Forecasting model for the change in stage of reservoir water level

Reservoir is one of major structural approaches for flood mitigation. During floods, early reservoir water release is one of the actions taken by the reservoir operator to accommodate incoming heavy rainfall. Late water release might give negative effect to the reservoir structure and cause flood at downstream area. However, current rainfall may not directly influence the change of reservoir water level. The delay may occur as the streamflow that carries the water might take some time to reach the reservoir. This study is aimed to develop a forecasting model for the change in stage of reservoir water level. The model considers the changes of reservoir water level and its stage as the input and the future change in stage of reservoir water level as the output. In this study, the Timah Tasoh reservoir operational data was obtained from the Perlis Department of Irrigation and Drainage (DID). The reservoir water level was categorised into stages based on DID manual. A modified sliding window algorithm has been deployed to segment the data into temporal patterns. Based on the patterns, three models were developed: the reservoir water level model, the change of reservoir water level and stage of reservoir water level model, and the combination of the change of reservoir water level and stage of reservoir water level model. All models were simulated using neural network and their performances were compared using on mean square error (MSE) and percentage of correctness. The result shows that the change of reservoir water level and stage of reservoir water model produces the lowest MSE and the highest percentage of correctness when compared to the other two models. The findings also show that a delay of two previous days has affected the change in stage of reservoir water level. The model can be applied to support early reservoir water release decision making. Thus, reduce the impact of flood at the downstream area

Downstream flow top width prediction in a river system

ANFIS, ARIMA and Hybrid Multiple Inflows Muskingum models (HMIM) were applied to simulate and forecast downstream discharge and flow top widths in a river system. The ANFIS model works on a set of linguistic rules while the ARIMA model uses a set of past values to predict the next value in a time series. The HMIM model assumes a power-law relationship between water discharge and flow top width at a section. The models were used to simulate and forecast discharge and flow top width at a downstream section in the Barak River system in India. Flow top widths corresponding to  different flow depths at the downstream section were estimated using a digital elevation model (DEM). The parameters in the hybrid model were estimated by applying Non-dominated Sorting Genetic Algorithm II  (NSGA-II). The study shows that the power-law relationship involving section characteristics can describe the top width versus discharge  relationship for a section. The models allow direct estimation of the downstream flow top width on the basis of upstream flow variables. Results obtained in the study show that performances of the HMIM, ANFIS and  ARIMA models are satisfactory, having average prediction errors of less than 7% of the average value of the observed series. Application of the ANFIS, ARIMA and the HMIM models to the studied river system  demonstrate the suitability of the models in simulating and forecasting  downstream flow top width in river systems.Keywords: River system, flow top width, genetic algorithm, flood flow, hybrid mode

Machine Learning Methods for Better Water Quality Prediction

In any aquatic system analysis, the modelling water quality parameters are of considerable significance. The traditional modelling methodologies are dependent on datasets that involve large amount of unknown or unspecified input data and generally consist of time-consuming processes. The implementation of artificial intelligence (AI) leads to a flexible mathematical structure that has the capability to identify non-linear and complex relationships between input and output data. There has been a major degradation of the Johor River Basin because of several developmental and human activities. Therefore, setting up of a water quality prediction model for better water resource management is of critical importance and will serve as a powerful tool. The different modelling approaches that have been implemented include: Adaptive Neuro-Fuzzy Inference System (ANFIS), Radial Basis Function Neural Networks (RBF-ANN), and Multi-Layer Perceptron Neural Networks (MLP-ANN). However, data obtained from monitoring stations and experiments are possibly polluted by noise signals as a result of random and systematic errors. Due to the presence of noise in the data, it is relatively difficult to make an accurate prediction. Hence, a Neuro-Fuzzy Inference System (WDT-ANFIS) based augmented wavelet de-noising technique has been recommended that depends on historical data of the water quality parameter. In the domain of interests, the water quality parameters primarily include ammoniacal nitrogen (AN), suspended solid (SS) and pH. In order to evaluate the impacts on the model, three evaluation techniques or assessment processes have been used. The first assessment process is dependent on the partitioning of the neural network connection weights that ascertains the significance of every input parameter in the network. On the other hand, the second and third assessment processes ascertain the most effectual input that has the potential to construct the models using a single and a combination of parameters, respectively. During these processes, two scenarios were introduced: Scenario 1 and Scenario 2. Scenario 1 constructs a prediction model for water quality parameters at every station, while Scenario 2 develops a prediction model on the basis of the value of the same parameter at the previous station (upstream). Both the scenarios are based on the value of the twelve input parameters. The field data from 2009 to 2010 was used to validate WDT-ANFIS. The WDT-ANFIS model exhibited a significant improvement in predicting accuracy for all the water quality parameters and outperformed all the recommended models. Also, the performance of Scenario 2 was observed to be more adequate than Scenario 1, with substantial improvement in the range of 0.5% to 5% for all the water quality parameters at all stations. On validating the recommended model, it was found that the model satisfactorily predicted all the water quality parameters (R2 values equal or bigger than 0.9). © 201

Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan

Multi-step ahead inflow forecasting has a critical role to play in reservoir operation and management in Taiwan during typhoons as statutory legislation requires a minimum of 3-hours warning to be issued before any reservoir releases are made. However, the complex spatial and temporal heterogeneity of typhoon rainfall, coupled with a remote and mountainous physiographic context makes the development of real-time rainfall-runoff models that can accurately predict reservoir inflow several hours ahead of time challenging. Consequently, there is an urgent, operational requirement for models that can enhance reservoir inflow prediction at forecast horizons of more than 3-hours. In this paper we develop a novel semi-distributed, data-driven, rainfall-runoff model for the Shihmen catchment, north Taiwan. A suite of Adaptive Network-based Fuzzy Inference System solutions is created using various combinations of auto-regressive, spatially-lumped radar and point-based rain gauge predictors. Different levels of spatially-aggregated radar-derived rainfall data are used to generate 4, 8 and 12 sub-catchment input drivers. In general, the semi-distributed radar rainfall models outperform their less complex counterparts in predictions of reservoir inflow at lead-times greater than 3-hours. Performance is found to be optimal when spatial aggregation is restricted to 4 sub-catchments, with up to 30% improvements in the performance over lumped and point-based models being evident at 5-hour lead times. The potential benefits of applying semi-distributed, data-driven models in reservoir inflow modelling specifically, and hydrological modelling more generally, is thus demonstrated

Reservoir water release dynamic decision model based on spatial temporal pattern

The multi-purpose reservoir water release decision requires an expert to make a decision by assembling complex decision information that occurred in real time. The decision needs to consider adequate reservoir water balance in order to maintain reservoir multi-purpose function and provide enough space for incoming heavy rainfall and inflow. Crucially, the water release should not exceed the downstream maximum river level so that it will not cause flood. The rainfall and water level are fuzzy information, thus the decision model needs the ability to handle the fuzzy information. Moreover, the rainfalls that are recorded at different location take different time to reach into the reservoir. This situation shows that there is spatial temporal relationship hidden in between each gauging station and the reservoir. Thus, this study proposed dynamic reservoir water release decision model that utilize both spatial and temporal information in the input pattern. Based on the patterns, the model will suggest when the reservoir water should be released. The model adopts Adaptive Neuro-Fuzzy Inference System (ANFIS) in order to deal with the fuzzy information. The data used in this study was obtained from the Perlis Department of Irrigation and Drainage. The modified Sliding Window algorithm was used to construct the rainfall temporal pattern, while the spatial information was established by simulating the mapped rainfall and reservoir water level pattern. The model performance was measured based on the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Findings from this study shows that ANFIS produces the lowest RMSE and MAE when compare to Autoregressive Integrated Moving Average (ARIMA) and Backpropagation Neural Network (BPNN) model. The model can be used by the reservoir operator to assist their decision making and support the new reservoir operator in the absence of an experience reservoir operator

A comparison of artificial intelligence approaches in predicting discharge coefficient of streamlined weirs

In the present research, three different data-driven models (DDMs) are developed to predict the discharge coefficient of streamlined weirs (C-dstw). Some machine-learning methods (MLMs) and intelligent optimization models (IOMs) such as Random Forest (RF), Adaptive NeuroFuzzy Inference System (ANFIS), and gene expression program (GEP) methods are employed for the prediction of C-dstw. To identify input variables for the prediction of C-dstw by these DMMs, among potential parameters on C-dstw, the most effective ones including geometric features of streamlined weirs, relative eccentricity (lambda), downstream slope angle (beta), and water head over the crest of the weir (h(1)) are determined by applying Buckingham pi-theorem and cosine amplitude analyses. In this modeling, by changing architectures and fundamental parameters of the aforesaid approaches, many scenarios are defined to obtain ideal estimation results. According to statistical metrics and scatter plot, the GEP model is determined as a superior method to estimate C-dstw with high performance and accuracy. It yields an R-2 of 0.97, a Total Grade (TG) of 20, RMSE of 0.032, and MAE of 0.024. Besides, the generated mathematical equation for C-dstw in the best scenario by GEP is likened to the corresponding measured ones and the differences are within 0-10%

Performance Evaluation of Two ANFIS Models for Predicting Water Quality Index of River Satluj (India)

Water quality index is the most convenient way of communicating water quality status of water bodies, but its evaluation requires subjectivity in terms of user involvement and dealing with uncertainty. Recently, artificial intelligence algorithms that are appropriate for nonlinear forecasting and also dealing with uncertainties have been applied to various domains of water quality forecasting. This paper focuses on development of a data-driven adaptive neurofuzzy system for the water quality index using a real data set obtained from eight different monitoring stations across River Satluj in northern India. Novelty in the paper lies in the estimation of water quality index using two different clustering techniques: fuzzy C-means and subtractive clustering-based ANFIS and assessing their predictive accuracy. Each model was used to train, validate, and test the index that was obtained from seven water quality parameters including pH, conductivity, chlorides, nitrates, ammonia, and fecal coliforms. The models were evaluated on the basis of statistical performance criteria. Based on the evaluations, it was found that the SC-ANFIS method gave more accurate result as compared to the FCM-ANFIS. The tested model, SC-ANFIS model, was further used to identify those sensitive parameters across various monitoring stations that were capable of causing change in the existing water quality index value

Integration of Data-Driven Modeling and Stochastic Modeling for Multi-purpose Reservoir Simulation

Water Resources Planning and Managemen

Forecasting project success in the construction industry using adaptive neuro-fuzzy inference system

Project managers often find it a challenge to successfully manage construction projects. As a result, understanding, evaluating, and achieving project success are critical for sponsors to control projects. In practice, determining key success factors and criteria to assess the performance of construction projects and forecast the success of new projects is difficult. To address these concerns, our objective is to go beyond the efficiency-oriented project success criteria by considering both efficiency- and effectiveness-oriented measures to evaluate project success. This paper contributes to existing knowledge by identifying a holistic and multidimensional set of project success factors and criteria using a two-round Delphi technique. We developed a decision support system using the Adaptive Neuro-Fuzzy Inference System (ANFIS) to forecast the success of mid- and large-sized construction projects. We gathered data from 142 project managers in Australia and New Zealand to implement the developed ANFIS. We then validated the constructed ANFIS using the K-fold cross-validation procedure and a real case study of a large construction project in Western Australia. The forecasting accuracy measures R2=0.97461, MAPE = 2.57912%, MAE = 1.88425, RMSE = 2.3610, RRMSE = 0.03149, and PI = 0.01589 suggest that the developed ANFIS is a very good predictor of project success