Adaptive Neuro-fuzzy Inference System on Downstream Water Level Forecasting

Wang, An-Pei; Liao, Heng-Yi; Chang, Te-Hsing

doi:10.1109/fskd.2008.671

Cited by 9 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both errors are measured in the same units as the variable under study (in our case, hm 3 ) and have been widely used for model evaluation [25][26][27][28][29]. While MAE gives the same weight to all errors, RMSE penalizes variance as it gives errors with larger absolute values more weight than errors with smaller absolute values.…”

Section: Experimental Designmentioning

confidence: 99%

“…One of the first approaches using ML algorithms for level prediction in reservoirs was presented by [24], who compared the performance of artificial neural networks and neuro-fuzzy approaches in a problem of short-term water level in two German rivers, from hydrological upstream data. Adaptive neuro-fuzzy inference algorithms were also considered by [25,26] for water level prediction in reservoirs after typhoons events. In [1], the performances of different ML algorithms such as neural networks, support vector regression, and deep learning algorithms are evaluated in a problem of reservoir operation (mainly inflow and outflow prediction) at different time scales, in the Gezhouba dam, across the Yangtze River, in China.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques

Castillo-Botón

Casillas-Pérez

Casanova-Mateo

et al. 2020

Water

View full text Add to dashboard Cite

This paper presents long- and short-term analyses and predictions of dammed water level in a hydropower reservoir. The long-term analysis was carried out by using techniques such as detrended fluctuation analysis, auto-regressive models, and persistence-based algorithms. On the other hand, the short-term analysis of the dammed water level in the hydropower reservoir was modeled as a prediction problem, where machine learning regression techniques were studied. A set of models, including different types of neural networks, Support Vector regression, or Gaussian processes was tested. Real data from a hydropower reservoir located in Galicia, Spain, qwew considered, together with predictive variables from upstream measuring stations. We show that the techniques presented in this paper offer an excellent tool for the long- and short-term analysis and prediction of dammed water level in reservoirs for hydropower purposes, especially important for the management of water resources in areas with hydrology stress, such as Spain.

show abstract

Section: Experimental Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques

Castillo-Botón

Casillas-Pérez

Casanova-Mateo

et al. 2020

Water

View full text Add to dashboard Cite

show abstract

“…The integration of neuronal networks with fuzzy logic, the model adaptive neuro-fuzzy inference system (ANFIS) appears as an object of investigation in numerous articles on prediction in the hydrological field. The work done by Chang and Chang [15], Wang et al [16] and Valizadeh and El-Shafie [9], can be an example. All of them carry out predictions of water volumes in reservoirs using the ANFIS technique.…”

Section: State Of the Artmentioning

confidence: 99%

Predicting Energy Generation Using Forecasting Techniques in Catalan Reservoirs

2019

View full text Add to dashboard Cite

Reservoirs are natural or artificial lakes used as a source of water supply for society daily applications. In addition, hydroelectric power plants produce electricity while water flows through the reservoir. However, reservoirs are limited natural resources since water levels vary according to annual rainfalls and other natural events, and consequently, the energy generation. Therefore, forecasting techniques are helpful to predict water level, and thus, electricity production. This paper examines state-of-the-art methods to predict the water level in Catalan reservoirs comparing two approaches: using the water level uniquely, uni-variant; and adding meteorological data, multi-variant. With respect to relating works, our contribution includes a longer times series prediction keeping a high precision. The results return that combining Support Vector Machine and the multi-variant approach provides the highest precision with an R 2 value of 0.99.

show abstract

“…ANFIS integrates both neural networks and fuzzy logic principle, whose inference system corresponds to a set of fuzzy rules [17] that have learning capability to approximate nonlinear functions. Successful implementations of ANFIS in many fields have been reported, such as prediction of water level in the reservoir [18] [19], forecasting of water discharge in a river [20], sea level prediction considering tide-generating forces and oceanic thermal expansion [21], prediction of flow through rockfill dams [22], downstream water level forecasting [23], flood forecasting [24], generation of customer satisfaction models [25], speech recognition [26], chaotic traffic volumes forecasting [27], etc.…”

Section: Introductionmentioning

confidence: 99%

Application of the Adaptive Neuro-Fuzzy Inference System for Optimal Design of Reinforced Concrete Beams

Yeh¹,

Yang²

2014

JILSA

View full text Add to dashboard Cite

Using a genetic algorithm owing to high nonlinearity of constraints, this paper first works on the optimal design of two-span continuous singly reinforced concrete beams. Given conditions are the span, dead and live loads, compressive strength of concrete and yield strength of steel; design variables are the width and effective depth of the continuous beam and steel ratios for positive and negative moments. The constraints are built based on the ACI Building Code by considering the strength requirements of shear and the maximum positive and negative moments, the development length of flexural reinforcement, and the serviceability requirement of deflection. The objective function is to minimize the total cost of steel and concrete. The optimal data found from the genetic algorithm are divided into three groups: the training set, the checking set and the testing set for the use of the adaptive neuro-fuzzy inference system (ANFIS). The input vector of ANFIS consists of the yield strength of steel, compressive strength of concrete, dead load, span, width and effective depth of the beam; its outputs are the minimum total cost and optimal steel ratios for positive and negative moments. To make ANFIS more efficient, the technique of Subtractive Clustering is applied to group the data to help streamline the fuzzy rules. Numerical results show that the performance of ANFIS is excellent, with correlation coefficients between the three targets and outputs of the testing data being greater than 0.99.

show abstract

Adaptive Neuro-fuzzy Inference System on Downstream Water Level Forecasting

Cited by 9 publications

References 14 publications

Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques

Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques

Predicting Energy Generation Using Forecasting Techniques in Catalan Reservoirs

Application of the Adaptive Neuro-Fuzzy Inference System for Optimal Design of Reinforced Concrete Beams

Contact Info

Product

Resources

About