Multi-step rainfall forecasting using deep learning approach

Narejo, Sanam; Jawaid, Muhammad Moazzam; Talpur, Shahnawaz; Baloch, Rizwan; Pasero, Eros

doi:10.7717/peerj-cs.514

Cited by 24 publications

(9 citation statements)

References 50 publications

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“…Application of statistical models is on the increase in a number of areas such as prediction of precipitation ( [12,[38][39][40][41][42], river flow ( [43][44][45][46][47]), and temperature ( [48,49], and [50]). Some recent studies on modelling water quality using statistical methods include Wadkar and Kote [51], Li et al [52], García-Ávila et al [29], and De Santi et al [53]).…”

Section: Discussionmentioning

confidence: 99%

Multiple Statistical Model Ensemble Predictions of Residual Chlorine in Drinking Water: Applications of Various Deep Learning and Machine Learning Algorithms

Onyutha

2022

Journal of Environmental and Public Health

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Applicability of statistical models in predicting chlorine decay remains minimally explored. This study predicted residual chlorine using six deep learning and nine machine learning techniques. Suitability of multimodel ensembles (MMEs) including arithmetic mean of all the models (Ens1), average of the best three performing models (Ens2), and weighted mean of outputs from all the 15 models was investigated. A total of nine “goodness-of-fit” measures (such as distance correlation (rd) and Taylor skill score) were used to rank the models. The two best deep learning methods were the nonlinear autoregressive model with exogenous input (NARX) ( r d = 0.51 ) and feedforward backpropagation (FFB) ( r d = 0.61 ). The two best machine learning algorithms included random forests (RF) ( r d = 0.64 ) and Gaussian process regression (GPR) ( r d = 0.59 ). Eventually, Ens2 was obtained using RF, FFB, and GPR. Ens2 performed better than Ens1 and Ens3. The amount of variance explained by individual models and MMEs was over the ranges of 13–66% and 51–74%, respectively. Ens2 explained 74% of the total variance in observed residual chlorine. Remarkably, the appropriateness of the MMEs depends on the approach for combining model outputs, and the number of models considered. This study demonstrated the acceptability of statistical MMEs in predicting chlorine residual concentration in drinking water.

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Section: Discussionmentioning

confidence: 99%

Multiple Statistical Model Ensemble Predictions of Residual Chlorine in Drinking Water: Applications of Various Deep Learning and Machine Learning Algorithms

Onyutha

2022

Journal of Environmental and Public Health

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“…Finally, a comparative MSE and RMSE analysis of the CSMO-OKRR technique with recent methods is offered in Tab. 3 [25]. Fig.…”

Section: Experimental Validationmentioning

confidence: 98%

Spider Monkey Optimization with Statistical Analysis for Robust Rainfall Prediction

Ragab¹

2022

Computers, Materials &Amp; Continua

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Rainfall prediction becomes popular in real time environment due to the developments of recent technologies. Accurate and fast rainfall predictive models can be designed by the use of machine learning (ML), statistical models, etc. Besides, feature selection approaches can be derived for eliminating the curse of dimensionality problems. In this aspect, this paper presents a novel chaotic spider money optimization with optimal kernel ridge regression (CSMO-OKRR) model for accurate rainfall prediction. The goal of the CSMO-OKRR technique is to properly predict the rainfall using the weather data. The proposed CSMO-OKRR technique encompasses three major processes namely feature selection, prediction, and parameter tuning. Initially, the CSMO algorithm is employed to derive a useful subset of features and reduce the computational complexity. In addition, the KRR model is used for the prediction of rainfall based on weather data. Lastly, the symbiotic organism search (SOS) algorithm is employed to properly tune the parameters involved in it. A series of simulations are performed to demonstrate the better performance of the CSMO-OKRR technique with respect to different measures. The simulation results reported the enhanced outcomes of the CSMO-OKRR technique with existing techniques.

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“…CNNs were used first to classify if there was rain, then GRUs forecasted rain for Taiwan. Lastly, Narejo et al (2021) trained DBNs and CNNs for forecasts up to 8 hours for the Neuronica Laboratory, Italy.…”

Section: D Rainfall Forecastingmentioning

confidence: 99%

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

Sit¹,

Demiray²,

Demir³

2022

Preprint

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With technological enhancements, the volume, velocity, and variety (3Vs) of the raw digital Earth data have increased in recent years. Due to the increased availability of computer resources and the growing popularity of deep learning applications, this data has been a crucial source for data-driven studies that have transformed the fields of climate and earth science. One of the critical data sources is precipitation supporting climate and earth science studies on modeling, forecasting, and preparedness for extreme events (i.e., floods, droughts, pollution transport). In this study, we worked on an extensive review of manuscripts focusing on use of deep learning methods to tackle challenges either to improve the quality or extrapolate (forecast) rainfall datasets. The purpose of this study is to summarize the most recent developments in deep learning approaches for forecasting rainfall or improving rainfall datasets, as well as highlighting issues, shortcomings, and open questions with insightful recommendations for future directions.

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Multi-step rainfall forecasting using deep learning approach

Cited by 24 publications

References 50 publications

Multiple Statistical Model Ensemble Predictions of Residual Chlorine in Drinking Water: Applications of Various Deep Learning and Machine Learning Algorithms

Multiple Statistical Model Ensemble Predictions of Residual Chlorine in Drinking Water: Applications of Various Deep Learning and Machine Learning Algorithms

Spider Monkey Optimization with Statistical Analysis for Robust Rainfall Prediction

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

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