scholarly journals Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods

2021 ◽  
Vol 13 (1) ◽  
pp. 401-415
Author(s):  
Jie Wang ◽  
Guoqing Wang ◽  
Amgad Elmahdi ◽  
Zhenxin Bao ◽  
Qinli Yang ◽  
...  
Keyword(s):  
Water Resource Management ◽  
Ensemble Methods ◽  
Ensemble Method ◽  
Water Volume ◽  
Hydrological Process ◽  
Efficiency Coefficient ◽  
Hydrologic Models ◽  
Runoff Forecasting ◽  
Artificial Neural Network Ann ◽  
Xin’Anjiang Model

Abstract Ensemble hydrologic forecasting which takes advantages of multiple hydrologic models has made much contribution to water resource management. In this study, four hydrological models (the Xin’anjiang model (XAJ), Simhyd, GR4J, and artificial neural network (ANN) models) and three ensemble methods (the simple average, black box-based, and binomial-based methods) were applied and compared to simulate the hydrological process during 1979–1983 in three representative catchments (Daixi, Hengtangcun, and Qiaodongcun). The results indicate that for a single model, the XAJ model and the GR4J model performed relatively well with averaged Nash and Sutcliffe efficiency coefficient (NSE) values of 0.78 and 0.83, respectively. For the ensemble models, the results show that the binomial-based ensemble method (dynamic weight) outperformed with water volume error reduced by 0.8% and NSE value increased by 0.218. The best performance on runoff forecasting occurs in the Hengtang catchment by integrating four hydrologic models based on binomial ensemble method, achieving the water volume error of 2.73% and NSE value of 0.923. Finding would provide scientific support to water engineering design and water resources management in the study areas.

scholarly journals Transferability of the Xin'anjiang model based on similarity in climate and geography

2021 ◽  
Author(s):  
Yue Liu ◽  
Jian-yun Zhang ◽  
Amgad Elmahdi ◽  
Qin-li Yang ◽  
Xiao-xiang Guan ◽  
...  
Keyword(s):  
Eastern China ◽  
Hydrologic Model ◽  
Hydrological Modelling ◽  
Model Parameters ◽  
Hydrological Process ◽  
Hydrologic Models ◽  
Ungauged Catchments ◽  
Model Based ◽  
Hydrological Cycles ◽  
Xin’Anjiang Model

Abstract Hydrological experiments are essential to understand the hydrological cycles and promoting the development of hydrologic models. Model parameter transfers provide a new way of doing hydrological forecasts and simulations in ungauged catchments. To study the transferability of model parameters for hydrological modelling and the influence of parameter transfers on hydrological simulations, the Xin'anjiang model (XAJ model), which is a lumped hydrologic model based on a saturation excess mechanism, and has been widely applied in different climate regions of the world, was applied to a low hilly catchment in eastern China, the Chengxi Experimental Watershed (CXEW). The suitability of the XAJ model was tested in the eastern branch catchment of CXEW and the calibrated model parameters of the eastern branch catchment were then transferred to the western branch catchment and the entire watershed of the CXEW. The results show that the XAJ model performs well for the calibrated eastern branch catchment at both daily and monthly scales on hydrological modelling with the NSEs over 0.6 and the REs less than 2.0%. Besides, the uncalibrated catchments of the western branch catchment and the entire watershed of the CSEW share similarities in climate (the precipitation) and geography (the soil texture and vegetation cover) with the calibrated catchment, the XAJmodel and the transferred model parameters can capture the main features of the hydrological processes in both uncalibrated catchments (western catchments and entire watershed). This transferability of the model is useful for a scarce data region to simulate the hydrological process and its forecasting.

Estimating Heterogeneous Treatment Effects and the Effects of Heterogeneous Treatments with Ensemble Methods

2017 ◽  
Vol 25 (4) ◽  
pp. 413-434 ◽  
Author(s):  
Justin Grimmer ◽  
Solomon Messing ◽  
Sean J. Westwood
Keyword(s):  
Treatment Effects ◽  
Ensemble Methods ◽  
Ensemble Method ◽  
Superior Performance ◽  
Accurate Estimation ◽  
Randomized Experiments ◽  
Heterogeneous Treatment Effects ◽  
Data Set ◽  
Heterogeneous Effects ◽  
The Ensemble Method

Randomized experiments are increasingly used to study political phenomena because they can credibly estimate the average effect of a treatment on a population of interest. But political scientists are often interested in how effects vary across subpopulations—heterogeneous treatment effects—and how differences in the content of the treatment affects responses—the response to heterogeneous treatments. Several new methods have been introduced to estimate heterogeneous effects, but it is difficult to know if a method will perform well for a particular data set. Rather than using only one method, we show how an ensemble of methods—weighted averages of estimates from individual models increasingly used in machine learning—accurately measure heterogeneous effects. Building on a large literature on ensemble methods, we show how the weighting of methods can contribute to accurate estimation of heterogeneous treatment effects and demonstrate how pooling models lead to superior performance to individual methods across diverse problems. We apply the ensemble method to two experiments, illuminating how the ensemble method for heterogeneous treatment effects facilitates exploratory analysis of treatment effects.

scholarly journals FBSEM: A Novel Feature-Based Stacked Ensemble Method for Sentiment Analysis

2020 ◽  
Vol 12 (6) ◽  
pp. 11-22
Author(s):  
Yasin Görmez ◽  
◽  
Yunus E. Işık ◽  
Mustafa Temiz ◽  
Zafer Aydın
Keyword(s):  
Sentiment Analysis ◽  
Cross Validation ◽  
Emotional State ◽  
Ensemble Methods ◽  
Extraction Methods ◽  
Error Rates ◽  
Ensemble Method ◽  
Learning Tasks ◽  
Feature Based ◽  
The Individual

Sentiment analysis is the process of determining the attitude or the emotional state of a text automatically. Many algorithms are proposed for this task including ensemble methods, which have the potential to decrease error rates of the individual base learners considerably. In many machine learning tasks and especially in sentiment analysis, extracting informative features is as important as developing sophisticated classifiers. In this study, a stacked ensemble method is proposed for sentiment analysis, which systematically combines six feature extraction methods and three classifiers. The proposed method obtains cross-validation accuracies of 89.6%, 90.7% and 67.2% on large movie, Turkish movie and SemEval-2017 datasets, respectively, outperforming the other classifiers. The accuracy improvements are shown to be statistically significant at the 99% confidence level by performing a Z-test.

Predictably Predictable -  The Role of Catchment Characteristics and Complexity.

2021 ◽  
Author(s):  
Sophia Eugeni ◽  
Eric Vaags ◽  
Steven V. Weijs
Keyword(s):  
Water Resource Management ◽  
Performance Metrics ◽  
Complexity Analysis ◽  
Model Performance ◽  
Principal Component ◽  
The United States ◽  
Hydrologic Model ◽  
Hydrologic Models ◽  
Hydrologic Modelling

<p>Accurate hydrologic modelling is critical to effective water resource management. As catchment attributes strongly influence the hydrologic behaviors in an area, they can be used to inform hydrologic models to better predict the discharge in a basin. Some basins may be more difficult to accurately predict than others. The difficulty in predicting discharge may also be related to the complexity of the discharge signal. The study establishes the relationship between a catchment’s static attributes and hydrologic model performance in those catchments, and also investigates the link to complexity, which we quantify with measures of compressibility based in information theory. </p><p>The project analyzes a large national dataset, comprised of catchment attributes for basins across the United States, paired with established performance metrics for corresponding hydrologic models. Principal Component Analysis (PCA) was completed on the catchment attributes data to determine the strongest modes in the input. The basins were clustered according to their catchment attributes and the performance within the clusters was compared. </p><p>Significant differences in model performance emerged between the clusters of basins. For the complexity analysis, details of the implementation and technical challenges will be discussed, as well as preliminary results.</p>

scholarly journals Hydrological Modelling and Water Resources Assessment of Chongwe River Catchment using WEAP Model

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 839 ◽  
Author(s):  
Tewodros M. Tena ◽  
Phenny Mwaanga ◽  
Alick Nguvulu
Keyword(s):  
Water Resources ◽  
Water Resource Management ◽  
Measurement Data ◽  
Annual Precipitation ◽  
Mean Annual Precipitation ◽  
Efficiency Coefficient ◽  
Management Options ◽  
Weap Model ◽  
River Catchment ◽  
Monthly Streamflow

The Chongwe River Catchment (CRC) is located in Zambia. It receives a mean annual precipitation of 889 mm. The catchment is facing growing anthropogenic and socio-economic activities leading to severe water shortages in recent years, particularly from July to October. The objective of this study was to assess the available water resources by investigating the important hydrological components and estimating the catchment water balance using the Water Evaluation and Planning (WEAP) model. The average precipitation over a 52 year period and a 34 year period of streamflow measurement data for four stations were used in the hydrological balance model. The results revealed that the catchment received an estimated mean annual precipitation of 4603.12 Mm3. It also released an estimated mean annual runoff and evapotranspiration of 321.94 Mm3 and 4063.69 Mm3, respectively. The estimated mean annual total abstractions in the catchment was 119.87 Mm3. The average annual change in the catchment storage was 120.18 Mm3. The study also determined an external inflow of 22.55 Mm3 from the Kafue River catchment. The simulated mean monthly streamflow at the outlet of the CRC was 10.32 m3/s. The estimated minimum and maximum streamflow volume of the Chongwe River was about 1.01 Mm3 in September and 79.7 Mm3 in February, respectively. The performance of the WEAP model simulation was assessed statistically using the coefficient of determination (R2 = 0.97) and the Nash–Sutcliffe model efficiency coefficient (NSE = 0.64). The R2 and NSE values indicated a satisfactory model fit and result. Meeting the water demand of the growing population and associated socio-economic development activities in the CRC is possible but requires appropriate water resource management options.

scholarly journals Qom—A New Hydrologic Prediction Model Enhanced with Multi-Objective Optimization

2019 ◽  
Vol 10 (1) ◽  
pp. 251 ◽  
Author(s):  
Gustavo R. Zavala ◽  
José García-Nieto ◽  
Antonio J. Nebro
Keyword(s):  
River Basins ◽  
Hydrologic Model ◽  
Optimization Approach ◽  
Prediction Errors ◽  
Efficiency Coefficient ◽  
Multi Objective Optimization ◽  
Hydrologic Models ◽  
Multi Objective ◽  
Decision Variables ◽  
Hydrologic Prediction

The efficient calibration of hydrologic models allows experts to evaluate past events in river basins, as well as to describe new scenarios and predict possible future floodings. A difficulty in this context is the need to adjust a large number of parameters in the model to reduce prediction errors. In this work, we address this issue with two complementary contributions. First, we propose a new lumped rainfall-runoff hydrologic model—called Qom—which is featured by a limited set of continuous decision variables associated with soil moisture and direct runoff. Qom allows to separate and quantify the volume of losses and excesses of the rainwater falling in a hydrographic basin, while a Clark’s model is used to determine output hydrograms. Second, we apply a multi-objective optimization approach to find accurate calibrations of the model in a systematic and automatic way. The idea is to formulate the process as a bi-objective optimization problem where the Nash-Sutcliffe Efficiency coefficient and percent bias have to be minimized, and to combine the results found by a set of metaheuristics used to solve it. For validation purposes, we apply our proposal in six hydrographic scenarios, comprising river basins located in Spain, USA, Brazil and Argentina. The proposed approach is shown to minimize prediction errors of simulated streamflows with regards to those observed in these real-world basins.

Water Resource Management Practices of Food Establishments in Siquijor Province

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Steven J. Sumaylo
Keyword(s):  
Resource Management ◽  
Water Flow ◽  
Water Resource ◽  
Water Resource Management ◽  
Management Practices ◽  
Water Volume ◽  
Human Beings ◽  
Efficient System ◽  
Life On Earth ◽  
Descriptive Survey

Water is one of the basic needs of human beings and is imperative for sustaining the quality of life on earth (Brooks, 2006).  However, its unbalanced and unmanaged use makes its scarce. Hence, this study assessed the water resource management practices of the 53 food establishments in Siquijor Province. The study utilized the descriptive survey using a self- made questionnaire which yielded that majority of the respondents are females and are in the age brackets of 18-40 and 41-62. A greater majority of the establishments were in the operation for more than ten years. As to respondents’ water resource management practices, they Often do not let water flow while cleaning or rinsing, check the water supply system for leaks and turn off unnecessary flows, and adjust water flow by the type of cleaning to be undertaken, but they Never install self-closing faucets. However, reading water meter regularly, washing only full loads in the dishwasher, and installing low flush toilets were sometimes done while installing automatic water volume controls, reusing the rinse water from the dishwasher and installing flow regulators on the faucet heads were seldom practiced. The respondents’ sex, age and number of years in operating the business had no significant relationships to their extent of water resource management practices. Thus, it is concluded that better management coupled with effective policy, awareness and efficient system is vital to enhance water resource management.

Measuring and modelling high resolution rainfall fields for hydrologic process understanding

2020 ◽  
Author(s):  
Lionel Benoit ◽  
Anthony Michelon ◽  
Bettina Schaefli ◽  
Grégoire Mariéthoz
Keyword(s):  
High Resolution ◽  
Urban Areas ◽  
Low Cost ◽  
Special Focus ◽  
Hydrological Process ◽  
Rainfall Runoff ◽  
Hydrologic Models ◽  
Process Understanding ◽  
Rainfall Model ◽  
Set Up

<p>Observing and modelling rainfall at high spatial and temporal resolution is known to be key for hydrologic applications in urban areas, but little is known about the relevance of high density observations in natural headwater catchments. In this contribution, we present the case of the Vallon de Nant experimental catchment (Switzerland) where high resolution rainfall observations have been carried out with low cost (drop-counting) sensors to develop a new sub-kilometer scale stochastic rainfall model and to investigate the relevance of high resolution rainfall observations to understand the rainfall-runoff response of a small alpine headwater catchment (13.4 km²).</p><p>We will give an overview over the experimental set-up (in place for two consecutive summers), the reliability of the used sensors (Driptych Pluvimate) and the potential of such a network to inform high resolution stochastic rainfall field models and hydrologic models. A special focus will be on the developed methodological framework to assess the importance of high resolution observations for hydrological process research. Given the relatively low cost of the deployed rainfall sensors (around 600 USD each), the presented methods are readily transferable to similar hydrologic settings, in natural as well as urban areas.</p>

scholarly journals Runoff forecasting model based on variational mode decomposition and artificial neural networks

2021 ◽  
Vol 19 (2) ◽  
pp. 1633-1648
Author(s):  
Xin Jing ◽  
◽  
Jungang Luo ◽  
Shangyao Zhang ◽  
Na Wei
Keyword(s):  
Neural Networks ◽  
Water Resource Management ◽  
Short Term Memory ◽  
Forecasting Model ◽  
Variational Mode Decomposition ◽  
Model Based ◽  
Runoff Forecasting ◽  
Mode Decomposition ◽  
Runoff Series ◽  
Correlation Information

<abstract> <p>Accurate runoff forecasting plays a vital role in water resource management. Therefore, various forecasting models have been proposed in the literature. Among them, the decomposition-based models have proved their superiority in runoff series forecasting. However, most of the models simulate each decomposition sub-signals separately without considering the potential correlation information. A neoteric hybrid runoff forecasting model based on variational mode decomposition (VMD), convolution neural networks (CNN), and long short-term memory (LSTM) called VMD-CNN-LSTM, is proposed to improve the runoff forecasting performance further. The two-dimensional matrix containing both the time delay and correlation information among sub-signals decomposing by VMD is firstly applied to the CNN. The feature of the input matrix is then extracted by CNN and delivered to LSTM with more potential information. The experiment performed on monthly runoff data investigated from Huaxian and Xianyang hydrological stations at Wei River, China, demonstrates the VMD-superiority of CNN-LSTM to the baseline models, and robustness and stability of the forecasting of the VMD-CNN-LSTM for different leading times.</p> </abstract>

scholarly journals Application of the decomposition prediction reconstruction framework to middle and long-term runoff forecasting

2020 ◽  
Author(s):  
Yi Ji ◽  
Hong-Tao Dong ◽  
Zhen-Xiang Xing ◽  
Ming-xin Sun ◽  
Qiang Fu ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Annual Runoff ◽  
Dry Season ◽  
Validation Dataset ◽  
Support Vector ◽  
Efficiency Coefficient ◽  
Flood Season ◽  
Runoff Forecasting ◽  
Forecasting Performance

Abstract Middle and long-term runoff forecasting has always been a problem, especially in flood seasons. The forecasting performance can be improved using complementary ensemble empirical mode decomposition (CEEMD) to produce clearer signals as model inputs. In the forecasting models based on CEEMD, the entire time series is decomposed into several sub-series, each sub-series is divided into training and validation dataset, and forecasted by some common models, such as least-squares support vector machine (LSSVM), and finally an ensemble forecasting result is obtained by summing the forecasted results of each sub-series. This model is applied to forecast the inflow runoff of theShitouxia Reservoir (STX Reservoir). The forecasting results show that the Nash efficiency coefficient of the LSSVM model is 0.815, and the Nash efficiency coefficient of the CEEMD-LSSVM model is 0.954, an increase of 13.9%. The root mean square error value is reduced from 20.654 to 10.235, a decrease of 50.4%.The runoff forecasting performance can be improved effectively by applying the CEEMD-LSSVM model.When analyzing the annual runoff forecasting results month by month, it was found that the forecasting results from November to April of the following year were unsatisfactory compared with the nearest neighbor bootstrapping regressive (NNBR) model which more suitable in dry season, but the forecasting results from May to October improved significantly. This also proves that the CEEMD-LSSVM model has a great advantage in the forecasting of inflow runoff during the flood season. In the optimized operation of reservoirs, the forecasting result of inflow runoff in flood season is more important than in dry season. Therefore, when forecasting annual runoff month by month, it is recommended to adopt the CEEMD-LSSVM model in the flood season and the NNBR model in the dry season, that is, the combination of the two models is applied to the forecasting of the inflow runoff of the STX Reservoir.

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