https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2018WR022643

A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for effective reservoir management. In this research, the auto-regressive integrated moving average (ARIMA) model coupled with the ensemble empirical mode decomposition (EEMD) is presented for forecasting annual runoff time series. First, the original annual runoff time series is decomposed into a finite and often small number of intrinsic mode functions (IMFs) and one residual series using EEMD technique for a deep insight into the data characteristics. Then each IMF component and residue is forecasted, respectively, through an appropriate ARIMA model. Finally, the forecasted results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff series. Three annual runoff series from Biuliuhe reservoir, Dahuofang reservoir and Mopanshan reservoir, in China, are investigated using developed model based on the four standard statistical performance evaluation measures (RMSE, MAPE, R and NSEC). The results obtained in this work indicate that EEMD can effectively enhance forecasting accuracy and that the proposed EEMD-ARIMA model can significantly improve ARIMA time series approaches for annual runoff time series forecasting.

Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition

[1] Daily water demand forecasts are an important component of cost-effective and sustainable management and optimization of urban water supply systems. In this study, a method based on coupling discrete wavelet transforms (WA) and artificial neural networks (ANNs) for urban water demand forecasting applications is proposed and tested. Multiple linear regression (MLR), multiple nonlinear regression (MNLR), autoregressive integrated moving average (ARIMA), ANN and WA-ANN models for urban water demand forecasting at lead times of one day for the summer months (May to August) were developed, and their relative performance was compared using the coefficient of determination, root mean square error, relative root mean square error, and efficiency index. The key variables used to develop and validate the models were daily total precipitation, daily maximum temperature, and daily water demand data from 2001 to 2009 in the city of Montreal, Canada. The WA-ANN models were found to provide more accurate urban water demand forecasts than the MLR, MNLR, ARIMA, and ANN models. The results of this study indicate that coupled wavelet-neural network models are a potentially promising new method of urban water demand forecasting that merit further study.

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Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction of the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists. Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods. This survey can be used as a guideline for hydrologists as well as climate scientists in choosing the proper ML method according to the prediction task.

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Flood prediction using machine learning models: Literature review

Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction

https://ira.lib.polyu.edu.hk/bitstream/10397/1314/3/JH8.pdf

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

17 18 Lin Qiu 19 Professor, Institute of environmental & municipal engineering, North China Institute of Water 20 Conservancy and Hydroelectric power, ZhengZhou, 450011, P.R.Ch 21 22 Abstract. Developing a hydrological forecasting model based on past records is crucial to 23

A comparison of performance of several artificial intelligence

Firefly algorithm (FA) is a classical and efficient swarm intelligence optimization method and has a natural capability to address multimodal optimization. However, it suffers from premature convergence and low stability in the solution quality. In this paper, a Yin-Yang firefly algorithm (YYFA) based on dimensionally Cauchy mutation is proposed for performance improvement of FA. An initial position of fireflies is specified by the good nodes set (GNS) strategy to ensure the spatial representativeness of the firefly population. A designed random attraction model is then used in the proposed work to reduce the time complexity of the algorithm. Besides, a key self-learning procedure on the brightest firefly is undertaken to strike a balance between exploration and exploitation. The performance of the proposed algorithm is verified by a set of CEC 2013 benchmark functions used for the single objective real parameter algorithm competition. Experimental results are compared with those of other the state-of-the-art variants of FA. Nonparametric statistical tests on the results demonstrate that YYFA provides highly competitive performance in terms of the tested algorithms. In addition, the application in constrained engineering optimization problems shows the practicability of YYFA algorithm.

Yin-Yang firefly algorithm based on dimensionally Cauchy mutation

Conceptual rainfall–runoff (CRR) model calibration is a global optimization problem with the main objective to find a set of optimal model parameter values that attain a best fit between observed and simulated flow. In this paper, a novel hybrid genetic algorithm (GA), which combines chaos and simulated annealing (SA) method, is proposed to exploit their advantages in a collaborative manner. It takes advantage of the ergodic and stochastic properties of chaotic variables, the global search capability of GA and the local optimal search capability of SA method. First, the single criterion of the mode calibration is employed to compare the performance of the evolutionary process of iteration with GA and chaos genetic algorithm (CGA). Then, the novel method together with fuzzy optimal model (FOM) is investigated for solving the multi-objective Xinanjiang model parameters calibration. Thirty-six historical floods with one-hour routing period for 5 years (2000–2004) in Shuangpai reservoir are employed to calibrate the model parameters whilst 12 floods in two recent years (2005–2006) are utilized to verify these parameters. The performance of the presented algorithm is compared with GA and CGA. The results show that the proposed hybrid algorithm performs better than GA and CGA.

/pdf/calibration-of-xinanjiang-model-parameters-using-hybrid-4k39sxfpbz.pdf

Calibration of Xinanjiang model parameters using hybrid genetic algorithm based fuzzy optimal model

The Xinanjiang model, a conceptual rainfall-runoff (CRR) model with distributed parameters, has been successfully and widely applied to flood forecasting of large basins in humid and semi-humid regions of China. With an increasing demand for timely and accurate forecasts in hydrology, how to obtain more appropriate parameters for CRR models has long been an important topic. These models have a large number of parameters which cannot be directly obtained from measurable quantities of catchments characteristics. In this study, three different optimization methods are used to calibrate the Xinanjiang streamflow model: genetic algorithm (GA), shuffled complex evolution of the University of Arizona (SCE-UA) and the recently developed shuffled complex evolution Metropolis algorithm of the University of Arizona (SCEM-UA), using streamflow data of the Shuangpai Reservoir in China. Two different time steps of 1 and 3 hr are used in the analysis. The results indicate that the SCEM-UA algorithm can infer the most probable parameter set and furnish useful information about the nature of the response surface in the vicinity of the optimum. Moreover, there is larger uncertainty for 1 hr forecasting than for 3 hr forecasting. This is significant in assessing risks in likely applications of Xinanjiang models.

Wenchuan Wang

Papers

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

A comparison of performance of several artificial intelligence

Yin-Yang firefly algorithm based on dimensionally Cauchy mutation

Calibration of Xinanjiang model parameters using hybrid genetic algorithm based fuzzy optimal model

Comparison of three global optimization algorithms for calibration of the Xinanjiang model parameters.