Neural-network approach to ground-based passive microwave estimation of precipitation intensity and extinction

Marzano, Frank S.; Fionda, E.; Ciotti, P.

doi:10.1016/j.jhydrol.2005.11.042

Cited by 30 publications

(21 citation statements)

References 37 publications

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“…Nevertheless, for water resources planning purposes, a long-term rainfall series is needed in many hydrological and simulation models (Tantanee et al 2005;Venkata Ramana et al 2013). There have been many attempts to address the hydrological processes in general and the quantitative precipitation forecasting in particular through different techniques including numerical weather prediction models and remote sensing observations (Yates et al 2000;Ganguly and Bras 2003;Diomede et al 2008;He et al 2013), statistical models (Chu and He 1994;Chan and Shi 1999;DelSole and Shukla 2002;Munot and Kumar 2007;Li and Zeng 2008;Nayagam et al 2008), and soft computing methods including artificial neural network (ANN), self-organizing map (SOM), support vector machine (SVM), fuzzy inference system and extreme learning machine (ELM) (French et al 1992;Navone and Ceccatto 1994;Pongracz et al 2001;Freiwan and Cigizoglu 2005;Marzano et al 2006;Kalteh and Berndtsson 2007;El-Shafie et al 2011;Chen et al 2015;Gholami et al 2015;Taormina and Chau 2015). For example, an ANN model for forecasting rainfall intensity fields at a lead-time of 1 h was applied by French et al (1992).…”

Section: Introductionmentioning

confidence: 99%

“…Pongracz et al (2001) used fuzzy inference for monthly rainfall forecasting. Marzano et al (2006) compared the accuracy of ANN approach with the previously developed regression methods in estimating precipitation intensity. El-Shafie et al (2011) developed adaptive neuro-fuzzy inference system and ANN models for rainfall forecasting in Klang River, Malaysia.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Monthly Precipitation Forecasting Using Artificial Neural Network and Singular Spectrum Analysis Conjunction Models

Kalteh

2017

INAE Lett

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Precipitation is a complex atmospheric process with both temporal and spatial variations which make it difficult to forecast. Several models have been developed to forecast precipitation. This study investigates the accuracy of artificial neural network (ANN) and singular spectrum analysis (SSA) conjunction model in monthly precipitation forecasting. Here, SSA is used to preprocess raw input signals to provide data of high quality to ANN. The conjunction model is obtained by combining two methods, ANN and SSA, and compared with the single/regular ANN. In SSA, the window length and the optimum number of components must be determined. For this purpose, several tentative models, one for each of the available component numbers, are developed, and the related component number of the model with best performance is considered as the optimum number of components. If the selected optimum number is less or equal to the chosen window length which in turn indicates the adequacy of the chosen window length. Monthly precipitation data from one rain gauge station, Ponel station, in northern Iran, is used in the study. The root mean square error (RMSE), correlation coefficient (R) and coefficient of efficiency (CE) statistics are used as the comparing criteria. The comparison of the results reveals that the conjunction models could increase the forecast accuracy of the ANN model in monthly precipitation forecasting. It is found that the conjunction model with RMSE ¼ 52:257, R ¼ 0:858 and CE ¼ 0:731 in the validation/testing period is superior in forecasting monthly precipitations than the most accurate ANN model with RMSE ¼ 91:096, R ¼ 0:444 and CE ¼ 0:183, respectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced Monthly Precipitation Forecasting Using Artificial Neural Network and Singular Spectrum Analysis Conjunction Models

Kalteh

2017

INAE Lett

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“…Among these hybrid models, the neural network (NN) models, such as the NF (neuro-fuzzy) and ANN, are the most popularly utilised sub-models for signal forecast due to their capabilities of effectively learning complex and nonlinear relationships (Maier et al, 2010). The ANN model has been commonly used in hydrological signal forecasts by a number of researchers (French et al, 1992;Jain et al, 1999;ASCE, 2000;Cigizoglu, 2005;Marzano et al, 2006;Zou et al, 2010). The NF model has been introduced and successfully used in the hydrological sciences in recent years (Nayak et al, 2004(Nayak et al, , 2005Kisi, 2005;Chang and Chang, 2006).…”

Section: J-s Yang Et Al: Multi-step-ahead Predictor Design For Effmentioning

confidence: 99%

Multi-step-ahead predictor design for effective long-term forecast of hydrological signals using a novel wavelet neural network hybrid model

Yang

Liu

2013

Hydrol. Earth Syst. Sci.

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Abstract. In order to increase the accuracy of serialpropagated long-range multi-step-ahead (MSA) prediction, which has high practical value but also great implementary difficulty because of huge error accumulation, a novel wavelet neural network hybrid model -CDW-NN -combining continuous and discrete wavelet transforms (CWT and DWT) and neural networks (NNs), is designed as the MSA predictor for the effective long-term forecast of hydrological signals. By the application of 12 types of hybrid and pure models in estuarine 1096-day river stages forecasting, the different forecast performances and the superiorities of CDW-NN model with corresponding driving mechanisms are discussed. One type of CDW-NN model, CDW-NF, which uses neuro-fuzzy as the forecast submodel, has been proven to be the most effective MSA predictor for the prominent accuracy enhancement during the overall 1096-day long-term forecasts. The special superiority of CDW-NF model lies in the CWT-based methodology, which determines the 15-day and 28-day prior data series as model inputs by revealing the significant short-time periodicities involved in estuarine river stage signals. Comparing the conventional single-stepahead-based long-term forecast models, the CWT-based hybrid models broaden the prediction range in each forecast step from 1 day to 15 days, and thus reduce the overall forecasting iteration steps from 1096 steps to 74 steps and finally create significant decrease of error accumulations. In addition, combination of the advantages of DWT method and neuro-fuzzy system also benefits filtering the noisy dynamics in model inputs and enhancing the simulation and forecast ability for the complex hydro-system.

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“…Freiwan and investigated the potential of feed forward ANN technique in prediction of monthly precipitation amount. Marzano et al (2006) compared the accuracy of ANN approach with the previously developed regression techniques. It is important to say that accurate precipitation forecasting is very difficult because of complexity of the physical processes involved of precipitation.…”

Section: Introductionmentioning

confidence: 99%

Wavelet and neuro-fuzzy conjunction model for precipitation forecasting

Partal

Kişi

2007

Journal of Hydrology

272

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Neural-network approach to ground-based passive microwave estimation of precipitation intensity and extinction

Cited by 30 publications

References 37 publications

Enhanced Monthly Precipitation Forecasting Using Artificial Neural Network and Singular Spectrum Analysis Conjunction Models

Enhanced Monthly Precipitation Forecasting Using Artificial Neural Network and Singular Spectrum Analysis Conjunction Models

Multi-step-ahead predictor design for effective long-term forecast of hydrological signals using a novel wavelet neural network hybrid model

Wavelet and neuro-fuzzy conjunction model for precipitation forecasting

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