Streamflow and sediment load prediction using linear genetic programming

Daily flow and suspended sediment discharge are two major hydrological variables that affect rivers’ morphology and ecosystem, particularly during flood events. Artificial neural networks (ANNs) have been successfully used to model and predict these variables in recent studies. However, these are implicit and cannot be simply used in practice. In this paper, linear genetic programming (LGP) approach has been suggested to develop explicit models to predict these variables in two rivers in Iran. The explicit relationships (prediction rules) evolved by LGP take the form of equations or program codes, which can be checked for its physical consistency. The results showed that the LGP outperforms ANNs to get global maximum and minimum discharges providing lowest root mean squared error and higher coefficient of efficiency both for training and validation periods.Nehirlerin morfolojisini, ekosistemi ve özellikle taşkın olaylarını etkileyen iki ana değişken askıdaki sediment ve günlük akımlardır. Yapay sinir ağları (YSA), bu değişkenleri modellemek ve tahmin etmek için yakın zamanda yapılmış çalışmalarda başarıyla kullanılmıştır. Bununla birlikte, bunlar kapalı yöntemlerdir ve pratik uygulamalarda kolaylıkla kullanılamazlar. Bu makalede, İran'daki iki nehirde bu değişkenleri tahmin etmek üzere açık modeller geliştirmek için doğrusal genetik programlama (DGP) yaklaşımı önerilmiştir. DGP tarafından geliştirilen açık ilişkiler (tahmin kuralları), fiziksel tutarlılığı açısından kontrol edilebilen denklemler veya program kodları şeklindedir. Sonuçlar, global maksimum ve minimum akımları elde etme noktasında, DGP’nin YSA’ya göre daha başarılı olduğunu gerek kalibrasyon gerekse doğrulama aşamalarında hataların karelerinin ortalamasının karekökünün en düşük, verimlilik katsayısının ise daha yüksek olmasını sağlayarak göstermiştir.No sponso

Month ahead rainfall forecasting using gene expression programming

In the present study, gene expression programming (GEP) technique was used to develop one-month ahead monthly rainfall forecasting models in two meteorological stations located at a semi-arid region, Iran. GEP was trained and tested using total monthly rainfall (TMR) time series measured at the stations. Time lagged series of TMR samples having weak stationary state were used as inputs for the modeling. Performance of the best evolved models were compared with those of classic genetic programming (GP) and autoregressive state-space (ASS) approaches using coefficient of efficiency (R2) and root mean squared error measures. The results showed good performance (0.53<R2<0.56) for GEP models at testing period. In both stations, the best model evolved by GEP outperforms the GP and are significantly superior to the ASS models.No sponso

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

Conjunctive Management of Water Resources under Climate Change Projection Uncertainty

Goal of this study is to investigate the impacts of climate change projection uncertainty on conjunctive use of water resources. To pursue this goal first, a conjunctive-use model is developed for management of groundwater and surface water resources via mixed integer linear fractional programming (MILFP). The conjunctive management model maximizes the ratio of groundwater usage to reservoir water usage. A conditional head constraint is imposed to maintain groundwater sustainability. A transformation approach is introduced to transform the conditional head constraint into a set of mixed integer linear constraints in terms of groundwater head. A supply network is proposed to apply the conjunctive-use model to northern Louisiana and southern Arkansas. Then, simple model averaging (SMA), reliability ensemble averaging (REA), and hierarchical Bayesian model averaging (HBMA) are utilized as ensemble averaging methods to provide a thorough understanding of the impacts of climate change on future runoff for the study area. An ensemble of 78 hydroclimate models is formed by forcing HELP3 with climate data from combinations of 13 GCMs, 2 RCPs, and 3 downscaling methods. Runoff projections obtained from SMA, REA, and HBMA are compared. The Analysis of Variance (ANOVA) is used to quantify the sources of uncertainty of SMA projection and compare to the estimations made by HBMA. Both methods show similar contribution of uncertainty indicating that GCMs are the dominant source of uncertainty. At last, the proposed conjunctive use model is applied to optimize the conjunctive use of future surface water and groundwater resources under climate change projection. Future inflows to the reservoirs are estimated from the future runoffs projected through hydroclimate modeling, where the Variable Infiltration Capacity (VIC) model and 11 GCM RCP8.5 downscaled climate outputs are considered. Bayesian model averaging (BMA) is adopted to quantify uncertainty in future runoff projections and reservoir inflow projections due to uncertain future climate projections. The results from the developed conjunctive management model indicate that the future reservoir water even with low inflow projections at 2.5% cumulative probability would be able to counterbalance groundwater pumping reduction to satisfy demands while improving the Sparta aquifer through conditional groundwater head constraint

Long-Term Evaluation of Norris Reservoir Operation Under Climate Change

This study aimed to address the potential long-term effects of future climate change on the Tennessee Valley Authority’s (TVA’s) operation policy for Norris Reservoir. The Community Earth System Model 1.0 (CESM1.0), a general circulation model (GCM) accessible through the Intergovernmental Panel on Climate Change’s (IPCC’s) Coupled Model Intercomparison Project Phase 5 (CMIP5), with the Representative Concentration Pathway 4.5 (RCP4.5) was used to obtain projected precipitation and temperature data for three future climate scenarios, 2030’s, 2050’s, and 2070’s. Three hydrologic models were individually calibrated on 30 years of observed runoff data and combined utilizing linear programming to consider the strengths of each model. Inflow hydrographs were simulated for the future time spans using projected precipitation and temperature. Reservoir routing was then simulated using the inflow hydrographs via mass balance and the current operation policy to determine the storage elevation of the reservoir. Next, the routing simulations were utilized as input for a genetic algorithm forced optimization model, to minimize an elevation-based penalty value, optimizing Norris Reservoir’s operation policy. Finally, the operation performance of Norris Reservoir’s current operation policy versus the policies generated by the developed optimization model for each projected scenario were evaluated. The results suggested a 20.7, 23.8, and 24.3 percent increase in runoff for the 2030’s, 2050’s, and 2070’s, respectively. Although the current policy was able to support this increase in runoff, the optimization model decreased operation penalties by 23.3, 22.2, and 24.4 percent for the 2030’s, 2050’s and 2070’s, respectively. These results can provide substantial insight to TVA hydrologists and decision makers that their current policy may require re-evaluation, considering the potential impacts of climate change

Modeling of Rainfall-Runoff Correlations Using Artificial Neural Network-A Case Study of Dharoi Watershed of a Sabarmati River Basin, India

The use of an Artificial Neural Network (ANN) is becoming common due to its ability to analyse complex nonlinear events. An ANN has a flexible, convenient and easy mathematical structure to identify the nonlinear relationships between input and output data sets. This capability could efficiently be employed for the different hydrological models such as rainfall-runoff models, which are inherently nonlinear in nature. Artificial Neural Networks (ANN) can be used in cases where the available data is limited. The present work involves the development of an ANN model using Feed-Forward Back Propagation algorithm for establishing monthly and annual rainfall runoff correlations. The hydrologic variables used were monthly and annual rainfall and runoff for monthly and annual time period of monsoon season. The ANN model developed in this study is applied to Dharoi reservoir watersheds of Sabarmati river basin of India. The hydrologic data were available for twenty-nine years at Dharoi station at Dharoi dam project. The model results yielding into the least error is recommended for simulating the rainfall-runoff characteristics of the watersheds. The obtained results can help the water resource managers to operate the reservoir properly in the case of extreme events such as flooding and drought

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

Optimal Design of a Rain Gauge Network to Improve Streamflow Forecasting

Enhanced streamflow forecasting has always been an important task for researchers and water resources managers. However, streamflow forecasting is often challenging owing to the complexity of hydrologic systems. The accuracy of streamflow forecasting mainly depends on the input data, especially rainfall as it constitutes the key input in transforming rainfall into runoff. This emphasizes the need for incorporating accurate rainfall input in streamflow forecasting models in order to achieve enhanced streamflow forecasting. Based on past research, it is well-known that an optimal rain gauge network is necessary to provide high quality rainfall estimates. Therefore, this study focused on the optimal design of a rain gauge network and integration of the optimal network-based rainfall input in artificial neural network (ANN) models to enhance the accuracy of streamflow forecasting. The Middle Yarra River catchment in Victoria, Australia was selected as the case study catchment, since the management of water resources in the catchment is of great importance to the majority of Victorians

Multi-agent system for flood forecasting in Tropical River Basin

It is well known, the problems related to the generation of floods, their control, and management, have been treated with traditional hydrologic modeling tools focused on the study and the analysis of the precipitation-runoff relationship, a physical process which is driven by the hydrological cycle and the climate regime and that is directly proportional to the generation of floodwaters. Within the hydrological discipline, they classify these traditional modeling tools according to three principal groups, being the first group defined as trial-and-error models (e.g., "black-models"), the second group are the conceptual models, which are categorized in three main sub-groups as "lumped", "semi-lumped" and "semi-distributed", according to the special distribution, and finally, models that are based on physical processes, known as "white-box models" are the so-called "distributed-models". On the other hand, in engineering applications, there are two types of models used in streamflow forecasting, and which are classified concerning the type of measurements and variables required as "physically based models", as well as "data-driven models". The Physically oriented prototypes present an in-depth account of the dynamics related to the physical aspects that occur internally among the different systems of a given hydrographic basin. However, aside from being laborious to implement, they rely thoroughly on mathematical algorithms, and an understanding of these interactions requires the abstraction of mathematical concepts and the conceptualization of the physical processes that are intertwined among these systems. Besides, models determined by data necessitates an a-priori understanding of the physical laws controlling the process within the system, and they are bound to mathematical formulations, which require a lot of numeric information for field adjustments. Therefore, these models are remarkably different from each other because of their needs for data, and their interpretation of physical phenomena. Although there is considerable progress in hydrologic modeling for flood forecasting, several significant setbacks remain unresolved, given the stochastic nature of the hydrological phenomena, is the challenge to implement user-friendly, re-usable, robust, and reliable forecasting systems, the amount of uncertainty they must deal with when trying to solve the flood forecasting problem. However, in the past decades, with the growing environment and development of the artificial intelligence (AI) field, some researchers have seldomly attempted to deal with the stochastic nature of hydrologic events with the application of some of these techniques. Given the setbacks to hydrologic flood forecasting previously described this thesis research aims to integrate the physics-based hydrologic, hydraulic, and data-driven models under the paradigm of Multi-agent Systems for flood forecasting by designing and developing a multi-agent system (MAS) framework for flood forecasting events within the scope of tropical watersheds. With the emergence of the agent technologies, the "agent-based modeling" and "multiagent systems" simulation methods have provided applications for some areas of hydro base management like flood protection, planning, control, management, mitigation, and forecasting to combat the shocks produced by floods on society; however, all these focused on evacuation drills, and the latter not aimed at the tropical river basin, whose hydrological regime is extremely unique. In this catchment modeling environment approach, it was applied the multi-agent systems approach as a surrogate of the conventional hydrologic model to build a system that operates at the catchment level displayed with hydrometric stations, that use the data from hydrometric sensors networks (e.g., rainfall, river stage, river flow) captured, stored and administered by an organization of interacting agents whose main aim is to perform flow forecasting and awareness, and in so doing enhance the policy-making process at the watershed level. Section one of this document surveys the status of the current research in hydrologic modeling for the flood forecasting task. It is a journey through the background of related concerns to the hydrological process, flood ontologies, management, and forecasting. The section covers, to a certain extent, the techniques, methods, and theoretical aspects and methods of hydrological modeling and their types, from the conventional models to the present-day artificial intelligence prototypes, making special emphasis on the multi-agent systems, as most recent modeling methodology in the hydrological sciences. However, it is also underlined here that the section does not contribute to an all-inclusive revision, rather its purpose is to serve as a framework for this sort of work and a path to underline the significant aspects of the works. In section two of the document, it is detailed the conceptual framework for the suggested Multiagent system in support of flood forecasting. To accomplish this task, several works need to be carried out such as the sketching and implementation of the system’s framework with the (Belief-Desire-Intention model) architecture for flood forecasting events within the concept of the tropical river basin. Contributions of this proposed architecture are the replacement of the conventional hydrologic modeling with the use of multi-agent systems, which makes it quick for hydrometric time-series data administration and modeling of the precipitation-runoff process which conveys to flood in a river course. Another advantage is the user-friendly environment provided by the proposed multi-agent system platform graphical interface, the real-time generation of graphs, charts, and monitors with the information on the immediate event taking place in the catchment, which makes it easy for the viewer with some or no background in data analysis and their interpretation to get a visual idea of the information at hand regarding the flood awareness. The required agents developed in this multi-agent system modeling framework for flood forecasting have been trained, tested, and validated under a series of experimental tasks, using the hydrometric series information of rainfall, river stage, and streamflow data collected by the hydrometric sensor agents from the hydrometric sensors.Como se sabe, los problemas relacionados con la generación de inundaciones, su control y manejo, han sido tratados con herramientas tradicionales de modelado hidrológico enfocados al estudio y análisis de la relación precipitación-escorrentía, proceso físico que es impulsado por el ciclo hidrológico y el régimen climático y este esta directamente proporcional a la generación de crecidas. Dentro de la disciplina hidrológica, clasifican estas herramientas de modelado tradicionales en tres grupos principales, siendo el primer grupo el de modelos empíricos (modelos de caja negra), modelos conceptuales (o agrupados, semi-agrupados o semi-distribuidos) dependiendo de la distribución espacial y, por último, los basados en la física, modelos de proceso (o "modelos de caja blanca", y/o distribuidos). En este sentido, clasifican las aplicaciones de predicción de caudal fluvial en la ingeniería de recursos hídricos en dos tipos con respecto a los valores y parámetros que requieren en: modelos de procesos basados en la física y la categoría de modelos impulsados por datos. Los modelos basados en la física proporcionan una descripción detallada de la dinámica relacionada con los aspectos físicos que ocurren internamente entre los diferentes sistemas de una cuenca hidrográfica determinada. Sin embargo, aparte de ser complejos de implementar, se basan completamente en algoritmos matemáticos, y la comprensión de estas interacciones requiere la abstracción de conceptos matemáticos y la conceptualización de los procesos físicos que se entrelazan entre estos sistemas. Además, los modelos impulsados por datos no requieren conocimiento de los procesos físicos que gobiernan, sino que se basan únicamente en ecuaciones empíricas que necesitan una gran cantidad de datos y requieren calibración de los datos en el sitio. Los dos modelos difieren significativamente debido a sus requisitos de datos y de cómo expresan los fenómenos físicos. La elaboración de modelos hidrológicos para el pronóstico de inundaciones ha dado grandes pasos, pero siguen sin resolverse algunos contratiempos importantes, dada la naturaleza estocástica de los fenómenos hidrológicos, es el desafío de implementar sistemas de pronóstico fáciles de usar, reutilizables, robustos y confiables, la cantidad de incertidumbre que deben afrontar al intentar resolver el problema de la predicción de inundaciones. Sin embargo, en las últimas décadas, con el entorno creciente y el desarrollo del campo de la inteligencia artificial (IA), algunos investigadores rara vez han intentado abordar la naturaleza estocástica de los eventos hidrológicos con la aplicación de algunas de estas técnicas. Dados los contratiempos en el pronóstico de inundaciones hidrológicas descritos anteriormente, esta investigación de tesis tiene como objetivo integrar los modelos hidrológicos, basados en la física, hidráulicos e impulsados por datos bajo el paradigma de Sistemas de múltiples agentes para el pronóstico de inundaciones por medio del bosquejo y desarrollo del marco de trabajo del sistema multi-agente (MAS) para los eventos de predicción de inundaciones en el contexto de cuenca hidrográfica tropical. Con la aparición de las tecnologías de agentes, se han emprendido algunos enfoques de simulación recientes en la investigación hidrológica con modelos basados en agentes y sistema multi-agente, principalmente en alerta por inundaciones, seguridad y planificación de inundaciones, control y gestión de inundaciones y pronóstico de inundaciones, todos estos enfocado a simulacros de evacuación, y este último no dirigido a la cuenca tropical, cuyo régimen hidrológico es extremadamente único. En este enfoque de entorno de modelado de cuencas, se aplican los enfoques de sistemas multi-agente como un sustituto del modelado hidrológico convencional para construir un sistema que opera a nivel de cuenca con estaciones hidrométricas desplegadas, que utilizan los datos de redes de sensores hidrométricos (por ejemplo, lluvia , nivel del río, caudal del río) capturado, almacenado y administrado por una organización de agentes interactuantes cuyo objetivo principal es realizar pronósticos de caudal y concientización para mejorar las capacidades de soporte en la formulación de políticas a nivel de cuenca hidrográfica. La primera sección de este documento analiza el estado del arte sobre la investigación actual en modelos hidrológicos para la tarea de pronóstico de inundaciones. Es un viaje a través de los antecedentes preocupantes relacionadas con el proceso hidrológico, las ontologías de inundaciones, la gestión y la predicción. El apartado abarca, en cierta medida, las técnicas, métodos y aspectos teóricos y métodos del modelado hidrológico y sus tipologías, desde los modelos convencionales hasta los prototipos de inteligencia artificial actuales, haciendo hincapié en los sistemas multi-agente, como un enfoque de simulación reciente en la investigación hidrológica. Sin embargo, se destaca que esta sección no contribuye a una revisión integral, sino que su propósito es servir de marco para este tipo de trabajos y una guía para subrayar los aspectos significativos de los trabajos. En la sección dos del documento, se detalla el marco de trabajo propuesto para el sistema multi-agente para el pronóstico de inundaciones. Los trabajos realizados comprendieron el diseño y desarrollo del marco de trabajo del sistema multi-agente con la arquitectura (modelo Creencia-Deseo-Intención) para la predicción de eventos de crecidas dentro del concepto de cuenca hidrográfica tropical. Las contribuciones de esta arquitectura propuesta son el reemplazo del modelado hidrológico convencional con el uso de sistemas multi-agente, lo que agiliza la administración de las series de tiempo de datos hidrométricos y el modelado del proceso de precipitación-escorrentía que conduce a la inundación en el curso de un río. Otra ventaja es el entorno amigable proporcionado por la interfaz gráfica de la plataforma del sistema multi-agente propuesto, la generación en tiempo real de gráficos, cuadros y monitores con la información sobre el evento inmediato que tiene lugar en la cuenca, lo que lo hace fácil para el espectador con algo o sin experiencia en análisis de datos y su interpretación para tener una idea visual de la información disponible con respecto a la cognición de las inundaciones. Los agentes necesarios desarrollados en este marco de modelado de sistemas multi-agente para el pronóstico de inundaciones han sido entrenados, probados y validados en una serie de tareas experimentales, utilizando la información de la serie hidrométrica de datos de lluvia, nivel del río y flujo del curso de agua recolectados por los agentes sensores hidrométricos de los sensores hidrométricos de campo.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: María Araceli Sanchis de Miguel.- Secretario: Juan Gómez Romero.- Vocal: Juan Carlos Corrale

Comparative Evaluation of Deep Learning Techniques in Streamflow Monthly Prediction of the Zarrine River Basin

Predicting monthly streamflow is essential for hydrological analysis and water resource management. Recent advancements in deep learning, particularly long short-term memory (LSTM) and recurrent neural networks (RNN), exhibit extraordinary efficacy in streamflow forecasting. This study employs RNN and LSTM to construct data-driven streamflow forecasting models. Sensitivity analysis, utilizing the analysis of variance (ANOVA) method, also is crucial for model refinement and identification of critical variables. This study covers monthly streamflow data from 1979 to 2014, employing five distinct model structures to ascertain the most optimal configuration. Application of the models to the Zarrine River basin in northwest Iran, a major sub-basin of Lake Urmia, demonstrates the superior accuracy of the RNN algorithm over LSTM. At the outlet of the basin, quantitative evaluations demonstrate that the RNN model outperforms the LSTM model across all model structures. The S3 model, characterized by its inclusion of all input variable values and a four-month delay, exhibits notably exceptional performance in this aspect. The accuracy measures applicable in this particular context were RMSE (22.8), R2 (0.84), and NSE (0.8). This study highlights the Zarrine River’s substantial impact on variations in Lake Urmia’s water level. Furthermore, the ANOVA method demonstrates exceptional performance in discerning the relevance of input factors. ANOVA underscores the key role of station streamflow, upstream station streamflow, and maximum temperature in influencing the model’s output. Notably, the RNN model, surpassing LSTM and traditional artificial neural network (ANN) models, excels in accurately mimicking rainfall–runoff processes. This emphasizes the potential of RNN networks to filter redundant information, distinguishing them as valuable tools in monthly streamflow forecasting