A novel medium- and long-term runoff combined forecasting model based on different lag periods

Ai, Ping; Song, Yanhong; Xiong, Chuansheng; Chen, Binbin; Yue, Zhaoxin

doi:10.2166/hydro.2022.116

Cited by 12 publications

(4 citation statements)

References 24 publications

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“…The article correctly showed the results for runoff, which suggests the possible structure of the water balance of the entire catchment area (Moosavi et al, 2022) and for the verification of hydrometeorological data (Młyński et al, 2020). In other studies, more effort should have been focused on the selection of factors for the input data (Ai et. al., 2022), and the reliability of the results.…”

Section: Surface Water Runoff From the Catchment Area (Water Losses)mentioning

confidence: 57%

The Rainfall-Runoff Model for the Carpathian Flysch Catchment Using the Swat+ Hydrological Software

Halecki¹,

Bedla²

2022

ASP.FC

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Aim of the study: The goal of this study was to apply SWAT+ tools to estimate the water shortage in the Smugawka catchment. Material and methods: The use of the area was documented in the form of an orthophotomap with GIS layers superimposed, which was provided by CORINE Land Cover 2018. A soil and agricultural map, supplemented with lithological content from a detailed geological map, was used as the input layer for QGIS ver. 3.16. In the next stage, multiannual meteorological data (1991–2020) were added to the SWAT+ model, which included: minimum and maximum daily air temperature, daily rainfall, average daily radiation and average daily wind speed. Full integration of the SWAT+ model with QGIS facilitates the procedure of iterative generation and modification of the model input parameters. The calculation of continuous simulations for average long periods of time is presented in the manuscript. Results and conclusions: The application of GIS-based approaches and modeling techniques are crucial in the quest for optimal methods for calculating the water balance today. Our findings also contribute to a better understanding of the hydrological processes at work in the flysch catchment.

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Section: Surface Water Runoff From the Catchment Area (Water Losses)mentioning

confidence: 57%

The Rainfall-Runoff Model for the Carpathian Flysch Catchment Using the Swat+ Hydrological Software

Halecki¹,

Bedla²

2022

ASP.FC

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“…The back propagation neural network (BPNN) algorithm [19] is one of many artificial neural network algorithms. The BPNN's learning process consists of forward and backward propagation.…”

Section: B Model Updating Methods 1) Machine Learning Algorithmmentioning

confidence: 99%

“…At this time, the improvement of the algorithm and the integration with other methods become the focus. Such as combining traditional hydrological models like the Xin'an Jiang model [5] or improving based on an intelligent algorithm like Particle Swarm Optimization (PSO) [6].…”

Section: Introductionmentioning

confidence: 99%

Effect of Data Characteristics Inconsistency on Medium and Long-Term Runoff Forecasting by Machine Learning

Xiong

et al. 2023

IEEE Access

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In the application of medium and long-term runoff forecasting, machine learning has some problems, such as high learning cost, limited computing cost, and difficulty in satisfying statistical data assumptions in some regions, leading to difficulty in popularization in the hydrology industry. In the case of a few data, it is one of the ways to solve the problem to analyze the data characteristics consistency. This paper analyzes the statistical hypothesis of machine learning and runoff data characteristics such as periodicity and mutation. Aiming at the effect of data characteristics inconsistency on three representative machine learning models (multiple linear regression, random forest, back propagation neural network), a simple correction/improvement method suitable for engineering was proposed. The model results were verified in the Danjiangkou area, China. The results show that the errors of the three models have the same distribution as the periodic characteristics of the runoff periods, and the correction/improvement based on periodicity and mutation characteristics can improve the forecasting accuracy of the three models. The back propagation neural network model is most sensitive to the data characteristics consistency.INDEX TERMS Danjiangkou reservoir, data characteristics consistency, machine learning, medium and long-term runoff forecasting, mutation, characteristics, periodicity characteristics.

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“…In addition, many teleconnection climate factors influencing runoff variation [9,10] have been considered as alternative candidate predictive factors, including the East Asian Trough Intensity Index, ENSO Modoki Index, and other indexes. The approaches used in key factor selection are principally the prior knowledge method [11], correlation coefficient method [12,13], principal component analysis (PCA) [14,15], mutual information (MI) [16], and partial mutual information (PMI) [17]. Among the various factor-selection methods, the prior knowledge method relies mainly on artificial experience, which is subjective and has certain limitations.…”

Section: Introductionmentioning

confidence: 99%

Monthly Runoff Forecasting Using Particle Swarm Optimization Coupled with Flower Pollination Algorithm-Based Deep Belief Networks: A Case Study in the Yalong River Basin

Yue

Liu²,

Zhou

2023

Water

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Accuracy in monthly runoff forecasting is of great significance in the full utilization of flood and drought control and of water resources. Data-driven models have been proposed to improve monthly runoff forecasting in recent years. To effectively promote the prediction effect of monthly runoff, a novel hybrid data-driven model using particle swarm optimization coupled with flower pollination algorithm-based deep belief networks (PSO-FPA-DBNs) was proposed, which selected the optimal network depth via PSO and searched for the optimum hyper parameters (the number of neurons in the hidden layer and the learning rate of the RBMs) in the DBN using FPA. The methodology was divided into three steps: (i) the Comprehensive Basin Response (COM) was constructed and calculated to characterize the hydrological state of the basin, (ii) the information entropy algorithm was adopted to select the key factors, and (iii) the novel model was proposed for monthly runoff forecasting. We systematically compared the PSO-FPA-DBN model with the traditional prediction models (i.e., the backpropagation neural network (BPNN), support vector machines (SVM), deep belief networks (DBN)), and other improved models (DBN-PLSR, PSO-GA-DBN, and PSO-ACO-DBN) for monthly runoff forecasting by using an original dataset. Experimental results demonstrated that our PSO-FPA-DBN model outperformed the peer models, with a mean absolute percentage error (MAPE) of 18.23%, root mean squared error (RMSE) of 230.45 m3/s, coefficient of determination (DC) of 0.9389, and qualified rate (QR) of 64.2% for the data from the Yalong River Basin. Also, the stability of our PSO-FPA-DBN model was evaluated. The proposed model might adapt effectively to the nonlinear characteristics of monthly runoff forecasting; therefore, it could obtain accurate and reliable runoff forecasting results.

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A novel medium- and long-term runoff combined forecasting model based on different lag periods

Cited by 12 publications

References 24 publications

The Rainfall-Runoff Model for the Carpathian Flysch Catchment Using the Swat+ Hydrological Software

The Rainfall-Runoff Model for the Carpathian Flysch Catchment Using the Swat+ Hydrological Software

Effect of Data Characteristics Inconsistency on Medium and Long-Term Runoff Forecasting by Machine Learning

Monthly Runoff Forecasting Using Particle Swarm Optimization Coupled with Flower Pollination Algorithm-Based Deep Belief Networks: A Case Study in the Yalong River Basin

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