Short-term water level prediction using neural networks and neuro-fuzzy approach

Bazartseren, Bunchingiv; Hildebrandt, G.; Holz, Klaus-Peter

doi:10.1016/s0925-2312(03)00388-6

Cited by 120 publications

(58 citation statements)

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“…Fuzzy datasets can directly come from a monitoring system, like radar data, which intrinsically provides areas of localization. Also, original crisp data can be artificially fuzzified for specific analysis, taking into account the precision of the measurement instruments and using specific fuzzification algorithms (see [20] [124] [173] [137] [66] [117]. In this paper, we will provide preprocessing methods oriented to crisp representation of data, which is the most used for simplicity in current real applications.…”

Section: Building the Original Data Matrixmentioning

confidence: 99%

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

Gibert

Sànchez–Marrè

Izquierdo

2016

AIC

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One of the important issues related with all types of data analysis, either statistical data analysis, machine learning, data mining, data science or whatever form of data-driven modeling, is data quality. The more complex the reality to be analyzed is, the higher the risk of getting low quality data. Unfortunately real data often contain noise, uncertainty, errors, redundancies or even irrelevant information. Useless models will be obtained when built over incorrect or incomplete data. As a consequence, the quality of decisions made over these models, also depends on data quality. This is why pre-processing is one of the most critical steps of data analysis in any of its forms. However, pre-processing has not been properly systematized yet, and little research is focused on this. In this paper a survey on most popular pre-processing steps required in environmental data analysis is presented, together with a proposal to systematize it. Rather than providing technical details on specific pre-processing techniques, the paper focus on providing general ideas to a non-expert user, who, after reading them, can decide which one is the more suitable technique required to solve his/her problem.

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Section: Building the Original Data Matrixmentioning

confidence: 99%

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

Gibert

Sànchez–Marrè

Izquierdo

2016

AIC

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“…Moreover, there are many noise levels in different time-series regions, which further increase the difficulty of forecasting models. Hence, it is hard for a single time-series forecasting model to capture the dynamic changing processes and features, which may encounter local under-fitting or over-fitting problems [29][30][31][32][33]. The accuracy of a single forecast method always has limited effects.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Cheng

Feng

Niu

et al. 2015

Water

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Abstract:Reservoir monthly inflow is rather important for the security of long-term reservoir operation and water resource management. The main goal of the present research is to develop forecasting models for the reservoir monthly inflow. In this paper, artificial neural networks (ANN) and support vector machine (SVM) are two basic heuristic forecasting methods, and genetic algorithm (GA) is employed to choose the parameters of the SVM. When forecasting the monthly inflow data series, both approaches are inclined to acquire relatively poor performances. Thus, based on the thought of refined prediction by model combination, a hybrid forecasting method involving a two-stage process is proposed to improve the forecast accuracy. In the hybrid method, the ANN and SVM are, first, respectively implemented to forecast the reservoir monthly inflow data. Then, the processed predictive values of both ANN and SVM are selected as the input variables of a newly-built ANN model for refined forecasting. Three models, ANN, SVM, and the hybrid method, are developed for the monthly inflow forecasting in Xinfengjiang reservoir with 71-year discharges from 1944 to 2014. The comparison of results reveal that three models have satisfactory performances in the Xinfengjiang reservoir monthly inflow prediction, and the hybrid method performs better than ANN and SVM in terms of five statistical indicators. Thus, the hybrid method is an efficient tool for the long-term operation and dispatching of Xinfengjiang reservoir. OPEN ACCESSWater 2015, 7 4478

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“…Addressing this issue with optimisation techniques, short-term time scale water level prediction have been researched by (Bazartseren, Hildebrandt, & Holz, 2003). They have optimized their models with the neuro-fuzzy (NF) system model and compared its proficiency with an ANN model.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Data Intelligent Models and Applications for Water Level Prediction

Yaseen

Deo

Ebtehaj

et al. 2018

Advances in Computational Intelligence and Robotics

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Short-term water level prediction using neural networks and neuro-fuzzy approach

Cited by 120 publications

References 1 publication

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

Hybrid Data Intelligent Models and Applications for Water Level Prediction

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