Multi-Step-Ahead Monthly Streamflow Forecasting Using Convolutional Neural Networks

Water Resources Management - Many hydrological applications related to water resource planning and management primarily rely on a succession of streamflow forecasts with extensive lead times. In...     

Monthly Streamflow Forecasting Using ELM-IPSO Based on Phase Space Reconstruction

We have developed a hybrid model that integrates chaos theory and an extreme learning machine with optimal parameters selected using an improved particle swarm optimization (ELM-IPSO) for monthly runoff analysis and prediction. Monthly streamflow data covering a period of 55 years from Daiying hydrological station in the Chaohe River basin in northern China were used for the study. The Lyapunov exponent, the correlation dimension method, and the nonlinear prediction method were used to characterize the streamflow data. With the time series of the reconstructed phase space matrix as input variables, an improved particle swarm optimization was used to improve the performance of the extreme learning machine. Finally, the optimal chaotic ensemble learning model for monthly streamflow prediction was obtained. The accuracy of the predictions of the streamflow series (linear correlation coefficient of about 0.89 and efficiency coefficient of about 0.78) indicate the validity of our approach for predicting streamflow dynamics. The developed method had a higher prediction accuracy compared with an auto-regression method, an artificial neural network, an extreme learning machine with genetic algorithm and with PSO algorithm, suggesting that ELM-IPSO is an efficient method for monthly streamflow prediction.

     

Real-Time Water-Level Forecasting Using Dilated Causal Convolutional Neural Networks

Accurate forecasts of hourly water levels during typhoons are crucial to disaster emergency response. To mitigate flood damage, the development of a water-level forecasting model has played an essential role. We propose a model based on a dilated causal convolutional neural network (DCCNN) that can yield water-level forecasts with lead times of 1- to 6-h. A DCCNN model can efficiently exploit a broad-range history. Residual and skip connections are also applied throughout the network to enable training of deeper networks and to accelerate convergence. To demonstrate the superiority of the proposed forecasting technique, we applied it to a dataset of 16 typhoon events that occurred during the years 2012–2017 in the Yilan River basin in Taiwan. In order to examine the efficiency of the improved methodology, we also compared the proposed model with two existing models that were based on the multilayer perceptron (MLP) and the support vector machine (SVM). The results indicate that a DCCNN-based model is superior to both the SVM and MLP models, especially for modeling peak water levels. Much of the performance improvement of the proposed model is due to its ability to provide water-level forecasts with a long lead time. The proposed model is expected to be particularly useful in support of disaster warning systems.

     

Monthly Rainfall Prediction Using Wavelet Neural Network Analysis

Rainfall is one of the most significant parameters in a hydrological model. Several models have been developed to analyze and predict the rainfall forecast. In recent years, wavelet techniques have been widely applied to various water resources research because of their time-frequency representation. In this paper an attempt has been made to find an alternative method for rainfall prediction by combining the wavelet technique with Artificial Neural Network (ANN). The wavelet and ANN models have been applied to monthly rainfall data of Darjeeling rain gauge station. The calibration and validation performance of the models is evaluated with appropriate statistical methods. The results of monthly rainfall series modeling indicate that the performances of wavelet neural network models are more effective than the ANN models.     

A Comparative Assessment of Artificial Neural Network,Generalized Regression Neural Network,Least-Square Support Vector Regression,and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions

Monthly forecasting of streamflow is of particular importance in water resources management especially in the provision of rule curves for dams. In this paper, the performance of four data-driven models with different structures including Artificial Neural Network (ANN), Generalized Regression Neural Network (GRNN), Least Square-Support Vector Regression (LS-SVR), and K-Nearest Neighbor Regression (KNN) are evaluated in order to forecast monthly inflow to Karkheh dam, Iran, in linear and non-linear conditions while the optimized values of the model parameters are determined in the same condition via the Leave-One-Out Cross Validation (LOOCV) method. Results show that the performance of the models is different in linear and nonlinear conditions; the cumulative ranking of the models according to the three assessment criteria including NSE, RMSE and R² indicates that ANN performs best in linear conditions while LS-SVR, GRNN and KNN are in the next ranks, respectively. But in nonlinear conditions, the best performance belongs to LS-SVR, followed by KNN, ANN, and GRNN models.     

A Hybrid Wavelet and Neuro-Fuzzy Model for Forecasting the Monthly Streamflow Data

Researchers have studied to forecast the streamflow in order to develop the water usage policy. They have used not only traditional methods, but also computer aided methods. Some black-box models, like Adaptive Neuro Fuzzy Inference Systems (ANFIS), became very popular for the hydrologic engineering, because of their rapidity and less variation requirements. Wavelet Transform has become a useful tool for the analysis of the variations in time series. In this study, a hybrid model, Wavelet-Neuro Fuzzy (WNF), has been used to forecast the streamflow data of 5 Flow Observation Stations (FOS), which belong to Sakarya Basin in Turkey. In order to evaluate the accuracy performance of the model, Auto Regressive Integrated Moving Average (ARIMA) model has been used with the same data sets. The comparison has been made by Root Mean Squared Errors (RMSE) of the models. Results showed that hybrid WNF model forecasts the streamflow more accurately than ARIMA model.     

Short-Term Streamflow Forecasting Using the Feature-Enhanced Regression Model

Reservoir inflow forecasting is extremely important for the management of a reservoir. In practice, accurate forecasting depends on the feature learning performance. To better address this issue, this paper proposed a feature-enhanced regression model (FER), which combined stack autoencoder (SAE) with long short-term memory (LSTM). This model had two constituents: (1) The SAE was constructed to learn a representation as close as possible to the original inputs. Through deep learning, the enhanced feature could be captured sufficiently. (2) The LSTM was established to simulate the mapping between the enhanced features and the outputs. Under recursive modeling, the patterns of correlation in the short term and dependence in the long term were considered comprehensively. To estimate the performance of the FER model, two historical daily discharge series were investigated, i.e., the Yangtze River in China and the Sava Dolinka River in Slovenia. The proposed model was compared with other machine-learning methods (i.e., the LSTM, SAE-based neural network, and traditional neural network). The results demonstrated that the proposed FER model yields the best forecasting performance in terms of six evaluation criteria. The proposed model integrates the deep learning and recursive modeling, and thus being beneficial to exploring complex features in the reservoir inflow forecasting. Moreover, for smaller catchments with significant torrential characteristics, more data are needed (e.g., at least 20 years) to effectively train the model and to obtain accurate flood-forecasting results.

     

Intermittent Streamflow Forecasting by Using Several Data Driven Techniques

Forecasting intermittent streamflows is an important issue for water quality management, water supplies, hydropower and irrigation systems. This paper compares the accuracy of several data driven techniques, that is, adaptive neuro fuzzy inference system (ANFIS), artificial neural networks (ANNs) and support vector machine (SVM) for forecasting daily intermittent streamflows. The results are also compared with those of the local linear regression (LLR) and the dynamic local linear regression (DLLR). Intermittent streamflow data from two stations, Uzunkopru and Babaeski, in Thrace region located in north-western Turkey are used in the study. The root mean square error and correlation coefficient were used as comparison criteria. The comparison results indicated that the ANFIS, ANN and SVM models performed better than the LLR and DLLR models in forecasting daily intermittent streamflows. The ANN and ANFIS gave the best forecasts for the Uzunkopru and Babaeski stations, respectively.     

Long Term Streamflow Forecasting Using a Hybrid Entropy Model

In this paper, the development and evaluation of an entropy based hybrid data driven model coupled with input selection approach and wavelet transformation is investigated for long-term streamflow forecasting with 10 years lead time. To develop and test the models, data including 45 years of monthly streamflow time series from Taleghan basin, located in northwest of Tehran, are employed. For this purpose, first the performance of a maximum entropy forecasting model is evaluated. To boost the accuracy, an auto-correlation method with %95 confidence levels was carried out to determine the optimum order of the entropy model. Nevertheless, the basic entropy model, as expected, was only able to reach Nash-Sutcliffe efficiency (NSE) index of 0.35 during the test period. On the other hand, data driven models such as artificial neural networks (ANN) have shown to yield good accuracy in modeling complicated and nonlinear systems. Thus, to improve the performance of the maximum entropy model, an entropy-based hybrid model using evolutionary ANN (ENN) was proposed for further investigation. The proposed model with seasonality index substantially improved the test NSE to 0.51 and provided more accurate results than the basic entropy model. Moreover, when wavelet transform was applied to preprocess the input data, the model shows a slight improvement (NSE?=?0.54).     

A Hybrid Model to Predict Monthly Streamflow Using Neighboring Rivers Annual Flows

Water Resources Management - The issue of predicting monthly streamflow data is an important issue in water resources engineering. In this paper, a hybrid model was proposed to generate monthly...     

Middle- and Long-Term Streamflow Forecasting and Uncertainty Analysis Using Lasso-DBN-Bootstrap Model

Middle-term and long-term streamflow forecasting is of great significance for water resources planning and management, cascade reservoirs optimal operation, agriculture and hydro-power generation. In this work, a framework was proposed which integrates least absolute shrinkage and selection operator (lasso), DBN and bootstrap to improve the performance and the stability of streamflow forecasting with the lead-time of one month. Lasso helps to screen the appropriate predictors for the DBN model, and the DBN model simulates the complex relationship between the selection predictors and streamflow, and then bootstrap with the DBN model contributes to evaluate the uncertainty. The Three-River Headwaters Region (TRHR) was taken as a case study. The results indicated that lasso-DBN-bootstrap model produced significantly more accurate forecasting results than the other three models and provides reliable information on the forecasting uncertainty, which will be valuable for water resources management and planning.

     

Scale Effects of the Monthly Streamflow Prediction Using a State-of-the-art Deep Learning Model

Water Resources Management - The accurate prediction of monthly streamflow is important in sustainable water resources planning and management. There is a growing interest in the development of...     

Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach

Forecasting the ground water level fluctuations is an important requirement for planning conjunctive use in any basin. This paper reports a research study that investigates the potential of artificial neural network technique in forecasting the groundwater level fluctuations in an unconfined coastal aquifer in India. The most appropriate set of input variables to the model are selected through a combination of domain knowledge and statistical analysis of the available data series. Several ANN models are developed that forecasts the water level of two observation wells. The results suggest that the model predictions are reasonably accurate as evaluated by various statistical indices. An input sensitivity analysis suggested that exclusion of antecedent values of the water level time series may not help the model to capture the recharge time for the aquifer and may result in poorer performance of the models. In general, the results suggest that the ANN models are able to forecast the water levels up to 4 months in advance reasonably well. Such forecasts may be useful in conjunctive use planning of groundwater and surface water in the coastal areas that help maintain the natural water table gradient to protect seawater intrusion or water logging condition.     

Monthly Rainfall Forecasting Using EEMD-SVR Based on Phase-Space Reconstruction

Rainfall links atmospheric and surficial processes and is one of the most important hydrologic variables. We apply support vector regression (SVR), which has a high generalization capability, to construct a rainfall forecasting model. Before construction of the model, a self-adaptive data analysis methodology called ensemble empirical mode decomposition (EEMD) is used to preprocess a rainfall data series. In addition, the phase-space reconstruction method is implemented to design input vectors for the forecasting model. The proposed hybrid model is applied to forecast the monthly rainfall at a weather station in Changchun, China as a case study. To demonstrate the capacity of the proposed hybrid model, a typical three-layer feed-forward artificial neural network model, an auto-regressive integrated moving average model, and a support vector regression model are constructed. Predictive performance of the models is evaluated based on normalized mean squared error (NMSE), mean absolute percent error (MAPE), Nash–Sutcliffe efficiency (NSE), and the coefficient of correlation (CC). Results indicate that the proposed hybrid model has the lowest NMSE and MAPE values of 0.10 and 14.90, respectively, and the highest NSE and CC values of 0.91 and 0.83, respectively, during the validation period. We conclude that the proposed hybrid model is feasible for monthly rainfall forecast and is better than the models currently in common use.     

Intermittent Streamflow Forecasting and Extreme Event Modelling using Wavelet based Artificial Neural Networks

Forecasting of intermittent stream flow is necessary for water resource planning and management at catchment scale. Forecasting of extreme events and events outside the range of training data used for artificial neural network (ANN) model development has been a major bottleneck in their generalization capabilities till date. Despite of several studies using wavelet analysis in water resource modelling, no study has yet been conducted to explore capabilities of hybrid ANN modelling techniques for extreme events outside the training range. In this study a wavelet based ANN model (WANN) is proposed for intermittent streamflow forecasting and extreme event modelling. This study is carried out in a watershed in semi arid middle region of Gujarat, India. 6 years of hydro-climatic data are used in this study. 4 years of data are used for model training, 1 year for cross-validation and remaining 1 year data are used to evaluate the effectiveness of the WANN model. Two different approaches of data arrangement are considered in this study, in one approach testing data are within the range of training dataset, whereas in another approach testing data are outside the training dataset range. Performance of four different training algorithms and different types of wavelet functions are also evaluated for WANN model development. In this study it is found that WANN model performed significantly better than standard ANN models. It is observed in this study that different wavelet functions have different role in modelling complexities of normal and extreme events. WANN model simulated peak values very well and it shows that WANN model has the potential to be applied successfully for intermittent streamflow forecasting even for the data outside the training range and for extreme events.     

Monthly Runoff Forecasting Using Variational Mode Decomposition Coupled with Gray Wolf Optimizer-Based Long Short-term Memory Neural Networks

Water Resources Management - Accurate and reliable monthly runoff forecasting plays an important role in making full use of water resources. In recent years, long short-term memory neural networks...     

Binary Grey Wolf Optimization-Regularized Extreme Learning Machine Wrapper Coupled with the Boruta Algorithm for Monthly Streamflow Forecasting

Input variable selection plays a key role in data-driven streamflow forecasting models. In this study, we propose a two-stage wrapper model to drive one-month-ahead streamflow forecasting in the context of high-dimensional candidate input variables. Initially, the Boruta algorithm, a feature selection method, was applied to select all the relevant input variables for the streamflow series. Then, a novel binary grey wolf optimizer (BGWO)-regularized extreme learning machine (RELM) wrapper was derived. We carried out experiments on two US catchments with 132 candidate input variables, including local meteorological information, global climatic indices, and lags of the streamflow series. Furthermore, the sensitivities of the proposed model in terms of the optimal objective function were compared. The results indicate two important findings. First, the proposed model outperformed commonly used models in terms of four error evaluation criteria. Second, for the proposed model, the root mean square error is a more suitable criterion than the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for the optimal objective function. These findings are of great reference value for developing ELM models for streamflow forecasting.

     

Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons

Water Resources Management - Streamflow forecasting is paramount process in water and flood management, determination of river water flow potentials, environmental flow analysis, agricultural...     

基于BP神经网络的来水量预测模型

针对径流量长期变化的因果关系复杂特性,常规的中长期水文预报模型又很难满足精度要求,提出了基于BP神经网络的来水量预测模型。结合实际径流数据,验证了模型的预报精度,可用来进行中长期水文预报。     

影响水文神经网络预报精度的因素探讨

在运用神经网络来进行水文预报过程中,采用不同的参数,对预报效果是有影响的.通过对不同参数组合进行计算,来分析不同系列组合、训练系列长度、数据归一化等对神经网络预报效果的影响.研究发现,不同数据系列组合的预报效果有很大的不同,训练系列长度对预报精度是有影响的,训练数据与预报数据之间存在时间、空间的间隔对预报精度的影响是不确定的,输入数据的归一化处理对预报精度的影响与输入数据的分布区间存在一定关系.