Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (9)

Search Parameters:
Keywords = medium to long-term runoff prediction

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
18 pages, 14586 KiB  
Article
Research on Annual Runoff Prediction Model Based on Adaptive Particle Swarm Optimization–Long Short-Term Memory with Coupled Variational Mode Decomposition and Spectral Clustering Reconstruction
by Xueni Wang, Jianbo Chang, Hua Jin, Zhongfeng Zhao, Xueping Zhu and Wenjun Cai
Water 2024, 16(8), 1179; https://doi.org/10.3390/w16081179 - 20 Apr 2024
Cited by 1 | Viewed by 2016
Abstract
Accurate medium- and long-term runoff prediction models play crucial guiding roles in regional water resources planning and management. However, due to the significant variation in and limited amount of annual runoff sequence samples, it is difficult for the conventional machine learning models to [...] Read more.
Accurate medium- and long-term runoff prediction models play crucial guiding roles in regional water resources planning and management. However, due to the significant variation in and limited amount of annual runoff sequence samples, it is difficult for the conventional machine learning models to capture its features, resulting in inadequate prediction accuracy. In response to the difficulties in leveraging the advantages of machine learning models and limited prediction accuracy in annual runoff forecasting, firstly, the variational mode decomposition (VMD) method is adopted to decompose the annual runoff series into multiple intrinsic mode function (IMF) components and residual sequences, and the spectral clustering (SC) algorithm is applied to classify and reconstruct each IMF. Secondly, an annual runoff prediction model based on the adaptive particle swarm optimization–long short-term memory network (APSO-LSTM) model is constructed. Finally, with the basis of the APSO-LSTM model, the decomposed and clustered IMFs are predicted separately, and the predicted results are integrated to obtain the ultimate annual runoff forecast results. By decomposing and clustering the annual runoff series, the non-stationarity and complexity of the series have been reduced effectively, and the endpoint effect of modal decomposition has been effectively suppressed. Ultimately, the expected improvement in the prediction accuracy of the annual runoff series based on machine learning models is achieved. Four hydrological stations along the upper reaches of the Fen River in Shanxi Province, China, are studied utilizing the method proposed in this paper, and the results are compared with those obtained from other methods. The results show that the method proposed in this article is significantly superior to other methods. Compared with the APSO-LSTM model and the APSO-LSTM model based on processed annual runoff sequences by single VMD or Wavelet Packet Decomposition (WPD), the method proposed in this paper reduces the RMSE by 40.95–80.28%, 25.26–57.04%, and 15.49–40.14%, and the MAE by 24.46–80.53%, 16.50–59.30%, and 16.58–41.80%, in annual runoff prediction, respectively. The research has important reference significance for annual runoff prediction and hydrological prediction in areas with data scarcity. Full article
Show Figures

Figure 1

22 pages, 11815 KiB  
Article
Comparisons of Different Machine Learning-Based Rainfall–Runoff Simulations under Changing Environments
by Chenliang Li, Ying Jiao, Guangyuan Kan, Xiaodi Fu, Fuxin Chai, Haijun Yu and Ke Liang
Water 2024, 16(2), 302; https://doi.org/10.3390/w16020302 - 16 Jan 2024
Cited by 5 | Viewed by 2234
Abstract
Climate change and human activities have a great impact on the environment and have challenged the assumption of the stability of the hydrological time series and the consistency of the observed data. In order to investigate the applicability of machine learning (ML)-based rainfall–runoff [...] Read more.
Climate change and human activities have a great impact on the environment and have challenged the assumption of the stability of the hydrological time series and the consistency of the observed data. In order to investigate the applicability of machine learning (ML)-based rainfall–runoff (RR) simulation methods under a changing environment scenario, several ML-based RR simulation models implemented in novel continuous and non-real-time correction manners were constructed. The proposed models incorporated categorical boosting (CatBoost), a multi-hidden-layer BP neural network (MBP), and a long short-term memory neural network (LSTM) as the input–output simulators. This study focused on the Dongwan catchment of the Yiluo River Basin to carry out daily RR simulations for the purpose of verifying the model’s applicability. Model performances were evaluated based on statistical indicators such as the deterministic coefficient, peak flow error, and runoff depth error. The research findings indicated that (1) ML-based RR simulation by using a consistency-disrupted dataset exhibited significant bias. During the validation phase for the three models, the R2 index decreased to around 0.6, and the peak flow error increased to over 20%. (2) Identifying data consistency transition points through data analysis and conducting staged RR simulations before and after the transition point can improve simulation accuracy. The R2 values for all three models during both the baseline and change periods were above 0.85, with peak flow and runoff depth errors of less than 20%. Among them, the CatBoost model demonstrated superior phased simulation accuracy and smoother simulation processes and closely matched the measured runoff processes across high, medium, and low water levels, with daily runoff simulation results surpassing those of the BP neural network and LSTM models. (3) When simulating the entire dataset without staged treatment, it is impossible to achieve good simulation results by adopting uniform extraction of the training samples. Under this scenario, the MBP exhibited the strongest generalization capability, highest prediction accuracy, better algorithm stability, and superior simulation accuracy compared to the CatBoost and LSTM simulators. This study offers new ideas and methods for enhancing the runoff simulation capabilities of machine learning models in changing environments. Full article
Show Figures

Figure 1

20 pages, 5171 KiB  
Article
A Multi-Factor Combination Model for Medium to Long-Term Runoff Prediction Based on Improved BP Neural Network
by Kun Yan, Shang Gao, Jinhua Wen and Shuiping Yao
Water 2023, 15(20), 3559; https://doi.org/10.3390/w15203559 - 12 Oct 2023
Cited by 2 | Viewed by 1671
Abstract
Taking a certain coastal area of Jiangsu province as the research background, this study scientifically predicts the runoff on the medium and long-term time scale according to the changes of various climate factors such as atmospheric circulation, sea surface temperature, and solar activity [...] Read more.
Taking a certain coastal area of Jiangsu province as the research background, this study scientifically predicts the runoff on the medium and long-term time scale according to the changes of various climate factors such as atmospheric circulation, sea surface temperature, and solar activity in the first half of the year. A lag correlation is established between various related climate factors and the monthly runoff process in the research area for the previous 1–6 months. Selecting advantageous factors and constructing a significant factor set. Using the improved BP (Back-Propagation) artificial neural network model and combining it with the sensitivity analysis method, a specific number of 8-factor combinations are selected from the set of significant factors for medium and long-term runoff prediction. After that, the prediction results are compared with the forecasting effects of two multi-factor combination runoff simulation schemes formed by stepwise regression and Spearman rank correlation methods. The study concluded that the multi-factor combination simulation effect formed through sensitivity analysis was the best. The 20% standard forecast qualification rate of the three schemes is not significantly different. The Mean Absolute Relative Error of the multi-factor combination training and validation periods simulated through sensitivity analysis is the smallest among the three schemes, which are 36.61% and 38.01%, respectively. The Nash Efficiency Coefficient in the validation period is 0.45, which is far better than other schemes and has better generalization ability. The Standard Deviation of Relative Error in the training and validation periods is much smaller than other schemes, and the dispersion of relative errors is the smallest. Full article
(This article belongs to the Special Issue New Challenges in Rainfall Erosion)
Show Figures

Figure 1

19 pages, 5990 KiB  
Article
A Hybrid Forecasting Model to Simulate the Runoff of the Upper Heihe River
by Huazhu Xue, Hui Wu, Guotao Dong and Jianjun Gao
Sustainability 2023, 15(10), 7819; https://doi.org/10.3390/su15107819 - 10 May 2023
Cited by 3 | Viewed by 1931
Abstract
River runoff simulation and prediction are important for controlling the water volume and ensuring the optimal allocation of water resources in river basins. However, the instability of medium- and long-term runoff series increases the difficulty of runoff forecasting work. In order to improve [...] Read more.
River runoff simulation and prediction are important for controlling the water volume and ensuring the optimal allocation of water resources in river basins. However, the instability of medium- and long-term runoff series increases the difficulty of runoff forecasting work. In order to improve the prediction accuracy, this research establishes a hybrid deep learning model framework based on variational mode decomposition (VMD), the mutual information method (MI), and a long short-term memory network (LSTM), namely, VMD-LSTM. First, the original runoff data are decomposed into a number of intrinsic mode functions (IMFs) using VMD. Then, for each IMF, a long short-term memory (LSTM) network is applied to establish the prediction model, and the MI method is used to determine the data input lag time. Finally, the prediction results of each subsequence are reconstructed to obtain the final forecast result. We explored the predictive performance of the model with regard to monthly runoff in the upper Heihe River Basin, China, and compared its performance with other single and hybrid models. The results show that the proposed model has obvious advantages in terms of the performance of point prediction and interval prediction compared to several comparative models. The Nash–Sutcliffe efficiency coefficient (NSE) of the prediction results reached 0.96, and the coverage of the interval prediction reached 0.967 and 0.908 at 95% and 90% confidence intervals, respectively. Therefore, the proposed model is feasible for simulating the monthly runoff of this watershed. Full article
Show Figures

Figure 1

13 pages, 4543 KiB  
Article
Runoff Forecast for the Flood Season Based on Physical Factors and Their Effect Process and Its Application in the Second Songhua River Basin, China
by Yangzong Cidan, Hongyan Li, Yunqing Xuan, Hong Sun and Fang You
Sustainability 2022, 14(17), 10627; https://doi.org/10.3390/su141710627 - 26 Aug 2022
Cited by 4 | Viewed by 1911
Abstract
The Second Songhua River Basin is located at the northern edge of the East Asian monsoon region in China. The river basin has a large interannual rainfall-runoff variation often associated with frequent droughts and floods. Therefore, the mid-long-term runoff prediction is of great [...] Read more.
The Second Songhua River Basin is located at the northern edge of the East Asian monsoon region in China. The river basin has a large interannual rainfall-runoff variation often associated with frequent droughts and floods. Therefore, the mid-long-term runoff prediction is of great significance. According to a review of the national and international literature, there are few studies on sunspots in the prediction of medium- and long-term runoff. In this study, sunspots are selected as the influencing factors of runoff based on the mechanism of astronomical factors; sensitivity analysis was used to identify the time delay of sunspots’ influence on runoff and determine the prediction factor (relative number of sunspots in January and March). The BP (backpropagation) network is used to identify the correlation between prediction factors and prediction items (monthly average inflow rate of the Fengman Reservoir and the Baishan Reservoir in the flood season), and then the prediction model is constructed. According to the test results of historical data and the actual forecast results, the forecast is working well, and the accuracy of qualitative forecasting is high. Full article
Show Figures

Figure 1

24 pages, 2306 KiB  
Article
Effects of Climate Conditions on TP Outsourcing in Lake Kinneret (Israel)
by Moshe Gophen
Climate 2021, 9(9), 142; https://doi.org/10.3390/cli9090142 - 16 Sep 2021
Cited by 2 | Viewed by 2884
Abstract
Since the mid-1980s, significant changes in climate conditions have occurred, and trends of dryness in the Kinneret drainage basin have been documented, including a temperature increase and precipitation decline. The precipitation decline, and consequently the reduction in river discharge, resulted in a decrease [...] Read more.
Since the mid-1980s, significant changes in climate conditions have occurred, and trends of dryness in the Kinneret drainage basin have been documented, including a temperature increase and precipitation decline. The precipitation decline, and consequently the reduction in river discharge, resulted in a decrease in TP (total phosphorus) flux into Lake Kinneret. After the drainage of the Hula natural wetlands and old Lake Hula during the 1950s, the ecological characteristics of the Hula Valley were modified. Nutrient fluxes downstream into Lake Kinneret were therefore predicted. The impacts of climate conditions (precipitation and discharge) on TP (total phosphorus) outsourcing through erosive action are significant: higher and lower discharge enhances and reduces TP load, respectively. The total TP flushing range from the Hula Valley peat soil through the subterranean medium and where TP is directed are not precisely known but are probably outside Lake Kinneret. Most runoff water and mediated TP originates from bedrock through erosive action. Long-term records of TP concentrations in headwaters and potential resources in the Hula Valley confirmed the significant influence of climate conditions on the outsourcing of TP capacity. The impacts of agricultural development, external fertilizer loads and migratory cranes in the winter are probably insignificant. Full article
Show Figures

Figure 1

18 pages, 1998 KiB  
Article
Runoff Prediction Method Based on Adaptive Elman Neural Network
by Chenming Li, Lei Zhu, Zhiyao He, Hongmin Gao, Yao Yang, Dan Yao and Xiaoyu Qu
Water 2019, 11(6), 1113; https://doi.org/10.3390/w11061113 - 28 May 2019
Cited by 37 | Viewed by 4266
Abstract
The prediction of medium- and long-term runoff is of great significance to the comprehensive utilization of water resources. Building an adaptive data-driven runoff prediction model by automatic identification of multivariate time series change in runoff forecasting and identifying its influence degree is an [...] Read more.
The prediction of medium- and long-term runoff is of great significance to the comprehensive utilization of water resources. Building an adaptive data-driven runoff prediction model by automatic identification of multivariate time series change in runoff forecasting and identifying its influence degree is an attractive and intricate task. At present, the commonly used screening factor method is correlational analysis; others offer multi-collinearity. If these factors are directly input into the model, the parameters of the model tend to increase, and the excessive redundancy and noise adversely affects the prediction results of the model. On the basis of previous studies on medium- and long-term runoff prediction methods, this paper proposes an Elman Neural Network (ENN) adaptive runoff prediction method based on normalized mutual information (NMI) and kernel principal component analysis (KPCA). In this method, the features of the screening factors are extracted automatically by using the mutual information automatic screening factor, and then input into the Elman Neural Network for training. With less features, the parameters of the Elman Neural Network model can be reduced, and the problem of overfitting of the Elman Neural Network model is effectively alleviated. The method is evaluated by using the annual average runoff data of Jinping hydropower station in Chengdu, China, from 2007 to 2011. The maximum relative error of multiple forecasts was found to be less than 16%, and forecast effect was good. The accuracy of prediction is further improved by averaging the results of multiple forecasts. Full article
(This article belongs to the Special Issue Techniques for Mapping and Assessing Surface Runoff)
Show Figures

Figure 1

19 pages, 3633 KiB  
Article
Rainfall-Runoff Modelling Considerations to Predict Streamflow Characteristics in Ungauged Catchments and under Climate Change
by Francis H.S. Chiew, Hongxing Zheng and Nicholas J. Potter
Water 2018, 10(10), 1319; https://doi.org/10.3390/w10101319 - 24 Sep 2018
Cited by 17 | Viewed by 5566
Abstract
This paper investigates the prediction of different streamflow characteristics in ungauged catchments and under climate change, with three rainfall-runoff models calibrated against three different objective criteria, using a large data set from 780 catchments across Australia. The results indicate that medium and high [...] Read more.
This paper investigates the prediction of different streamflow characteristics in ungauged catchments and under climate change, with three rainfall-runoff models calibrated against three different objective criteria, using a large data set from 780 catchments across Australia. The results indicate that medium and high flows are relatively easier to predict, suggesting that using a single unique set of parameter values from model calibration against an objective criterion like the Nash–Sutcliffe efficiency is generally adequate and desirable to provide a consistent simulation and interpretation of daily streamflow series and the different medium and high flow characteristics. However, the low flow characteristics are considerably more difficult to predict and will require careful modelling consideration to specifically target the low flow characteristic of interest. The modelling results also show that different rainfall-runoff models and different calibration approaches can give significantly different predictions of climate change impact on streamflow characteristics, particularly for characteristics beyond the long-term averages. Predicting the hydrological impact from climate change, therefore, requires careful modelling consideration and calibration against appropriate objective criteria that specifically target the streamflow characteristic that is being assessed. Full article
Show Figures

Figure 1

30 pages, 3842 KiB  
Article
Estimating the Exposure of Coral Reefs and Seagrass Meadows to Land-Sourced Contaminants in River Flood Plumes of the Great Barrier Reef: Validating a Simple Satellite Risk Framework with Environmental Data
by Caroline Petus, Michelle Devlin, Angus Thompson, Len McKenzie, Eduardo Teixeira da Silva, Catherine Collier, Dieter Tracey and Katherine Martin
Remote Sens. 2016, 8(3), 210; https://doi.org/10.3390/rs8030210 - 5 Mar 2016
Cited by 34 | Viewed by 11677
Abstract
River runoff and associated flood plumes (hereafter river plumes) are a major source of land-sourced contaminants to the marine environment, and are a significant threat to coastal and marine ecosystems worldwide. Remote sensing monitoring products have been developed to map the spatial extent, [...] Read more.
River runoff and associated flood plumes (hereafter river plumes) are a major source of land-sourced contaminants to the marine environment, and are a significant threat to coastal and marine ecosystems worldwide. Remote sensing monitoring products have been developed to map the spatial extent, composition and frequency of occurrence of river plumes in the Great Barrier Reef (GBR), Australia. There is, however, a need to incorporate these monitoring products into Risk Assessment Frameworks as management decision tools. A simple Satellite Risk Framework has been recently proposed to generate maps of potential risk to seagrass and coral reef ecosystems in the GBR focusing on the Austral tropical wet season. This framework was based on a “magnitude × likelihood” risk management approach and GBR plume water types mapped from satellite imagery. The GBR plume water types (so called “Primary” for the inshore plume waters, “Secondary” for the midshelf-plume waters and “Tertiary” for the offshore plume waters) represent distinct concentrations and combinations of land-sourced and marine contaminants. The current study aimed to test and refine the methods of the Satellite Risk Framework. It compared predicted pollutant concentrations in plume water types (multi-annual average from 2005–2014) to published ecological thresholds, and combined this information with similarly long-term measures of seagrass and coral ecosystem health. The Satellite Risk Framework and newly-introduced multi-annual risk scores were successful in demonstrating where water conditions were, on average, correlated to adverse biological responses. Seagrass meadow abundance (multi-annual change in % cover) was negatively correlated to the multi-annual risk score at the site level (R2 = 0.47, p < 0.05). Relationships between multi-annual risk scores and multi-annual changes in proportional macroalgae cover (as an index for coral reef health) were more complex (R2 = 0.04, p > 0.05), though reefs incurring higher risk scores showed relatively higher proportional macroalgae cover. Multi-annual risk score thresholds associated with loss of seagrass cover were defined, with lower risk scores (≤0.2) associated with a gain or little loss in seagrass cover (gain/−12%), medium risk scores (0.2–0.4) associated with moderate loss (−12/−30%) and higher risk scores (>0.4) with the greatest loss in cover (>−30%). These thresholds were used to generate an intermediate river plume risk map specifically for seagrass meadows of the GBR. An intermediate river plume risk map for coral reefs was also developed by considering a multi-annual risk score threshold of 0.2—above which a higher proportion of macroalgae within the algal communities can be expected. These findings contribute to a long-term and adaptive approach to set relevant risk framework and thresholds for adverse biological responses in the GBR. The ecological thresholds and risk scores used in this study will be refined and validated through ongoing monitoring and assessment. As uncertainties are reduced, these risk metrics will provide important information for the development of strategies to manage water quality and ecosystem health. Full article
(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)
Show Figures

Graphical abstract

Back to TopTop