Improving a stochastic multi-site generation model of daily rainfall using discrete wavelet de-noising: a case study to a semi-arid region

Hellassa, Sarah; Souag-Gamane, Doudja

doi:10.1007/s12517-018-4168-0

Cited by 6 publications

(3 citation statements)

References 84 publications

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“…Studies examining modern denoising methods using objective methodologies are scarce [24]. To restore phase data when there is significant speckle decorrelation noise, it is critical to unwrap and de-noise the data.…”

Section: Literature Surveymentioning

confidence: 99%

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Deep Learning Approach for the Detection of Noise Type in Ancient Images

Pawar¹,

Ainapure²,

Rashid³

et al. 2022

Sustainability

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Recent innovations in digital image capturing techniques facilitate the capture of stationary and moving objects. The images can be easily captured via high-end digital cameras, mobile phones and other handheld devices. Most of the time, captured images vary compared to actual objects. The captured images may be contaminated by dark, grey shades and undesirable black spots. There are various reasons for contamination, such as atmospheric conditions, limitations of capturing device and human errors. There are various mechanisms to process the image, which can clean up contaminated image to match with the original one. The image processing applications primarily require detection of accurate noise type which is used as input for image restoration. There are filtering techniques, fractional differential gradient and machine learning techniques to detect and identify the type of noise. These methods primarily rely on image content and spatial domain information of a given image. With the advancements in the technologies, deep learning (DL) is a technology that can be trained to mimic human intelligence to recognize various image patterns, audio files and text for accuracy. A deep learning framework empowers correct processing of multiple images for object identification and quick decision abilities without human interventions. Here Convolution Neural Network (CNN) model has been implemented to detect and identify types of noise in the given image. Over the multiple internal iterations to optimize the results, the identified noise is classified with 99.25% accuracy using the Proposed System Architecture (PSA) compared with AlexNet, Yolo V5, Yolo V3, RCNN and CNN. The proposed model in this study proved to be suitable for the classification of mural images on the basis of every performance parameter. The precision, accuracy, f1-score and recall of the PSA are 98.50%, 99.25%, 98.50% and 98.50%, respectively. This study contributes to the development of mural art recovery.

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Section: Literature Surveymentioning

confidence: 99%

“…Recall = TP FN + TP (24) F1 − Score = 2TP 2TP + FN + FP (25) where TP correctly predicts positive and is true, TN correctly predicts the negative and is true. Where FP predicts positive and it is false, and FN predicts negative and it is false.…”

Section: Noisy Imagesmentioning

confidence: 99%

Deep Learning Approach for the Detection of Noise Type in Ancient Images

Pawar¹,

Ainapure²,

Rashid³

et al. 2022

Sustainability

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“…2014;Zhang et al . 2018;Hellassa et al 2019). The semiparametric models simulate the occurrence and amount of precipitation through histograms adjusted to the data series, such as the LARS-WG model, produced by Semenov et al .…”

Section: Introductionmentioning

confidence: 99%

Modeling with Artificial Neural Networks to estimate daily precipitation in the Brazilian Legal Amazon

Pinheiro Gomes,

Progênio,

da Silva Holanda

2024

Clim Dyn

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Hydrological analyzes carried out from precipitation in the Legal Amazon (AMZ-L) are essential due to their importance in climate regulation, regional and global atmospheric circulation. However, in this region, there are limitations related to data series with short periods and many gaps, especially in the daily scale. Thus, to improve precipitation analyses, a non-parametric stochastic model based on Artificial Neural Networks (ANNs) was used to estimate daily precipitation in AMZ-L. For this, 22 rainfall stations were adopted over a period of 18 years (1998-2016) and with <1% missing data, which were organized considering the complete series and the seasonal periods (rainy and dry). The results obtained demonstrate the good capacity of the model to preserve the precipitation characteristics of the evaluated rainfall stations, mainly those with a more humid climate and with more frequent precipitations during the year, as is the case of those located in the Amazon Biome. However, in regions that suffer prolonged periods of drought, such as the Amazon-Cerrado Ecotone areas, the results were less satisfactory due to the greater recurrence of zeros in the historical series. The seasonal division into dry and rainy periods did not provide better estimates to the model, except for some rainfall gauge stations located at latitudes close to the equator.However, this study may support future research on the estimation of daily precipitation in the region.

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Spatiotemporal simulation of annual precipitation in the Urmia Lake basin

Faghih

Behmanesh

2023

Stoch Environ Res Risk Assess

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Improving a stochastic multi-site generation model of daily rainfall using discrete wavelet de-noising: a case study to a semi-arid region

Cited by 6 publications

References 84 publications

Deep Learning Approach for the Detection of Noise Type in Ancient Images

Deep Learning Approach for the Detection of Noise Type in Ancient Images

Modeling with Artificial Neural Networks to estimate daily precipitation in the Brazilian Legal Amazon

Spatiotemporal simulation of annual precipitation in the Urmia Lake basin

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