Prediction of Flood Discharge Using Hybrid PSO-SVM Algorithm in Barak River Basin

Samantaray, Sandeep; Sahoo, Abinash; Agnihotri, Ankita

doi:10.1016/j.mex.2023.102060

Cited by 35 publications

(8 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This research shows that ANIFS is very good for predicting volatile data and spikes, such as rainfall data. Several studies have produced the same conclusions about the ANFIS method (Li et al, 2023;Saleh et al, 2023;Samantaray et al, 2023;Sangar et al, 2024).…”

Section: Rainfall Modeling Using the Anfis Methodsmentioning

confidence: 73%

Rainfall Forecasting Using an Adaptive Neuro-Fuzzy Inference System with a Grid Partitioning Approach to Mitigating Flood Disasters

Fauzi,

Erlinda,

Arum

2024

JTAM

View full text Add to dashboard Cite

Hydrometeorological disasters are one of the disasters that often occur in big cities like Semarang. Hydrometeorological disasters that often occur are floods caused by high-intensity rainfall in the area. Early mitigation needs to be done by knowing about future rain. Rainfall data in Semarang City fluctuates, so the Adaptive Neuro-Fuzzy Inference System (ANFIS) method approach is very appropriate. This research will use the Grid Partitioning (GP) approach to produce more accurate forecasting. The data used in this research is daily rainfall observation data from the Meteorology Climatology Geophysics Agency (BMKG). The membership functions used are Gaussian and Generalized Bell. The two membership functions will be compared based on the RMSE and MAPE values to get the best one. The data used in this research is daily rainfall data. Rainfall in Semarang City every month experiences anomalies, which can result in flood disasters. The ANFIS-GP method with a Gaussian membership function is the best, with an RMSE value of 0.0898 and a MAPE of 5.2911. Based on the forecast results for the next thirty days, a rainfall anomaly of 102.53 mm on the thirtieth day could cause a flood disaster.

show abstract

Section: Rainfall Modeling Using the Anfis Methodsmentioning

confidence: 73%

Rainfall Forecasting Using an Adaptive Neuro-Fuzzy Inference System with a Grid Partitioning Approach to Mitigating Flood Disasters

Fauzi,

Erlinda,

Arum

2024

JTAM

View full text Add to dashboard Cite

show abstract

“…Hybrid models [10,26], integrating AI and non-linear time series models [27], k-nearest neighbor regression [23], Particle Swarm Optimization-support vector machine (PSO-SVM) [28], Particle Swarm Optimization-long short-term memory PSO-LSTM [29], CEEMDAN-PSO-ELM [30], support vector machine-Particle Swarm Optimization (SVM-PSO) [31], wavelet-autoregressive models [32], wavelet-LSTM [33], etc. ;…”

Section: (D)mentioning

confidence: 99%

“…Various methods can predict future time series behavior, from classical decomposition models-which emphasize trend, seasonality, and random variation to Box-Jenkins methods [8,10,[38][39][40], exponential smoothing [9], and different types of AI [12,[20][21][22][23][24][25] and hybrid models [28][29][30][31][32][33].…”

mentioning

confidence: 99%

Forecasting the River Water Discharge by Artificial Intelligence Methods

Bărbulescu,

Zhen

2024

Water

View full text Add to dashboard Cite

The management of water resources must be based on accurate models of the river discharge in the context of the water flow alteration due to anthropic influences and climate change. Therefore, this article addresses the challenge of detecting the best model among three artificial intelligence techniques (AI)—backpropagation neural networks (BPNN), long short-term memory (LSTM), and extreme learning machine (ELM)—for the monthly data series discharge of the Buzău River, in Romania. The models were built for three periods: January 1955–September 2006 (S1 series), January 1955–December 1983 (S2 series), and January 1984–December 2010 (S series). In terms of mean absolute error (MAE), the best performances were those of ELM on both Training and Test sets on S2, with MAETraining = 5.02 and MAETest = 4.01. With respect to MSE, the best was LSTM on the Training set of S2 (MSE = 60.07) and ELM on the Test set of S2 (MSE = 32.21). Accounting for the R2 value, the best model was LSTM on S2 (R2Training = 99.92%, and R2Test = 99.97%). ELM was the fastest, with 0.6996 s, 0.7449 s, and 0.6467 s, on S, S1, and S2, respectively.

show abstract

“…The model's prediction accuracy is evaluated using root mean square error (RMSE), mean absolute error (MAE), standard deviation (SD) and correlation coefficient (R). The calculation formula is provided in Equations ( 34)-( 37) [46][47][48]. RMSE and SD assess the accuracy and stability of the model, respectively.…”

Section: Calculation Procedures Of Arima-anfis-woa Hybrid Model and E...mentioning

confidence: 99%

Vibration Prediction and Evaluation System of the Pumping Station Based on ARIMA–ANFIS–WOA Hybrid Model and D-S Evidence Theory

Wang

Zhang

Yin

2023

Water

View full text Add to dashboard Cite

Research on the vibration response prediction and safety early warning is of great significance to the operation and management of pumping station engineering. In the current research, a hybrid prediction method was proposed to predict vibration responses of the pumping station based on a single model of autoregressive integrated moving average (ARIMA), a combined model of the adaptive network-based fuzzy inference system (ANFIS) and whale optimization algorithm (WOA). The performance of the developed models was studied based on the effective stress vibration data of the blades in a shaft tubular pumping station. Then, the D-S evidence theory was adopted to perform safety early warning of the operation state by integrating the displacement, velocity, acceleration and stress indicators of the vibration responses of the pumping station. The research results show that the proposed prediction model ARIMA–ANFIS–WOA exhibited better accuracy in obtaining both linear and nonlinear characteristics of vibration data than the single prediction model and hybrid model with different optimization algorithms. The D-S evidence fusion results quantitatively demonstrate the safe operation state of the pumping station. This research could provide a scientific basis for the real-time analysis and processing of data in pumping station operation and maintenance systems.

show abstract

Prediction of Flood Discharge Using Hybrid PSO-SVM Algorithm in Barak River Basin

Cited by 35 publications

References 51 publications

Rainfall Forecasting Using an Adaptive Neuro-Fuzzy Inference System with a Grid Partitioning Approach to Mitigating Flood Disasters

Rainfall Forecasting Using an Adaptive Neuro-Fuzzy Inference System with a Grid Partitioning Approach to Mitigating Flood Disasters

Forecasting the River Water Discharge by Artificial Intelligence Methods

Vibration Prediction and Evaluation System of the Pumping Station Based on ARIMA–ANFIS–WOA Hybrid Model and D-S Evidence Theory

Contact Info

Product

Resources

About