Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions

Maier, Holger R.; Jain, Ashu; Dandy, Graeme C.; Sudheer, K. P.

doi:10.1016/j.envsoft.2010.02.003

Cited by 755 publications

(496 citation statements)

References 220 publications

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“…20(3), p. 74~82, 2015 대수층의 비산출률을 이용하여 지하수 함양량을 산정하는 기법으로 최근 국내에서 방법론에 대한 고찰 및 적용 연 구가 빈번히 이루어지고 있다 (Koo and Lee, 2002;Choi et al, 2007;Yun et al, 2009;Koo et al, 2013) (Box and Jenkins, 1976;Tankersley, 1993;Yi and Lee, 2004). 이후 입·출력 변 수 간의 비선형성을 고려할 수 있는 기계학습기법 (Machine learning)을 활용하여 수자원 분야 예측 모델 개발 및 적용 연구가 진행되어 왔다 (Maier et al, 2010). (1) …”

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A Method to Filter Out the Effect of River Stage Fluctuations using Time Series Model for Forecasting Groundwater Level and its Application to Groundwater Recharge Estimation

Yoon¹,

Park²,

Kim³

et al. 2015

Journal of Soil and Groundwater Environment

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A method to filter out the effect of river stage fluctuations on groundwater level was designed using an artificial neural network-based time series model of groundwater level prediction. The designed method was applied to daily groundwater level data near the Gangjeong-Koryeong Barrage in the Nakdong river. Direct prediction time series models were successfully developed for both cases of before and after the barrage construction using past measurement data of rainfall, river stage, and groundwater level as inputs. The correlation coefficient values between observed and predicted data were over 0.97. Using the time series models the effect of river stage on groundwater level data was filtered out by setting a constant value for river stage inputs. The filtered data were applied to the hybrid water table fluctuation method in order to estimate the groundwater recharge. The calculated ratios of groundwater recharge to precipitation before and after the barrage construction were 11.0% and 4.3%, respectively. It is expected that the proposed method can be a useful tool for groundwater level prediction and recharge estimation in the riverside area.

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A Method to Filter Out the Effect of River Stage Fluctuations using Time Series Model for Forecasting Groundwater Level and its Application to Groundwater Recharge Estimation

Yoon¹,

Park²,

Kim³

et al. 2015

Journal of Soil and Groundwater Environment

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“…At present, researches on water resources in the world mainly concentrated on water quality analysis (Zhang et al 2014), water resource management (Umapathi et al 2013;Miao et al 2014), water resources use (Maier et al 2010), and the influence impacted by climate change with wide-ranging consequences for human societies and ecosystems (Abutaleb 2000;Arnell 2004;Barnett et al 2004;Higashi 2006), while, there is few study about water resources value outside of China. Water resources value refers to the money paid for water resource owners (nation or collective) by the users in order to obtain the right of using the water.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of water resources conserved by forests in the Hani rice terraces system of Honghe County, Yunnan, China: an application of the fuzzy comprehensive evaluation model

Min

et al. 2016

J. Mt. Sci.

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Abstract:The Hani Rice Terraces System, based on gravity-flow irrigation, is one of the Globally Important Agricultural Heritage Systems (GIAHS) pilot sites selected by FAO in 2010. The water resource plays an important role in the sustainable development of this system. The value of water conserved by the forest is influenced by natural, economic and social factors. In this paper, the water quality, per capita water resources, per capita GDP and population density are chosen as indices to construct an index system for a comprehensive evaluation of water resources value. The weights of these indices are 0.443, 0.31, 0.141 and 0.106 respectively, which are determined by the analytic hierarchy process (AHP) method. The water resources value has been assessed by the fuzzy comprehensive evaluation model. The results show that the water resources value in the Hani Rice Terraces System is 4.25 RMB/m 3 . Evaluating the value of water resources in the Hani Rice Terraces System can provide a reference for ecological compensation, for raising public awareness of the importance of protecting the system, and ultimately achieving its sustainable development.

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“…RAI and MATHUR [2008], RAJAEE [2009], MAIER [2010], MELESSE et al [2011], CHANG [2012], RAJAEE et al [2011], MUSTAFA et al [2012]. ANN techniques have been applied successfully for estimating and forecasting daily suspended sediments, modelling rivers sediment yields, estimation of reservoirs sedimentation, and prediction of sediment concentration in rivers MUSTAFA et al [2011].…”

Section: Introductionmentioning

confidence: 99%

Using artificial neural network (ANN) for prediction of sediment loads, application to the Mellah catchment, northeast Algeria

Bouzeria¹,

Ghenim²,

Khanchoul³

2017

Journal of Water and Land Development

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In this study, we present the performances of the best training algorithm in Multilayer Perceptron (MLP) neural networks for prediction of suspended sediment discharges in Mellah catchment. Time series data of daily suspended sediment discharge and water discharge from the gauging station of Bouchegouf were used for training and testing the networks. A number of statistical parameters, i.e. root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and coefficient of determination (R2) were used for performance evaluation of the model. The model produced satisfactory results and showed a very good agreement between the predicted and observed data. The results also showed that the performance of the MLP model was capable to capture the exact pattern of the sediment discharge data in the Mellah catchment.

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Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions

Cited by 755 publications

References 220 publications

A Method to Filter Out the Effect of River Stage Fluctuations using Time Series Model for Forecasting Groundwater Level and its Application to Groundwater Recharge Estimation

A Method to Filter Out the Effect of River Stage Fluctuations using Time Series Model for Forecasting Groundwater Level and its Application to Groundwater Recharge Estimation

Evaluation of water resources conserved by forests in the Hani rice terraces system of Honghe County, Yunnan, China: an application of the fuzzy comprehensive evaluation model

Using artificial neural network (ANN) for prediction of sediment loads, application to the Mellah catchment, northeast Algeria

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