A Novel Method to Water Level Prediction using RBF and FFA

Soleymani, Seyed Ahmad; Anisi, Mohammad Hossein; Hassan, Wan Haslina; Idris, Mohd Yamani Idna; Shamshirband, Shahaboddin; Noor, Noorzaily Mohamed; Ahmedy, Ismail

doi:10.1007/s11269-016-1347-1

Cited by 52 publications

(20 citation statements)

References 31 publications

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“…This motivated us to use the same concept for water level data too. In our proposed block sparse Bayesian learning method, BSBL-WSN, the input signal x is partitioned into concatenation of several non-overlapping blocks as shown in (1). By removing the baseline from each segment (i.e.…”

Section: A Bsbl-wsnmentioning

confidence: 99%

“…With WSN, several sensors are connected with each other and deployed in the side of the river. This not only lets the managers to understand any sudden changes in the river but also let them to generate a large database of river water levels that could be used to predict the upcoming floods [1]. However, continuous and real-time monitoring of the river requires frequent transmission of data which drains the limited energy of the sensors.…”

Section: Introductionmentioning

confidence: 99%

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Compressive Sensing for Remote Flood Monitoring

Abolghasemi

Anisi

2021

IEEE Sens. Lett.

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Although wireless sensor networks (WSNs) are considered as one of the prominent solutions for flood monitoring; however, the energy constraint nature of the sensors is still a technical challenge. In this paper, we tackle this problem by proposing a novel energy-efficient remote flood monitoring system, enabled by compressive sensing. The proposed approach compressively captures water level data using; i) a random block-based sampler, and ii) a gradient-based compressive sensing approach, at a very low rate, exploiting water level data variability over time. Through extensive experiments on real water-level dataset, we show that the number of packet transmissions as well as the size of packets are significantly reduced. The results also demonstrate significant energy reduction in sensing and transmission. Moreover, data reconstruction from compressed samples are of high quality with negligible degradation, compared to classic compression techniques, even at high compression rates.

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Section: A Bsbl-wsnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compressive Sensing for Remote Flood Monitoring

Abolghasemi

Anisi

2021

IEEE Sens. Lett.

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“…One can also conclude that Deep Neural Network (DNN) is a sub-category MLP of since a deep neural network is a type of Artificial Neural Network that can easily handle a huge number of hidden layers efficiently. This model is highly efficient in handling non-linearity and peak values of data and also for producing an accurate future prediction [21], [22]. So basically Multi-Layer Perception and Deep Neural Networks are almost similar models that use back-propagation except for the fact that DNN are deeper models than MLP which means that a DNN can handle up to thousands of hidden layers accurately and efficiently while in MLP we limit the hidden layers to fewer numbers as compared to DNN (but multiple layers).…”

Section: Multi-layer Perceptionmentioning

confidence: 99%

Annual and Non-Monsoon Rainfall Prediction Modelling Using SVR-MLP: An Empirical Study From Odisha

et al. 2020

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Rainfall is a natural demolishing phenomenon. On the other side, it also serves as a major source of water when conserved through proper channel. For this issue, estimation of rain fall is of at utmost importance. The present study employed on rain fall forecasting in annual as well as non-moon session in Odisha (India). The total annual rainfall and relative humidity data were collected from period 1991-2015 from Department of Forest and Environment Govt. of Odisha. Support Vector Regression and Multilayer perception implemented for prediction of maximum rainfall in annual and non-monsoon session. Input parameter like average temperature in month, wind velocity, humidity, and cloud cover was conceder for predicting rainfall in non-monsoon session. The performance of the results was measure with MSE (mean squared error), correlation coefficient, coefficient of efficiency and MAE (mean absolute error). The results of SVR were compared to those of MLP and simple regression technique. MLP being a computationally intensive method, SVR could be used as an efficient alternative for runoff and sediment yield prediction under comparable accuracy in predictions.SVR-MLP may be used as promising alternative forecasting tool for higher accuracy in forecasting and better generalization ability. INDEX TERMS Support vector regression (SVR), multi-layer perception, rainfall.

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“…SVR-FFA gives promising accuracy to forecast precipitation in the semi-arid province as compared to other models. Soleymani & Goudarzi (2016) developed an amalgam method comprising of Radial Basis Function and FFA for predicting the water level of Selangor River, Malaysia. The performance of RBF-FFA was examined using simulated and real-time water level data and found that RBF-FFA is a proficient technique for accurately predicting river water level.…”

Section: Introductionmentioning

confidence: 99%

Prediction of S12-MKII rainfall simulator experimental runoff data sets using hybrid PSR-SVM-FFA approaches

Samantaray

Ghose

2021

Journal of Water and Climate Change

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Effective prediction of runoff is a substantial feature for the successful management of hydrological phenomena in arid regions. The present research findings reveal that a rainfall simulator (RS) can be a valuable instrument to estimate runoff as the intensity of rainfall is modifiable in the course of an experimental process, which turns out to be of great advantage. The rainfall-runoff process is a complex physical phenomenon caused by the effect of various parameters. In this research, a new hybrid technique integrating PSR (phase space reconstruction) with FFA (firefly algorithm) and SVM (support vector machine) has gained recognition in various modelling investigations in contrast to the principle of empirical risk minimization through ANN practices. Outcomes of SVM are contrasted against SVM-FFA and PSR-SVM-FFA models. The improvements in NSE (Nash–Sutcliffe efficiency), RMSE (Root Mean Square Error), and WI (Willmott's Index) by PSR-SVM-FFA over SVM models specify that the prediction accuracy of the hybrid model is better. The established PSR-SVM-FFA model generates preeminent WI values that range from 0.97 to 0.98, while the SVM and SVM-FFA models encompass 0.93–0.95 and 0.96–0.97, respectively. The proposed PSR-SVM-FFA model gives more accurate results and error limiting up to 2–3%.

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A Novel Method to Water Level Prediction using RBF and FFA

Cited by 52 publications

References 31 publications

Compressive Sensing for Remote Flood Monitoring

Compressive Sensing for Remote Flood Monitoring

Annual and Non-Monsoon Rainfall Prediction Modelling Using SVR-MLP: An Empirical Study From Odisha

Prediction of S12-MKII rainfall simulator experimental runoff data sets using hybrid PSR-SVM-FFA approaches

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