Rainfall–Runoff Modeling Using Support Vector Machine in Snow-Affected Watershed

Sedighi, Fatemeh; Vafakhah, Mehdi; Javadi, Mohamad Reza

doi:10.1007/s13369-016-2095-5

Cited by 28 publications

(7 citation statements)

References 36 publications

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“…First, they showed the machine learning model can be used in snow-covered regions by acceptable accuracy for runoff modeling. Second, they resulted in the best condition ANN simulated runoff by the coefficient of determination equal 0.77 for validation section (Sedighi et al 2016). Another study was provided by Toth and Brath (2007) on the runoff real-time prediction capabilities by ANN models.…”

Section: Application Of Ann In Runoff Simulationmentioning

confidence: 98%

“…3 The structure of the ordinary ANN model of traditional methods and ANN methods (Jain and Kumar 2007). Sedighi et al (2016) employed ANN and SVM for runoff simulating in a snow-covered watershed (in Iran). First, they showed the machine learning model can be used in snow-covered regions by acceptable accuracy for runoff modeling.…”

Section: Application Of Ann In Runoff Simulationmentioning

confidence: 99%

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A review on the applications of machine learning for runoff modeling

Mohammadi

2021

Sustain. Water Resour. Manag.

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The growing menace of global warming and restrictions on access to water in each region is a huge threat to global hydrological sustainability. Hence, the perspective at which hydrological studies are currently being carried out across the world to quantify and understand the water cycle modeling requires a further boost. In the past few decades, the theoretical understanding of machine learning (ML) algorithms for solving engineering issues, and the application of this method to practical problems have made very significant progress. In the field of hydrology, ML has been using for a better understanding of hydrological complexities. Then, using ML-based approaches for hydrological simulation have been a popular method for runoff modeling in recent years; it seems necessary to understand the application of ML in runoff modeling fully. Current research seeks to have an overview for rainfall–runoff modeling using ML approaches in recent years, including integrated and ordinary ML techniques (such as ANFIS, ANN, and SVM models). The main hydrological topics in this review study include surface hydrology, streamflow, rainfall–runoff, and flood modeling via ML approaches. Therefore, in this study, the author has critically reviewed the characteristics of machine learning models in runoff simulation, including advantages and disadvantages of three widely used machine learning models.

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Section: Application Of Ann In Runoff Simulationmentioning

confidence: 98%

Section: Application Of Ann In Runoff Simulationmentioning

confidence: 99%

A review on the applications of machine learning for runoff modeling

Mohammadi

2021

Sustain. Water Resour. Manag.

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“…Kernel functions are used to map the data into higher-dimensional spaces. Studies including the recent Sedighi et al (2016) use SVR in rainfall-runoff modeling. SVR's performance is sensitive to choice of kernel (linear kernel, polynomial kernel, RBF kernel, and sigmoid kernel), which if appropriately selected can result in better prediction accuracy.…”

Section: Machine Learning In Water Resourcesmentioning

confidence: 99%

Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction

Chadalawada

Herath

Babovic

2020

Water Resources Research

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Models of water resources systems are conceived to capture the underlying environmental dynamics occurring within watersheds. All such models can be regarded as working hypotheses, differing in the aspects of process representation and conceptualization. Most of the associated efforts in the water resources research community is dedicated to development of new models that perform well under specific atmospheric conditions and catchment properties. In this context, flexible modeling frameworks are gaining importance as they facilitate the model building process by providing the model building blocks, whereby the hydrologist is free to assemble the model for task at hand. Such flexible models have high degree of transferability, which in turn aid in progressing toward a unified hydrological theory at catchment scale. However, in cases without sufficient insights regarding a catchment characteristics and/or lack of expert's knowledge, one may have to try a large number of model configurations based on available model building blocks to construct an appropriate model for the catchment of interest. Undoubtedly, this may be time consuming and computationally intensive. This paper proposes a novel model building algorithm, which uses the full potential of flexible modeling frameworks by searching the model space and inferring suitable model configurations relying on machine learning. Proposed machine learning algorithm is based on evolutionary computation approach using genetic programming (GP). State-of-art GP applications in rainfall-runoff modeling so far used the algorithm as a short-term forecasting tool that generates an expected future time series very similar to neural networks application. In this case, the proposed algorithm develops a physically meaningful rainfall-runoff model. Although at the moment we learn models using two flexible modeling frameworks (SUPERFLEX and FUSE), the model induction toolkit can be armed with any internal coherence building blocks. The model induction capabilities of the proposed framework have been evaluated on the Blackwater River basin, Alabama, United States. The model configurations evolved through the model induction toolkit are consistent with the fieldwork investigations and previously reported research findings. Fixed Models Versus Flexible ModelsDesign of conceptual models traditionally begins with a perceptual model derived from insights gained on the basis of fieldwork and experience, proceeding through a mathematical formulation of the hypothesized structure to the numerically robust implementation in a computer code . Conceptual hydrological modeling can be broadly classified into single-and multiple-hypothesis (often referred to as flexible) modeling approaches.Development of models with fixed structure is based on the identification of a general model structure that is physically realistic and applicable to a reasonably wide range of catchments and climatic conditions. Several

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“…Outcomes indicated that the projected amalgam model is reasonable for forecasting monthly precipitation and performs better. Sedighi et al (2016) studied the performance of ANN and SVM models for simulating rainfall-runoff process prejudiced by the height of snow water equivalent (SWE) in Roodak catchment, Tehran region, Iran. Contemplation of SWE improves the performance and accurateness of SVM.…”

Section: Introductionmentioning

confidence: 99%

Prediction of S12-MKII rainfall simulator experimental runoff data sets using hybrid PSR-SVM-FFA approaches

Samantaray

Ghose

2021

Journal of Water and Climate Change

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Effective prediction of runoff is a substantial feature for the successful management of hydrological phenomena in arid regions. The present research findings reveal that a rainfall simulator (RS) can be a valuable instrument to estimate runoff as the intensity of rainfall is modifiable in the course of an experimental process, which turns out to be of great advantage. The rainfall-runoff process is a complex physical phenomenon caused by the effect of various parameters. In this research, a new hybrid technique integrating PSR (phase space reconstruction) with FFA (firefly algorithm) and SVM (support vector machine) has gained recognition in various modelling investigations in contrast to the principle of empirical risk minimization through ANN practices. Outcomes of SVM are contrasted against SVM-FFA and PSR-SVM-FFA models. The improvements in NSE (Nash–Sutcliffe efficiency), RMSE (Root Mean Square Error), and WI (Willmott's Index) by PSR-SVM-FFA over SVM models specify that the prediction accuracy of the hybrid model is better. The established PSR-SVM-FFA model generates preeminent WI values that range from 0.97 to 0.98, while the SVM and SVM-FFA models encompass 0.93–0.95 and 0.96–0.97, respectively. The proposed PSR-SVM-FFA model gives more accurate results and error limiting up to 2–3%.

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Rainfall–Runoff Modeling Using Support Vector Machine in Snow-Affected Watershed

Cited by 28 publications

References 36 publications

A review on the applications of machine learning for runoff modeling

A review on the applications of machine learning for runoff modeling

Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction

Prediction of S12-MKII rainfall simulator experimental runoff data sets using hybrid PSR-SVM-FFA approaches

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