-5584.0000070Barati, R. (2011). Parameter Estimation of Nonlinear Muskingum Models Using Nelder-Mead Simplex Algorithm. Journal of Hydrologic Engineering, 16(11), 946-954. doi:10.1061/(asce)he.1943-5584.0000379Karahan, H., Gurarslan, G., & Geem, Z. W. (2013). Parameter Estimation of the Nonlinear Muskingum Flood-Routing Model Using a Hybrid Harmony Search Algorithm. Journal of Hydrologic Engineering, 18(3), 352-360. doi:10.1061/(asce)he.1943-5584.0000608Chen, X. Y., Chau, K. W., & Busari, A. O. (2015). A comparative study of population-based optimization algorithms for downstream river flow forecasting by a hybrid neural network model. Engineering Applications of Artificial Intelligence, 46, 258-268. doi:10.1016/j.engappai.2015.09.010Latt, Z. Z. (2015). Application of Feedforward Artificial Neural Network in Muskingum Flood Routing: a Black-Box Forecasting Approach for a Natural River System. Water Resources Management, 29(14), 4995-5014. doi:10.1007/s11269-015-1100-1Niazkar, M., & Afzali, S. H. (2016). Application of New Hybrid Optimization Technique for Parameter Estimation of New Improved Version of Muskingum Model. Water Resources Management, 30(13), 4713-4730. doi:10.1007/s11269-016-1449-9Kucukkoc, I., & Zhang, D. Z. (2015). Integrating ant colony and genetic algorithms in the balancing and scheduling of complex assembly lines. The International Journal of Advanced Manufacturing Technology, 82(1-4), 265-285. doi:10.1007/s00170-015-7320-yBazargan, J., & Norouzi, H. (2018). Investigation the Effect of Using Variable Values for the Parameters of the Linear Muskingum Method Using the Particle Swarm Algorithm (PSO). Water Resources Management, 32(14), 4763-4777. doi:10.1007/s11269-018-2082-6Ehteram, M., Mousavi, S. F., Karami, H., Farzin, S., Singh, V. P., Chau, K., & El-Shafie, A. (2018). Reservoir operation based on evolutionary algorithms and multi-criteria decision-making under climate change and uncertainty. Journal of Hydroinformatics, 20(2), 332-355. doi:10.2166/hydro.2018.094Pei, J., Su, Y., & Zhang, D. (2016). Fuzzy energy management strategy for parallel HEV based on pigeon-inspired optimization algorithm. Science China Technological Sciences, 60(3), 425-433. doi:10.1007/s11431-016-0485-8SCHUMM, S. A. (1956). EVOLUTION OF DRAINAGE SYSTEMS AND SLOPES IN BADLANDS AT PERTH AMBOY, NEW JERSEY. Geological Society of America Bulletin, 67(5), 597. doi:10.1130/0016-7606(1956)67[597:eodsas]2.0.co;2Baláž, M., Danáčová, M., & Szolgay, J. (2010). On the use of the Muskingum method for the simulation of flood wave movements. Slovak Journal of Civil Engineering, 18(3), 14-20. doi:10.2478/v10189-010-0012-6Franchini, M., & Lamberti, P. (1994). A flood routing Muskingum type simulation and forecasting model based on level data alone. Water Resources Research, 30(7), 2183-2196. doi:10.1029/94wr00536Yadav, O. P., Singh, N., Goel, P. S., & Itabashi-Campbell, R. (2003). A Framework for Reliability Prediction During Product Development Process Incorporating Engineering Judgments. Quality Engineering, 15(4), 649-662. doi:10.1081/qen-120018396Weinmann, P. E., & Laurenson, E. M. (1979). Approximate Flood Routing Methods: A Review. Journal of the Hydraulics Division, 105(12), 1521-1536. doi:10.1061/jyceaj.0005329Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10(3), 282-290. doi:10.1016/0022-1694(70)90255-6Kitanidis, P. K., & Bras, R. L. (1980). Real-time forecasting with a conceptual hydrologic model: 2. Applications and results. Water Resources Research, 16(6), 1034-1044. doi:10.1029/wr016i006p01034Franchini, M., Bernini, A., Barbetta, S., & Moramarco, T. (2011). Forecasting discharges at the downstream end of a river reach through two simple Muskingum based procedures. Journal of Hydrology, 399(3-4), 335-352. doi:10.1016/j.jhydrol.2011.01.009Alhumoud, J., & Almashan, N. (2019). Muskingum Method with Variable Parameter Estimation. Mathematical Modelling of Engineering Problems, 6(3), 355-362. doi:10.18280/mmep.060306Yang, R., Hou, B., Xiao, W., Liang, C., Zhang, X., Li, B., & Yu, H. (2019). The applicability of real-time flood forecasting correction techniques coupled with the Muskingum method. Hydrology Research, 51(1), 17-29. doi:10.2166/nh.2019.12

ESTIMACIÓN DEL TRÁNSITO DE AVENIDAS EMPLEANDO EL MÉTODO DE MUSKINGUM EN LA ESTACIÓN EL TAMBO DE LA CUENCA CHICAMA, PERÚ

El objetivo de la investigación es estimar el tránsito de avenidas empleando un tránsito del tipo hidrológico aplicado en ríos que en este caso se escogió el método de Muskingum o llamado también modelo de almacenamiento tipo cuña. El método está basado en las relaciones caudal y almacenamiento para los diferentes intervalos de tiempo los cuales pueden ser aplicables en cuencas hidrográficas pequeñas y medianas. El escenario de investigación fue la estación hidrométrica El Tambo perteneciente a la cuenca Chicama de Perú, considerando para el análisis los registros históricos completos del período 1993 al 2011. El tipo de investigación fue cuantitativo y diseño cuasi-experimental cuya metodología en referencia al método aplicado sugiere que se deben establecer las constantes “Kj” definida como la proporcionalidad del volumen en cierto intervalo de tiempo y “Xj” como la ponderación del tránsito a lo largo del río, ambas variables se complementan a partir de las funciones matemáticas de entrada y salida de los caudales. Los resultados obtenidos indican que el tránsito de avenidas a partir del método de Muskingum aplicado a la estación El Tambo es el adecuado para los coeficientes promedio “Kj” de 0.571 y “Xj” de 0.424, llegando a la conclusión que se puede determinar el tránsito de avenidas para un intervalo de 1 hora y el hidrograma de salida

Dynamic small world network topology for particle swarm optimization

Abstract: A new particle optimization algorithm with dynamic topology is proposed based on a small world network. The technique imitates the dissemination of information in a small world network by dynamically updating the neighborhood topology of the particle swarm optimization(PSO). In comparison with other four classic topologies and two PSO algorithms based on small world network, the proposed dynamic neighborhood strategy is more eÆective in coordinating the exploration and exploitation ability of PSO. Simulations demonstrated that the convergence of the swarms is faster than its competitors. Meanwhile, the proposed method maintains population diversity and enhances the global search ability for a series of benchmark problems

Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

The Optimization of Energy Supply Systems by Sequential Streamflow Routing Method and Invasive Weed Optimization Algorithm; Case Study: Karun II Hydroelectric Power Plant

Among the major sources of energy supply systems, hydroelectric power plants are more common. Energy supply during peak hours and less environmental issues are some of the most important advantages of hydroelectric power plants. In this study, designing parameters to supply maximum amount of energy was determined by using the simulation-optimization perspective and combination of IWO-WEAP models. Subsequently, the developed model has been applied for designing the Karun II hydroelectric power plant. The sequential streamflow routing method has been developed for obtaining energy in WEAP water resources management software. In addition the optimization algorithm has been applied to optimize the invasive weeds. To verify the performance of this method, obtained results for the firm energy were compared to those of the total energy. Using this method, for 1398 GWY (Giga watt per your) firm energy, the minimum and normal levels of operation were 668 and 672 m.a.s.l (meters above sea level), respectively, and the installation capacity calculated around 498 MW as optimal value

Hybrid harmony search algorithm for continuous optimization problems

Harmony Search (HS) algorithm has been extensively adopted in the literature to address optimization problems in many different fields, such as industrial design, civil engineering, electrical and mechanical engineering problems. In order to ensure its search performance, HS requires extensive tuning of its four parameters control namely harmony memory size (HMS), harmony memory consideration rate (HMCR), pitch adjustment rate (PAR), and bandwidth (BW). However, tuning process is often cumbersome and is problem dependent. Furthermore, there is no one size fits all problems. Additionally, despite many useful works, HS and its variant still suffer from weak exploitation which can lead to poor convergence problem. Addressing these aforementioned issues, this thesis proposes to augment HS with adaptive tuning using Grey Wolf Optimizer (GWO). Meanwhile, to enhance its exploitation, this thesis also proposes to adopt a new variant of the opposition-based learning technique (OBL). Taken together, the proposed hybrid algorithm, called IHS-GWO, aims to address continuous optimization problems. The IHS-GWO is evaluated using two standard benchmarking sets and two real-world optimization problems. The first benchmarking set consists of 24 classical benchmark unimodal and multimodal functions whilst the second benchmark set contains 30 state-of-the-art benchmark functions from the Congress on Evolutionary Computation (CEC). The two real-world optimization problems involved the three-bar truss and spring design. Statistical analysis using Wilcoxon rank-sum and Friedman of IHS-GWO’s results with recent HS variants and other metaheuristic demonstrate superior performance