ABSTRACT
KEYWORDS
ARTICLE HISTORY
Water flow Forecasting Methods for Optimal Water Resource Operation and Management: A Review
Nurul Najwa Anuar
1M. Reyasudin Basir Khan *
2Aizat Faiz Ramli
11Universiti Kuala Lumpur, British Malaysian Institute, Batu 8, Jalan Sungai Pusu, 53100, Selangor, Malaysia.
2School of Science and Engineering, Manipal International University, 71800, Negeri Sembilan, Malaysia.
1. INTRODUCTION
Water is a precious and valuable resource and must be carefully preserved for all living and non-living possessions.
Nevertheless, water management systems have always been a major issue particularly in water-stressed countries such as South Africa. Therefore, water resource engineers and hydrologists have developed a variety of strategies for water management to satisfy the growing water demand. In the water resource management, reservoirs and river basins are the most vital parts which offering reliable multipurpose water storage such as flood control, irrigation, and water supply. Hence, is necessary to manage the reservoir rate optimally in order to achieve the desired efficiency, it [1]. Owing the importance of water resource management, the water flow modelling field becomes a crucial area of study.
In general, flow forecasting can be divided into two type which is in term of short or long forecasting. For the short-term
forecasting, it refers to the daily or hourly forecasting, such it is operated for short‐term operation purposes such as flood protection. Additionally, short term forecasting is valuable when the reservoir has a capacity smaller than its annual inflow volume[2]. Meanwhile for long‐term forecasting, it refers to the monthly, seasonal, or annual timescales. Long term forecasting is useful for huge reservoirs with medium to long‐term operating purpose such as water supply and hydropower generation[2]. Until now, a huge variety of models had been used in the simulating the water flow and can be generally classified as physical and data driven model. Physical models offer a good insight into the catchment operation however it been criticized for being hard to implement. On the other hand, data-driven models outshining the physical model with the minimal information requirements and consuming less time in development. It gains the information from input-output data sets without considering the complex physical process and provide statistical correspondence among both input and output Water flow is a hydrological process that highly intermittent and dependent on nature. Water flow forecasting is an important task for operation and management of water resources for application that includes irrigation, water distribution, hydropower generation and flood prediction. There has been many methods and tools used to forecast and predicts water flow in many hydrological areas. As a result, the water flow modeling has become a key area of study due to the importance of water resource management. Hence, this paper aim to provide a comprehensive that compare methods used for water flow forecasting and summarized their key characteristics.
Reservoir
Water Flow Prediction River
ANN SVM
Malaysian Journal of Science Advanced Technology and
journal homepage: https://mjsat.com.my/
Received 17 Feb 2021 Received in revised form 22 Feb 2021
Accepted 23 Feb 2021 Available online 24 Feb 2021
© 2021 The Authors. Published by Penteract Technology.
This is an open access article under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/).
*Corresponding author:
E-mail address: M. Reyasudin Basir Khan <[email protected]>
2785-8901/ © 2021 The Authors. Published by Penteract Technology.
This is an open access article under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/).
[3]. Data-driven models such as the Fuzzy logic, Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) have been identified as useful tool in simulating the time-series hydrological problems[3].
The rest of the paper is organized as follow: Section 2 will explain the various area of flow prediction such as river, river basin and reservoir. Section 3 consist of method used in forecasting the flow. Section 4 discussed on the method use for water flow forecasting by comparing previous works. And finally, Section 5 concluded this paper.
2. AREA OF STUDY
Accurate stream flow forecasting is important for effective management practices [4]. It also helps in providing data to city planning and the real-time operation of water resource projects, in which it minimizes the environmental impact of climate events. The water flow modelling occurs in many areas.
It can be seen in the previous works that many researchers applied their studies in various location such as at the river, watershed, and reservoir
2.1 River
River flow modelling is crucial for flood and droughts control, water allocation, navigation, hydroelectric energy production and for river basin management[5]. However, river flow modelling appears to be a complicated process due to the impacts of hydro-climatic parameter such as temperature, precipitation and evaporation[6]. Consequently, many previous researchers try to propose models to forecast river flow with relative ease and reasonable performance [6]–[14]. Where, several researchers have been found to applied various of model for simulating the river flow in in the short term [15]. For example, in the term of short-term forecasting, paper [16] used two ANN model for 1-day-ahead prediction of continuous daily streamflow. Meanwhile, paper [17] and [18] examined the wavelet neural networks (WNN) ability to predict short-term daily flow of rivers. Concurrently, Kalteh tested the reliability of two models to predict the monthly river flow in the long term [12]. Paper [5] also explores the applicability of two separate data to forecast the monthly flow of rivers in north-western Iran.
2.2 River Basin
River is a dynamic system that is closely related to the physical characteristics of the basin area. However, the accurate water flow forecasting at the river basin is important for many purposes since the basin controls the river flow, sped and water level fluctuation[20]. For instance, in the Upper Klamath River Basin in southern-central Oregon and northern California, the accurate streamflow forecasting during late spring and summer are required by water management agencies to balance water allocations for aquatic habit, agriculture, and hydropower [19].
Traditionally, streamflow forecasting at the river basin is performed using both physical and data driven models [21].
Nevertheless, many previous researchers used data drive model to predict the water flow at the river basin [22]–[24]. For instance, Adnan et al. select the hydraulic station in the upper Indus Basin as their area of research to evaluate the capability of the ANN and Support Vector Machine (SVM) models[21].
In addition, Ghumman et al. choose the watershed in Pakistan to compare the ANN model with the theoretical conceptual model for runoff prediction [25]. Meanwhile, Areerachakul and
Junsawang implement ANN model for discharge forecasting as well as daily rainfall in Lam Phachi watershed. Hadi and Tombul 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[26]. Moreover, paper [27] and [28] also developed ANN model for predicting flow at the river basin.
Meanwhile, Hadi and Tombul compares the accuracy of ANN, ANFIS, and SVM in forecasting the daily streamflow at basin with the traditional approach known as autoregressive (AR) model with different physical characteristics[26]. Moreover, paper [27] and [28] also developed ANN model for predicting flow at the river basin.
2.3 Reservoir
One of the most common engineering operations carried out on river systems is the construction of dams. Where, it affects half of the major global river systems[29]. Therefore, accurate reservoir predictions are crucial for reservoirs optimization. Inflow forecasting of reservoirs is of considerable importance which is related to flood control and downstream water release planning. Many studies have been reported on forecasting the reservoir inflow. For example, ANN and SVM models is proposed in paper [30] by using different new input patterns to predict inflow at the Zayandehroud reservoir. Apart from that, Santos and Silva used the WNN model to predict daily inflows to different reservoir[24]. Paper [23]also applied WNN model to predict the short-term daily inflow of reservoir using the data from the Tropical Rainfall Measurement Mission (TRMM). Moreover, paper [31] present a Nonlinear Auto Regressive model with eXogenous inputs (NARX) model for daily reservoir inflow forecasting for 365-day lead time.
Meanwhile, a budget river flow measurement system has been built to estimate the inflows that fed into the four hydroelectric in the northern part of Malaysia in order to improve reservoir regulation [32]. A modified SVM-based forecasting model is proposed in paper [33] to improve the predictability of the inflow at the Shihmen Reservoir using previous-period climate data. Paper [34] focuses on applying to ANN model to forecast the performance of hydropower plant in terms of water flow rate, power generation and net turbine head.
3. PREDICTION METHOD
Over the years, a variety of techniques have been used to estimate flow discharge. Since the last three decades of the preceding millennium, statistical methods have been successfully applied in the area of hydrology which include the prediction of river flows [35]. The example of the statistical method is autoregressive moving average (ARMA), multiple regression models and simple regression model[4]. Auto- regressive integrated moving average methods (ARIMA) and seasonal auto-regressive integrated moving average methods (SARIMA) are also the example of statistical method and commonly be applied in hydrology modelling[36]–[41]. For instance, Kurunc et al. [38], Adnan et al. [42], Ahlert and Mehta [36] used ARIMA statistical models to model the river flow data. Meanwhile, Valipour [43] and Rabenja et al. [44]
predicted runoff data by applying the SARIMA model and comparing it with the ARIMA model. However, the statistical model not capable of producing high predictive accuracy and do not perform well [45], [46].For instance, in the [45] Tayab et.al compared the performance of ANN model with th statistical model. They found that the statistical model was
unable to generate a good non-liner simulation as ANN model.
Furthermore, Dogan et al. compare the performance of autoregressive method and ANN model to forecast the daily streamflow [47]. Their research work conclude that ANN model showed to has better performance than autoregressive method.
3.1 Artificial Neural Network (ANN)
ANN model consists of a group of interconnected neurons normally organized in multiple layers. Fig 1 demonstrated the architecture of ANN model. Commonly, ANN model has three or more layers namely, input layer, hidden layer and output layer.
Fig. 1. ANN architecture
It has been shown that ANN model could provide a high- precision flow prediction in the earlier research [45], [48], [49].
Additionally, ANN model has the ability to predict the river flow by using other nearby river flow information, which could be an important tool for covering the missing flow data records [50]. Herewati et al. had proved that the ANN approach can be used for river flow forecasting at the Kapuas River with the result that close to the actual data [7]. Yaseen et.al also created two type of ANN model which is the feed forward back propagation neural network and radial basis function neural network to predict the daily streamflow at the Johor River[16].
Their results show that the radial basis function neural network surpasses the model of feed forward back propagation neural network and can be successfully implemented in the daily streamflow forecasting to provide reliability and high precision.
Lee and Kang used an ANN ensemble model for daily discharge simulations [28]. outcome proved that ANN ensemble technique is useful in calculating model ANN's uncertainties and provides satisfactory results for modelling the streamflow.
3.2 Adaptive Neuro Fuzzy Support Vector Machine (ANFIS) ANFIS is a kind of adaptive network that combines both Fuzzy logic and neural network concept. Fig 2 illustrates the ANFIS model's architecture. The ANFIS model combines the learning capability of neural networks and the ability of fuzzy logic to manage uncertain information to form better estimators [4], [51]. Hence, it has the strengths of the ANN and the fuzzy logic method concurrently [22]. The model was created by Jang in 1993 and was widely used in water resource management and hydrological issues, especially in streamflow forecasting [52]–
[54]. For example, for daily river discharge forecasting, Sarmad applied and compared the performance of ANFIS and ANN
model [11]. His research work concluded that ANN and ANFIS model are powerful tools to simulate short-term river flow.
For example, Sarmad applied and compared the performance of ANFIS and ANN model for daily river discharge forecasting [11]. His research work concluded that ANN and ANFIS models are powerful tools for simulating the river flow in short term.
Fig. 2. ANFIS architecture
In [22], ANN and ANFIS models were used to predict streamflow with different time steps and cross validation in the basin. The result shows that the ANFIS model produced superior performance than the ANN model. Khadangi et al.
developed ANFIS and radial basis function neural network model for daily river forecasting [55]. Their performance of both models was compared, and the result shows that ANFIS model had higher performance than the radial basis function neural network model in forecasting the river.
3.3 Support Vector Machine
SVM method is generally considered as the classifier and has been effectively expended on nonlinear regression issues [56], [57]. SVM model was built based on structural risk minimization principle [58]. Where, It minimizes the learning predicted error model and reduces the overfitting problem [14], [33], [56], [57]. In addition, SVM model can produce optimum results and reduce the time spent. SVM has been shown to be robust in hydrological modeling [59]. In the earlier research, SVM model had appeared to have better efficiency than ANN model in predicting the streamflow [21], [30], [60]. SVM model also had shown to performed biter than the statistical model.
For instance, paper [57] analysed SVM model’s ability to predict streamflow at ungagged location and its performance was compared with the multi-linear regression (MLR). The result shows that the SVM model was discovered to outperform the MLR predictability. Rafidah and Suhaila compared the performance of SVM model with the ARIMA model in term of flow forecasting and their result indicated that the performance of SVM model exceed the ARIMA model [13].
3.4 Wavelet Neural Network
In recent years, the study of wavelets has been carried out in the area of hydrology [61]–[64]. WNN was originally studied by Wang and Ding where they discovered that the integration of wavelet and ANN methods could enhance the model accuracy, particularly in term of long forecasting [64], [65]. It offers a mathematical method to decompose a signal into multi levels of details [66]. Its superior performance also was properly proven compared to traditional techniques [67]. It can provide an efficient approach to hydrological time series
analysis and its performance has properly proven to performed better than traditional techniques [67]–[70]. The idea of joining wavelet and ANN model had resulted in the formulation of wavelet neural network which has been applied in different fields [66]. For example, Adamowski and Sun implemented WNN an ANN model at two different non-perennial rivers to predict 1-and 3-day lead times. Their results demonstrate WNN model provided more accurate results than the ANN model in short term flow forecasting [18]. Santos and Silva used WNN model to predict the daily inflow to another reservoir [24].
While, Wei et al. used data from the monthly time series to establish a WNN model for forecasting river discharge[71].
WNN model is implemented in paper [23] to forecast the daily inflow in the short time. Kalteh investigated the ANN and support vector regression (SVR) accuracy to predict the monthly river flow that combined with wavelet transform [12].
His outcome demonstrated that both ANN and SVR models that combined with wavelet transform produce better result than single ANN and SVR model in term of river flow prediction.
4. DISCUSSION
ANN is the most used method for dealing with the complex nonlinear relationship, especially if the data set is not complete.
In previous research, it has also shown that the ANN method is an effective tool for forecasting the water flow with the result that is close to the actual results. In addition, the ANN model also showed better results than the statistical method in which the statistical model could not produce a good non-liner simulation like the ANN model. The SVM model was also seen in conjunction with the ANN model as a relatively effective tool for modelling the water flow which performed better than the statistical model. Nevertheless, the problem of generalization remains with the ANN model. In this context ANFIS, SVM and WNN have performed better than ANN. Where, the previous researcher found that the model of ANFIS performs better than the model of ANN in simulating the flow of the river. Since the ANFIS model has a hybrid learning approaches in its structure.
It also has the learning capability of neural networks and the ability of fuzzy logic to handle the uncertain data that helps the algorithm to be faster and more accurate in terms of efficiency than most ANN algorithms. In the meantime, in predicting the streamflow, the WNN model also had shown better efficiency than the ANN model. The combination of wavelet transformations and the ANN model has become an effective tool for enhancing the performance of ANN models. Overall, hybrid models like ANFIS and WNN model could provide better prediction than a single prediction model with relatively accurate prediction.
5. CONCLUSIONS
In conclusion, water management systems become an important issue. Hence, due to the significance of water resource management, the water flow modelling field becomes a key area of study. Various of study areas for water flow prediction in the previous research have been reviewed in this paper. Additionally, a large range of models had been used to simulate water flow. In the earlier studies, the previous researchers applied statistical model for simulating the water flow. However, due to the incapable of statistical model to simulate in accuracy, many researchers implement data driven model to simulate the water flow. Yet, in the data driven model,
it had shown that the hybrid model could provide better prediction than a single prediction model with relatively accurate forecasts since it hold two benefits of model.
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