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.     

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...     

Improving Forecasting Accuracy of Streamflow Time Series Using Least Squares Support Vector Machine Coupled with Data-Preprocessing Techniques

Highly reliable forecasting of streamflow is essential in many water resources planning and management activities. Recently, least squares support vector machine (LSSVM) method has gained much attention in streamflow forecasting due to its ability to model complex non-linear relationships. However, LSSVM method belongs to black-box models, that is, this method is primarily based on measured data. In this paper, we attempt to improve the performance of LSSVM method from the aspect of data preprocessing by singular spectrum analysis (SSA) and discrete wavelet analysis (DWA). Kharjeguil and Ponel stations from Northern Iran are investigated with monthly streamflow data. The root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R) and coefficient of efficiency (CE) statistics are used as comparing criteria. The results indicate that both SSA and DWA can significantly improve the performance of forecasting model. However, DWA seems to be superior to SSA and able to estimate peak streamflow values more accurately. Thus, it can be recommended that LSSVM method coupled with DWA is more promising.     

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...     

基于改进GWO和SVM的大坝变形预测

建立准确可靠的大坝变形预测模型是大坝安全评价的重要内容,为此,将差分进化算法的交叉和变异算子引入灰狼优化算法(GWO),提出一种基于改进灰狼算法(MGWO)优化支持向量机(SVM)的大坝变形预测方法.通过差分进化算法丰富初始种群,提出改进灰狼优化算法(MGWO),并采用MGWO算法优化SVM的惩罚因子和核函数,建立基于...     

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...     

Monthly Rainfall Forecasting Using Echo State Networks Coupled with Data Preprocessing Methods

In this paper, two novel methods, echo state networks (ESN) and multi-gene genetic programming (MGGP), are proposed for forecasting monthly rainfall. Support vector regression (SVR) was taken as a reference to compare with these methods. To improve the accuracy of predictions, data preprocessing methods were adopted to decompose the raw rainfall data into subseries. Here, wavelet transform (WT), singular spectrum analysis (SSA) and ensemble empirical mode decomposition (EEMD) were applied as data preprocessing methods, and the performances of these methods were compared. Predictive performance of the models was evaluated based on multiple criteria. The results indicate that ESN is the most favorable method among the three evaluated, which makes it a promising alternative method for forecasting monthly rainfall. Although the performances of MGGP and SVR are less favorable, they are nevertheless good forecasting methods. Furthermore, in most cases, MGGP is inferior to SVR in monthly rainfall forecasting. WT and SSA are both favorable data preprocessing methods. WT is preferable for short-term forecasting, whereas SSA is excellent for long-term forecasting. However, EEMD tends to show inferior performance in monthly rainfall forecasting.     

基于灰狼优化算法优化支持向量机的变压器故障诊断方法

为了能够解决传统人工经验方法准确率低、智能算法中存在的适用性不强等问题,针对支持向量机受到惩罚系数和核函数的敏感性,提出一种变压器诊断模型,该模型利用三对比值作为特征输入,利用灰狼优化算法优化支持向量机的惩罚系数和核函数,并利用优化后的参数模型去对变压器进行故障诊断.实验结果表明,支持向量机模型预测的准确率为90％,而...     

Forecasting Daily Streamflow for Basins with Different Physical Characteristics through Data-Driven Methods

Modelling streamflow is essential for activities, such as flood control, drought mitigation, and water resources utilization and management. Artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machines (SVM) are techniques that are frequently used in hydrology to specifically model streamflow. This study compares the accuracy of ANN, ANFIS, and SVM in forecasting the daily streamflow with the traditional approach known as autoregressive (AR) model for basins with different physical characteristics. The accuracies of the models are compared for three basins, that is, 1801, 1805, and 1822, at the Seyhan River Basin in Turkey. The comparison was performed by using coefficient of efficiency, index of agreement, and root-mean-square error. Results indicate that ANN and ANFIS are more accurate than AR and SVM for all the basins. ANN and ANFIS perform similarly, while ANN outperformed ANFIS. Among the models used, the ANN demonstrates the highest performance in forecasting the peak flood values. This study also finds that physical characteristics, such as small area, high slope, and high elevation variation, and streamflow variance deteriorate the accuracy of the methods.     

Evaluating the Performance of CHIRPS Satellite Rainfall Data for Streamflow Forecasting

Water Resources Management - Streamflow forecasting can offer valuable information for optimal management of water resources, flood mitigation, and drought warning. This research aims in evaluating...     

Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions,Northwest China

Prediction of groundwater depth (GWD) is a critical task in water resources management. In this study, the practicability of predicting GWD for lead times of 1, 2 and 3 months for 3 observation wells in the Ejina Basin using the wavelet-artificial neural network (WA-ANN) and wavelet-support vector regression (WA-SVR) is demonstrated. Discrete wavelet transform was applied to decompose groundwater depth and meteorological inputs into approximations and detail with predictive features embedded in high frequency and low frequency. WA-ANN and WA-SVR relative of ANN and SVR were evaluated with coefficient of correlation (R), Nash-Sutcliffe efficiency (NS), mean absolute error (MAE), and root mean squared error (RMSE). Results showed that WA-ANN and WA-SVR have better performance than ANN and SVR models. WA-SVR yielded better results than WA-ANN model for 1, 2 and 3-month lead times. The study indicates that WA-SVR could be applied for groundwater forecasting under ecological water conveyance conditions.     

Prediction of Water-Level in the Urmia Lake Using the Extreme Learning Machine Approach

Predicting the dynamics of water-level in lakes plays a vital role in navigation, water resources planning and catchment management. In this paper, the Extreme Learning Machine (ELM) approach was used to predict the daily water-level in the Urmia Lake. Daily water-level data from the Urmia Lake in northwest of Iran were used to train, test and validate the employed models. Results showed that the ELM approach can accurately forecast the water-level in the Urmia Lake. Outcomes from the ELM model were also compared with those of genetic programming (GP) and artificial neural networks (ANNs). It was found that the ELM technique outperforms GP and ANN in predicting water-level in the Urmia Lake. It also can learn the relation between the water-level and its influential variables much faster than the GP and ANN. Overall, the results show that the ELM approach can be used to predict dynamics of water-level in lakes.     

Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States

Water Resources Management - Streamflow estimation plays a significant role in water resources management, especially for flood mitigation, drought warning, and reservoir operation. Hence, the...     

Application of ANN-Based Streamflow Forecasting Model for Agricultural Water Management in the Awash River Basin,Ethiopia

This paper presents the application of a long-term streamflow forecasting model developed using artificial neural networks at a stream gauging station in the Awash River Basin, Ethiopia. The gauging station is located above the headworks of a large irrigation scheme called the Middle Awash Agricultural Development Enterprise (MAADE). Based on the forecasted streamflow time series and water requirements for irrigation and environmental purposes, appropriate agricultural water management strategies have been proposed for the irrigation scheme (MAADE). The water management strategies which were evaluated in this study are based on different scenarios of abstraction demands. These demands were formulated based on a range of options for agricultural development and change in MAADE. The scenarios evaluated were based on such factors as the existing planting patterns, changing planting dates, changing crop varieties and reducing the area under cultivation. An appropriate scenario of agricultural development was decided on the basis of the modified flows in the river vis-à-vis the trigger/threshold value established at the Melka Sedi stream gauging station. Considering all the scenarios, it is suggested that a 1–24% reduction in the area currently irrigated in the scheme will ensure a reliable supply of water to the scheme throughout the growing season and will provide sustainable environmental flow in the river.     

利用ABCD模型预测流域水文对极端气候的响应

ABCD模型只有4个参数,以降水量和潜在蒸散量为输入,可以同时模拟蒸散、径流以及土壤水、地下水储量的变化,适用于进行月尺度或年尺度的流域模拟。把ABCD模型运用到美国内布拉斯加州的北Loup河流域,该流域有超过60 a的气象和水文数据。经过对初值和参数进行敏感性分析,确定合理的模型参数和土壤水、地下水储量取值区间,发现模型初值误差只影响10 a以内的径流量模拟结果。利用对初值不敏感时期的实测径流量优化识别了模型参数,在此基础上再利用前10 a数据优化识别了模型的初值。模拟得到的地下水储量变化特征与前人根据地下水位变化推算的结果基本一致,径流量模拟的Nash效率系数为0.68,验证了ABCD模型对北Loup河流域的有效性。然后,对流域气象数据进行统计分析,确定了50 a一遇的极端干旱和极端湿润气候条件,采用ABCD模型预测了持续极端气候情景下的流域水文状态变化情况。结果表明:土壤水储量对极端气候的响应很快,在气候变化的第2年基本达到稳定,对流域径流几乎没有调节作用;地下水储量对极端气候响应较慢,衰减或增加50%所需要的时间均超过10 a,从而降低了流域径流对极端气候的敏感性。     

A Novel Approach for Prediction of Monthly Ground Water Level Using a Hybrid Wavelet and Non-Tuned Self-Adaptive Machine Learning Model

Water Resources Management - In recent decades, due to groundwater withdrawal in the Kabodarahang region, Iran, Hamadan, hazardous events such as sinkholes, droughts, water scarcity, etc., have...     

黄河上游月尺度径流量预报集成与决策方法探讨

根据预报信息来源不同,采用不同的预报模型进行计算,将不同预报模型的计算结果进行预报集成和预报决策,从而获得定量的月尺度径流量预报值.检验结果表明,预报集成和预报决策提高了预报的稳定性,预报精度令人满意.     

A Comparative Assessment of Metaheuristic Optimized Extreme Learning Machine and Deep Neural Network in Multi-Step-Ahead Long-term Rainfall Prediction for All-Indian Regions

Prediction of long-term rainfall patterns is a highly challenging task in the hydrological field due to random nature of rainfall events. The contribution of monthly rainfall is important in agriculture and hydrological tasks. This paper proposes two data-driven models, namely biogeography-based extreme learning machine (BBO-ELM) and deep neural network (DNN), to predict one, two, and three month-ahead rainfall over India (All-India and six other homogeneous regions). Three other data-driven models called ELM, genetic algorithm (GA)-based ELM, and particle swarm optimization (PSO)-based ELM are used to compare the performance of the proposed models. Firstly, partial autocorrelation function (PACF) is applied in all datasets to select the optimal number of lags for input to the models. Secondly, the wavelet-based data pre-processing technique is applied in selected optimal lags and feed to the proposed models for achieving higher prediction performance. To investigate the performance of proposed models, a non-parametric statistical test, Anderson–Darling’ Normality test, is performed in all India dataset. The wavelet-based proposed hybrid models show better prediction capability compared to optimal lag-based proposed models. This study shows the successful application of time-series data using proposed techniques (optimal lags-based BBO-ELM and wavelet-based DNN) in the hydrological field which may be used for risk mitigation from dreadful natural events.

     

Assessment of Effective Monitoring Sites in a Reservoir Watershed by Support Vector Machine Coupled with Multi-Objective Genetic Algorithm for Sediment Flux Prediction during Typhoons

Water Resources Management - Effectively assessing crucial monitoring sites with suspended sediment concentration (SSC) is a vital challenge for achieving accurate prediction of sediment flux on...