Multilayer perceptron-based predictive model using wavelet transform for the reconstruction of missing rainfall data

[1]  Jeill Oh,et al.  A multivariate decomposition–ensemble model for estimating long-term rainfall dynamics , 2022, Climate Dynamics.

[2]  Muhammed E. Akiner,et al.  Long-Term Rainfall Information Forecast by Utilizing Constrained Amount of Observation through Artificial Neural Network Approach , 2021 .

[3]  Rafael Luís Silva Dias,et al.  Estimation of missing hydrological data in monthly rainfall series using meteorological satellite data , 2021, Environmental Earth Sciences.

[4]  Shahfahad,et al.  Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches , 2020, Scientific Reports.

[5]  Chii-Chang Chen,et al.  Application of the deep learning for the prediction of rainfall in Southern Taiwan , 2019, Scientific Reports.

[6]  Vishal Singh,et al.  Data assimilation for constructing long-term gridded daily rainfall time series over Southeast Asia , 2019, Climate Dynamics.

[7]  Imed Riadh Farah,et al.  Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review , 2019, Applied Sciences.

[8]  Sultan Noman Qasem,et al.  Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate , 2019, Engineering Applications of Computational Fluid Mechanics.

[9]  G. A. Blackburn,et al.  Infilling Missing Data in Hydrology: Solutions Using Satellite Radar Altimetry and Multiple Imputation for Data-Sparse Regions , 2018, Water.

[10]  S. Ly,et al.  Assessment of Satellite Rainfall Estimates as a Pre-Analysis for Water Environment Analytical Tools: A Case Study for Tonle Sap Lake in Cambodia , 2018 .

[11]  Hyung-Sup Jung,et al.  Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea , 2017 .

[12]  T. Gan,et al.  Regional Flood Frequency Analysis using Support Vector Regression under historical and future climate , 2016 .

[13]  Jaya Kandasamy,et al.  Prediction of hydrological time-series using extreme learning machine , 2016 .

[14]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[15]  Tara N. Sainath,et al.  Deep Convolutional Neural Networks for Large-scale Speech Tasks , 2015, Neural Networks.

[16]  Athanasios G. Paliatsos,et al.  Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece , 2014 .

[17]  Enrico Zio,et al.  Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method , 2014, Digit. Signal Process..

[18]  Yan-Fang Sang,et al.  Improved Wavelet Modeling Framework for Hydrologic Time Series Forecasting , 2013, Water Resources Management.

[19]  Jan Adamowski,et al.  Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. , 2010 .

[20]  Lindell Ormsbee,et al.  Optimal functional forms for estimation of missing precipitation data. , 2009 .

[21]  Paulin Coulibaly,et al.  Comparison of neural network methods for infilling missing daily weather records , 2007 .

[22]  Wei-Zhen Lu,et al.  Forecasting of ozone level in time series using MLP model with a novel hybrid training algorithm , 2006 .

[23]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[24]  C. Willmott,et al.  Estimating continental and terrestrial precipitation averages from rain-gauge networks , 1994 .

[25]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[26]  Pedro León García Reinoso.  Imputación de Datos en Series de Precipitación Diaria Caso de Estudio Cuenca del Río Quindío , 2015 .

[27]  R. Teegavarapu.  Use of universal function approximation in variance-dependent surface interpolation method: An application in hydrology , 2007 .