scholarly journals Multi-step streamflow forecasting using data-driven non-linear methods in contrasting climate regimes

2013 ◽  
Vol 16 (3) ◽  
pp. 671-689 ◽  
Author(s):  
Daniel J. Karran ◽  
Efrat Morin ◽  
Jan Adamowski
Keyword(s):  
Wavelet Transforms ◽  
Model Performance ◽  
Probability Of Detection ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Non Linear ◽  
Using Data

Considering the popularity of using data-driven non-linear methods for forecasting streamflow, there has been no exploration of how well such models perform in climate regimes with differing hydrological characteristics, nor has the performance of these models, coupled with wavelet transforms, been compared for lead times of less than 1 month. This study compares the use of four different models, namely artificial neural networks (ANNs), support vector regression (SVR), wavelet-ANN, and wavelet-SVR in a Mediterranean, Oceanic, and Hemiboreal watershed. Model performance was tested for 1, 2 and 3 day forecasting lead times, measured by fractional standard error, the coefficient of determination, Nash–Sutcliffe model efficiency, multiplicative bias, probability of detection and false alarm rate. SVR based models performed best overall, but no one model outperformed the others in more than one watershed, suggesting that some models may be more suitable for certain types of data. Overall model performance varied greatly between climate regimes, suggesting that higher persistence and slower hydrological processes (i.e. snowmelt, glacial runoff, and subsurface flow) support reliable forecasting using daily and multi-day lead times.

scholarly journals Data-Driven Modelling of Smart Building Ventilation Subsystem

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Grigore Stamatescu ◽  
Iulia Stamatescu ◽  
Nicoleta Arghira ◽  
Ioana Fagarasan
Keyword(s):  
Data Mining ◽  
Data Streams ◽  
Data Driven ◽  
Support Vector ◽  
Commercial Building ◽  
Monitoring And Control ◽  
Smart Building ◽  
Building Ventilation ◽  
Using Data ◽  
Rich Data

Considering the advances in building monitoring and control through networks of interconnected devices, effective handling of the associated rich data streams is becoming an important challenge. In many situations, the application of conventional system identification or approximate grey-box models, partly theoretic and partly data driven, is either unfeasible or unsuitable. The paper discusses and illustrates an application of black-box modelling achieved using data mining techniques with the purpose of smart building ventilation subsystem control. We present the implementation and evaluation of a data mining methodology on collected data from over one year of operation. The case study is carried out on four air handling units of a modern campus building for preliminary decision support for facility managers. The data processing and learning framework is based on two steps: raw data streams are compressed using the Symbolic Aggregate Approximation method, followed by the resulting segments being input into a Support Vector Machine algorithm. The results are useful for deriving the behaviour of each equipment in various modi of operation and can be built upon for fault detection or energy efficiency applications. Challenges related to online operation within a commercial Building Management System are also discussed as the approach shows promise for deployment.

scholarly journals Support Vector Machine and Neural Network based Model for Monthly Stream Flow Forecasting

2018 ◽  
Vol 7 (4.35) ◽  
pp. 683
Author(s):  
Nuratiah Zaini ◽  
Marlinda Abdul Malek ◽  
Marina Yusoff ◽  
Siti Fatimah Che Osmi ◽  
Nurul Hani Mardi ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Hybrid Model ◽  
Peninsular Malaysia ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Design Development ◽  
Streamflow Forecasting ◽  
Monthly Streamflow ◽  
Bpnn Model

Accurate forecasting of streamflow is desired in many water resources planning and management, flood prevention and design development. In this study, the accuracy of two hybrid model, support vector machine - particle swarm optimization (SVM-PSO) and bat algorithm – backpropagation neural network (BA-BPNN) for monthly streamflow forecasting at Kuantan River located in Peninsular Malaysia are investigated and compared to regular SVM and BPNN model. Heuristic optimization namely PSO and BA are introduced to find the optimum SVM and BPNN parameters. The input parameters to the forecasting models are antecedent streamflow, historical rainfall and meteorological parameters namely evaporation, temperature, relative humidity and mean wind speed. Two performance evaluation measure, root mean square error (RMSE) and coefficient of determination (R2) were employed to evaluate the performance of developed forecasting model. It is found that, RMSE and R2 for hybrid SVM-PSO are 24.8267 m3/s and 0.9651 respectively while general SVM model yields RMSE of 27.5086 m3/s and 0.9305 of R2 for testing phase. Besides that, hybrid BA-BPNN produces RMSE, 17.7579 m3/s and R2, 0.7740 while BPNN model produces lower RMSE and R2 of 28.1396 m3/s and 0.5015 respectively. Therefore, the results indicate that hybrid model, SVM-PSO and Bat-BPNN yield better performance as compared to general SVM and BPNN, respectively in streamflow forecasting.

scholarly journals A Probabilistic Wavelet–Support Vector Regression Model for Streamflow Forecasting with Rainfall and Climate Information Input*

2015 ◽  
Vol 16 (5) ◽  
pp. 2209-2229 ◽  
Author(s):  
Zhiyong Liu ◽  
Ping Zhou ◽  
Yinqin Zhang
Keyword(s):  
Regression Model ◽  
Support Vector Regression ◽  
Support Vector ◽  
Climate Indices ◽  
Lead Times ◽  
Discrete Wavelet ◽  
Streamflow Forecasting ◽  
Support Vector Regression Model ◽  
Inference System ◽  
Predictive Uncertainty

Abstract It is essential to explore reliable streamflow forecasting techniques for water resources management. In this study, a Bayesian wavelet–support vector regression model (BWS model) is developed for one- and multistep-ahead streamflow forecasting using local meteohydrological observations and climate indices including El Niño–Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD) as potential predictors. To accomplish this, a two-step strategy is applied. In the first step, the discrete wavelet transform is coupled with a support vector regression model for streamflow prediction. The three key factors of mother wavelets, decomposition levels, and edge effects are considered in the wavelet decomposition phase when using the hybrid wavelet–support vector regression model (WS model). Different combinations of these factors form a variety of WS models with corresponding forecasts. The second step combines multiple candidate WS models with “good” performance via Bayesian model averaging. This integrates the predictive strengths of different candidate WS models, giving a realistic assessment of the predictive uncertainty. The new ensemble model is used to forecast daily and monthly streamflows at two sites in Dongjiang basin, southern China. The results show that the proposed BWS model consistently generates more reliable predictions for daily (lead times of 1–7 days) and monthly (lead times of 1–3 months) forecasts as compared with the best single-member WS models and the adaptive neuro-fuzzy inference system (ANFIS). Furthermore, the proposed BWS model provides detailed information about the predictive uncertainty.

scholarly journals Analysis of Model Performance and Implementation of an Optimized Flood Prediction Model Using Data Mining Techniques

2021 ◽  
Vol 10 (2) ◽  
pp. 888-893
Keyword(s):  
Prediction Model ◽  
Model Performance ◽  
Warning System ◽  
Support Vector ◽  
Ann Model ◽  
Flood Prediction ◽  
River Region ◽  
Input Variables ◽  
Using Data ◽  
Artificial Neural Network Ann

Flooding is a major problem globally, and especially in SuratThani province, Thailand. Along the lower Tapeeriver in SuratThani, the population density is high. Implementing an early warning system can benefit people living along the banks here. In this study, our aim was to build a flood prediction model using artificial neural network (ANN), which would utilize water and stream levels along the lower Tapeeriver to predict floods. This model was used to predict flood using a dataset of rainfall and stream levels measured at local stations. The developed flood prediction model consisted of 4 input variables, namely, the rainfall amounts and stream levels at stations located in the PhraSeang district (X.37A), the Khian Sa district (X.217), and in the Phunphin district (X.5C). Model performance was evaluated using input data spanning a period of eight years (2011–2018). The model performance was compared with support vector machine (SVM), and ANN had better accuracy. The results showed an accuracy of 97.91% for the ANN model; however, for SVM it was 97.54%. Furthermore, the recall (42.78%) and f-measure (52.24%) were better for our model, however, the precision was lower. Therefore, the designed flood prediction model can estimate the likelihood of floods around the lower Tapee river region

scholarly journals Using data mining methods to improve discharge coefficient prediction in Piano Key and Labyrinth weirs

2021 ◽  
Author(s):  
Mahdi Majedi-Asl ◽  
Mehdi Foladipanah ◽  
Venkat Arun ◽  
Ravi Prakash Tripathi
Keyword(s):  
Discharge Coefficient ◽  
Gene Expression Programming ◽  
Research Work ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Effective Parameters ◽  
Data Set ◽  
Labyrinth Weir ◽  
Dimensional Parameters ◽  
Using Data

Abstract As a remarkable parameter, the discharge coefficient (Cd) plays an important role in determining weirs' passing capacity. In this research work, the support vector machine (SVM) and the gene expression programming (GEP) algorithms were assessed to predict Cd of piano key weir (PKW), rectangular labyrinth weir (RLW), and trapezoidal labyrinth weir (TLW) with gathered experimental data set. Using dimensional analysis, various combinations of hydraulic and geometric non-dimensional parameters were extracted to perform simulation. The superior model for the SVM and the GEP predictor for PKW, RLW, and TLW included , and respectively. The results showed that both algorithms are potential in predicting discharge coefficient, but the coefficient of determination (RMSE, R2, Cd(DDR)max) illustrated the superiority of the GEP performance over the SVM. The results of the sensitivity analysis determined the highest effective parameters for PKW, RLW, and TLW in predicting discharge coefficients are , , and Fr respectively.

scholarly journals Application of a Hybrid ARIMA–SVR Model Based on the SPI for the Forecast of Drought—A Case Study in Henan Province, China

2020 ◽  
Vol 59 (7) ◽  
pp. 1239-1259
Author(s):  
Dehe Xu ◽  
Qi Zhang ◽  
Yan Ding ◽  
Huiping Huang
Keyword(s):  
Time Scale ◽  
Interpolation Method ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Kriging Interpolation ◽  
Support Vector ◽  
Kriging Interpolation Method

AbstractDrought forecasts could effectively reduce the risk of drought. Data-driven models are suitable forecast tools because of their minimal information requirements. The motivation for this study is that because most data-driven models, such as autoregressive integrated moving average (ARIMA) models, can capture linear relationships but cannot capture nonlinear relationships they are insufficient for long-term prediction. The hybrid ARIMA–support vector regression (SVR) model proposed in this paper is based on the advantages of a linear model and a nonlinear model. The multiscale standard precipitation indices (SPI: SPI1, SPI3, SPI6, and SPI12) were forecast and compared using the ARIMA model and the hybrid ARIMA–SVR model. The performance of all models was compared using measures of persistence, such as the coefficient of determination, root-mean-square error, mean absolute error, Nash–Sutcliffe coefficient, and kriging interpolation method in the ArcGIS software. The results show that the prediction accuracies of the multiscale SPI of the combined ARIMA–SVR model and the single ARIMA model were related to the time scale of the index, and they gradually increase with an increase in time scale. The predicted value decreases with increase in lead time. Comparing the measured data with the predicted data from the model shows that the combined ARIMA–SVR model had higher prediction accuracy than the single ARIMA model and that the predicted results 1–2 months ahead show reasonably good agreement with the actual data.

Modeling Wood Density of Larch by Near-Infrared Spectrometry and Support Vector Machine

2011 ◽  
Vol 55-57 ◽  
pp. 433-438
Author(s):  
Ya Zhao Zhang ◽  
Yao Xiang Li ◽  
Hong Fu Zhang ◽  
Hui Juan Zhang ◽  
Pai Li
Keyword(s):  
Support Vector Machine ◽  
Infrared Spectroscopy ◽  
Wood Density ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Model Building ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Infrared Spectrometry ◽  
Support Vector

Model for predicting wood density of Larch was established using near-infrared spectroscopy (NIR) combined with support vector machine (SVM). A hundred and seventeen Larch samples were used in the study. Wood density of samples was measured according to standard test methods for physical and mechanical properties of wood. Support vector machines for regression (SVR) was used for model building. Radial basis function (RBF) was used as kernel function to establish a model for predicting wood density. For the train set, the coefficient of determination (R2) and the mean square error (MSE) were 0.8504 and 0.6460×10-3, while the R2 and MSE was 0.8520 and 0.4451×10-3, respectively, for the test set. Results showed that using SVM in near-infrared spectroscopy calibration could significantly improve the model performance in order to rapidly and accurately predict wood density.

scholarly journals Solar Irradiance Forecasting Using a Data-Driven Algorithm and Contextual Optimisation

2021 ◽  
Vol 12 (1) ◽  
pp. 134
Author(s):  
Paula Bendiek ◽  
Ahmad Taha ◽  
Qammer H. Abbasi ◽  
Basel Barakat
Keyword(s):  
Solar Irradiance ◽  
Contextual Information ◽  
Management Strategies ◽  
Energy Transition ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Short Term ◽  
Forecast Horizon

Solar forecasting plays a key part in the renewable energy transition. Major challenges, related to load balancing and grid stability, emerge when a high percentage of energy is provided by renewables. These can be tackled by new energy management strategies guided by power forecasts. This paper presents a data-driven and contextual optimisation forecasting (DCF) algorithm for solar irradiance that was comprehensively validated using short- and long-term predictions, in three US cities: Denver, Boston, and Seattle. Moreover, step-by-step implementation guidelines to follow and reproduce the results were proposed. Initially, a comparative study of two machine learning (ML) algorithms, the support vector machine (SVM) and Facebook Prophet (FBP) for solar prediction was conducted. The short-term SVM outperformed the FBP model for the 1- and 2- hour prediction, achieving a coefficient of determination (R2) of 91.2% in Boston. However, FBP displayed sustained performance for increasing the forecast horizon and yielded better results for 3-hour and long-term forecasts. The algorithms were optimised by further contextual model adjustments which resulted in substantially improved performance. Thus, DCF utilised SVM for short-term and FBP for long-term predictions and optimised their performance using contextual information. DCF achieved consistent performance for the three cities and for long- and short-term predictions, with an average R2 of 85%.

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