scholarly journals A Hybrid Model for Streamflow Forecasting in the Basin of Euphrates

Water ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 80
Author(s):  
Huseyin Cagan Kilinc ◽  
Bulent Haznedar
Keyword(s):  
Linear Regression ◽  
Hybrid Model ◽  
Water Resource Management ◽  
River Flow ◽  
Short Term Memory ◽  
Hybrid Approach ◽  
Streamflow Forecasting ◽  
Flow Measurements ◽  
Benchmark Model ◽  
Daily Streamflow

River flow modeling plays a crucial role in water resource management and ensuring its sustainability. Therefore, in recent years, in addition to the prediction of hydrological processes through modeling, applicable and highly reliable methods have also been used to analyze the sustainability of water resources. Artificial neural networks and deep learning-based hybrid models have been used by scientists in river flow predictions. Therefore, in this study, we propose a hybrid approach, integrating long-short-term memory (LSTM) networks and a genetic algorithm (GA) for streamflow forecasting. The performance of the hybrid model and the benchmark model was taken into account using daily flow data. For this purpose, the daily river flow time series of the Beyderesi-Kılayak flow measurement station (FMS) from September 2000 to June 2019 and the data from Yazıköy from December 2000 to June 2018 were used for flow measurements on the Euphrates River in Turkey. To validate the performance of the model, the first 80% of the data were used for training, and the remaining 20% were used for the testing of the two FMSs. Statistical methods such as linear regression was used during the comparison process to assess the proposed method’s performance and to demonstrate its superior predictive ability. The estimation results of the models were evaluated with RMSE, MAE, MAPE, STD and R2 statistical metrics. The comparison of daily streamflow predictions results revealed that the LSTM-GA model provided promising accuracy results and mainly presented higher performance than the benchmark model and the linear regression model.

A New Semantic Relations-Based Hybrid Approach for Implicit Aspect Identification in Sentiment Analysis

2020 ◽  
Vol 19 (03) ◽  
pp. 2050019
Author(s):  
Hajar El Hannach ◽  
Mohammed Benkhalifa
Keyword(s):  
Sentiment Analysis ◽  
Hybrid Model ◽  
Opinion Mining ◽  
Short Term Memory ◽  
Hybrid Approach ◽  
Active Phase ◽  
Semantic Relations ◽  
Term Extraction ◽  
Supervised Classifiers ◽  
Restaurant Reviews

Within the next few years, sentiment analysis or opinion mining is set to become an important component of real-world applications for product manufacturers, e-commerce companies, and potential customers. Sentiment analysis deals with the computational assessment of people’s opinions apparent or hidden within the text according to three levels: document, sentence and aspect levels. The aspect-level is increasingly becoming an active phase of sentiment analysis. At this level, the aim is to determine the hidden target of opinion represented in datasets, known as aspect term identification. This paper proposes an original hybrid model combining semantic relations and frequency-based approach with supervised classifiers for implicit aspect identification (IAI). The proposed approach is directed towards improving the F1-performances for traditional supervised classifiers commonly used in this field based on eager and lazy learning, and deep learning technique using long short-term memory whit attention mechanism applied for IAI. Particularly, this work addresses aspect term extraction and aggregation, the two sub-tasks of IAI, involving adjectives and verbs. The effects of this approach are empirically examined on multiple datasets of electronic products and restaurant reviews with multiple aspect granularity levels. Comparing this method with similar approaches clearly shows the benefits of this method: (i) the use of an appropriately selected WordNet semantic relations of adjectives and verbs that significantly helps classifiers for IAI. (ii) Using the hybrid model helps classifiers better handle these selected WordNet semantic relations and therefore deal better with IAI.

scholarly journals Ensemble streamflow forecasting based on variational mode decomposition and long short term memory

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Xiaomei Sun ◽  
Haiou Zhang ◽  
Jian Wang ◽  
Chendi Shi ◽  
Dongwen Hua ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Streamflow Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Historical Changes ◽  
Daily Streamflow ◽  
Mode Decomposition ◽  
Long Short Term Memory

AbstractReliable and accurate streamflow forecasting plays a vital role in the optimal management of water resources. To improve the stability and accuracy of streamflow forecasting, a hybrid decomposition-ensemble model named VMD-LSTM-GBRT, which is sensitive to sampling, noise and long historical changes of streamflow, was established. The variational mode decomposition (VMD) algorithm was first applied to extract features, which were then learned by several long short-term memory (LSTM) networks. Simultaneously, an ensemble tree, a gradient boosting tree for regression (GBRT), was trained to model the relationships between the extracted features and the original streamflow. The outputs of these LSTMs were finally reconstructed by the GBRT model to obtain the forecasting streamflow results. A historical daily streamflow series (from 1/1/1997 to 31/12/2014) for Yangxian station, Han River, China, was investigated by the proposed model. VMD-LSTM-GBRT was compared with respect to three aspects: (1) feature extraction algorithm; ensemble empirical mode decomposition (EEMD) was used. (2) Feature learning techniques; deep neural networks (DNNs) and support vector machines for regression (SVRs) were exploited. (3) Ensemble strategy; the summation strategy was used. The results indicate that the VMD-LSTM-GBRT model overwhelms all other peer models in terms of the root mean square error (RMSE = 36.3692), determination coefficient (R2 = 0.9890), mean absolute error (MAE = 9.5246) and peak percentage threshold statistics (PPTS(5) = 0.0391%). The addressed approach based on the memory of long historical changes with deep feature representations had good stability and high prediction precision.

Deep-learning GIS hybrid approach in precipitation modeling based on spatio-temporal variables in the coastal zone of Turkey

2020 ◽  
Vol 81 ◽  
pp. 149-165
Author(s):  
H Apaydin ◽  
MT Sattari
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Water Resource Management ◽  
Short Term Memory ◽  
Hybrid Approach ◽  
Support Vector ◽  
Monthly Precipitation ◽  
Artificial Intelligence Methods ◽  
Temporal Variables ◽  
Spatio Temporal

It is clearly known that precipitation is essential for fauna and flora. Studies have shown that location and temporal factors have an effect on precipitation. Accurate prediction of precipitation is very important for water resource management, and artificial intelligence methods are frequently used to make such predictions. In this study, the deep-learning and geographic information system (GIS) hybrid approach based on spatio-temporal variables was applied in order to model the amount of precipitation on Turkey’s coastline. Information about latitude, longitude, altitude, distance to the sea, and aspect was taken from meteorological stations, and these factors were utilized as spatial variables. The change in monthly precipitation was taken into account as a temporal variable. Artificial intelligence methods such as Gaussian process regression, support vector regression, the Broyden-Fletcher-Goldfarb-Shanno artificial neural network, M5, random forest, and long short-term memory (LSTM) were used. According to the results of the study, in which different input variable alternatives were also evaluated, LSTM was the most successful method for predicting precipitation with a value of 0.93 R. The study shows that the amount of precipitation can be estimated and a distribution map can be drawn by using spatio-temporal data and the deep-learning and GIS hybrid method at points where the measurement is not performed.

scholarly journals Evaluation of random forests and Prophet for daily streamflow forecasting

2018 ◽  
Vol 45 ◽  
pp. 201-208 ◽  
Author(s):  
Georgia A. Papacharalampous ◽  
Hristos Tyralis
Keyword(s):  
Linear Regression ◽  
Random Forests ◽  
Multiple Linear Regression Model ◽  
Streamflow Forecasting ◽  
Daily Streamflow ◽  
General Terms ◽  
Forecasting Methods ◽  
Naive Method ◽  
The Us ◽  
Made In

Abstract. We assess the performance of random forests and Prophet in forecasting daily streamflow up to seven days ahead in a river in the US. Both the assessed forecasting methods use past streamflow observations, while random forests additionally use past precipitation information. For benchmarking purposes we also implement a naïve method based on the previous streamflow observation, as well as a multiple linear regression model utilizing the same information as random forests. Our aim is to illustrate important points about the forecasting methods when implemented for the examined problem. Therefore, the assessment is made in detail at a sufficient number of starting points and for several forecast horizons. The results suggest that random forests perform better in general terms, while Prophet outperforms the naïve method for forecast horizons longer than three days. Finally, random forests forecast the abrupt streamflow fluctuations more satisfactorily than the three other methods.

scholarly journals Streamflow forecasting based on the hybrid decomposition-ensemble model 

2021 ◽  
Author(s):  
Xiaomei Sun ◽  
Haiou Zhang ◽  
Jian Wang ◽  
Chendi Shi ◽  
Dongwen Hua ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Gradient Boosting ◽  
Support Vector ◽  
Ensemble Model ◽  
Streamflow Forecasting ◽  
Historical Changes ◽  
Daily Streamflow ◽  
Mode Decomposition ◽  
Ensemble Strategy ◽  
Hybrid Decomposition

Abstract Reliable and accurate streamflow forecasting plays a vital role in the optimal management of water resources. To improve the stability and accuracy of streamflow forecasting, a hybrid decomposition-ensemble model named VMD-LSTM-GBRT, which is sensitive to sampling, noise and long historical changes of streamflow, was established. The variational mode decomposition (VMD) algorithm was first applied to extract features, which were then learned by several long short-term memory (LSTM) networks. Simultaneously, an ensemble tree, a gradient boosting tree for regression (GBRT), was trained to model the relationships between the extracted features and the original streamflow. The outputs of these LSTMs were finally reconstructed by the GBRT model to obtain the forecasting streamflow results. A historical daily streamflow series (from 1/1/1997 to 31/12/2014) for Yangxian station, Han River, China, was investigated by the proposed model. VMD-LSTM-GBRT was compared with respect to three aspects: (1) Feature extraction algorithm; ensemble empirical mode decomposition (EEMD) was used. (2) Feature learning techniques; deep neural networks (DNNs) and support vector machines for regression (SVRs) were exploited. (3) Ensemble strategy; the summation strategy was used. The results indicate that the VMD-LSTM-GBRT model overwhelms all other peer models in terms of the root mean square error (RMSE=36.3692), determination coefficient (R 2 =0.9890), mean absolute error (MAE=9.5246) and peak percentage threshold statistics (PPTS(5)=0.0391%). The addressed approach based on the memory of long historical changes with deep feature representations had good stability and high prediction precision.

scholarly journals Climate-driven Model Based on Long Short-Term Memory and Bayesian Optimization for Multi-day-ahead Daily Streamflow Forecasting

2021 ◽  
Author(s):  
Yani Lian ◽  
Jungang Luo ◽  
Jingmin Wang ◽  
Ganggang Zuo
Keyword(s):  
Climate Change ◽  
Short Term Memory ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Streamflow Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Daily Streamflow ◽  
Runoff Forecasting ◽  
Long Short Term Memory

Abstract Many previous studies have developed decomposition and ensemble models to improve runoff forecasting performance. However, these decomposition-based models usually introduce large decomposition errors into the modeling process. Since the variation in runoff time series is greatly driven by climate change, many previous studies considering climate change focused on only rainfall-runoff modeling, with few meteorological factors as input. Therefore, a climate-driven streamflow forecasting (CDSF) framework was proposed to improve the runoff forecasting accuracy. This framework is realized using principal component analysis (PCA), long short-term memory (LSTM) and Bayesian optimization (BO) referred to as PCA-LSTM-BO. To validate the effectiveness and superiority of the PCA-LSTM-BO method with which one autoregressive LSTM model and two other CDSF models based on PCA, BO, and either support vector regression (SVR) or, gradient boosting regression trees (GBRT), namely, PCA-SVR-BO and PCA-GBRT-BO, respectively, were compared. A generalization performance index based on the Nash-Sutcliffe efficiency (NSE), called the GI(NSE) value, is proposed to evaluate the generalizability of the model. The results show that (1) the proposed model is significantly better than the other benchmark models in terms of the mean square error (MSE<=185.782), NSE>=0.819, and GI(NSE) <=0.223 for all the forecasting scenarios; (2) the PCA in the CDSF framework can improve the forecasting capacity and generalizability; (3) the CDSF framework is superior to the autoregressive LSTM models for all the forecasting scenarios; and (4) the GI(NSE) value is demonstrated to be effective in selecting the optimal model with a better generalizability.

scholarly journals Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 256
Author(s):  
Pengfei Han ◽  
Han Mei ◽  
Di Liu ◽  
Ning Zeng ◽  
Xiao Tang ◽  
...  
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Short Term Memory ◽  
Hot Spot ◽  
Low Cost ◽  
Field Evaluation ◽  
Pollution Monitoring ◽  
Coefficient Of Determination ◽  
Air Pollution Monitoring ◽  
National Monitoring

Pollutant gases, such as CO, NO2, O3, and SO2 affect human health, and low-cost sensors are an important complement to regulatory-grade instruments in pollutant monitoring. Previous studies focused on one or several species, while comprehensive assessments of multiple sensors remain limited. We conducted a 12-month field evaluation of four Alphasense sensors in Beijing and used single linear regression (SLR), multiple linear regression (MLR), random forest regressor (RFR), and neural network (long short-term memory (LSTM)) methods to calibrate and validate the measurements with nearby reference measurements from national monitoring stations. For performances, CO > O3 > NO2 > SO2 for the coefficient of determination (R2) and root mean square error (RMSE). The MLR did not increase the R2 after considering the temperature and relative humidity influences compared with the SLR (with R2 remaining at approximately 0.6 for O3 and 0.4 for NO2). However, the RFR and LSTM models significantly increased the O3, NO2, and SO2 performances, with the R2 increasing from 0.3–0.5 to >0.7 for O3 and NO2, and the RMSE decreasing from 20.4 to 13.2 ppb for NO2. For the SLR, there were relatively larger biases, while the LSTMs maintained a close mean relative bias of approximately zero (e.g., <5% for O3 and NO2), indicating that these sensors combined with the LSTMs are suitable for hot spot detection. We highlight that the performance of LSTM is better than that of random forest and linear methods. This study assessed four electrochemical air quality sensors and different calibration models, and the methodology and results can benefit assessments of other low-cost sensors.

Hybrid approach combining EMD, ARIMA and monte carlo for multi-step ahead medical tourism forecasting

2021 ◽  
pp. 1-17
Author(s):  
Nuzhat Fatema ◽  
H Malik ◽  
Mutia Sobihah Binti Abd Halim
Keyword(s):  
Monte Carlo ◽  
Hybrid Model ◽  
Medical Tourism ◽  
Moving Average ◽  
Hybrid Approach ◽  
Mode Decomposition ◽  
Hybrid Forecasting ◽  
Input Variables ◽  
Intelligent Approach

This paper proposed a hybrid intelligent approach based on empirical mode decomposition (EMD), autoregressive integrated moving average (ARIMA) and Monte Carlo simulation (MCS) methods for multi-step ahead medical tourism (MT) forecasting using explanatory input variables based on two decade real-time recorded database. In the proposed hybrid model, these variables are 1st extracted then medical tourism is forecasted to perform the long term as well as the short term goal and planning in the nation. The multi-step ahead medical tourism is forecasted recursively, by utilizing the 1st forecasted value as the input variable to generate the next forecasting value and this procedure is continued till third step ahead forecasted value. The proposed approach firstly tested and validated by using international tourism arrival (ITA) dataset then proposed approach is implemented for forecasting of medical tourism arrival in nation. In order to validate the performance and accuracy of the proposed hybrid model, a comparative analysis is performed by using Monte Carlo method and the results are compared. Obtained results shows that the proposed hybrid forecasting approach for medical tourism has outperformance characteristics.

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