scholarly journals Daily rainfall estimates considering seasonality from a MODWT-ANN hybrid model

2021 ◽  
Vol 69 (1) ◽  
pp. 13-28
Author(s):  
Evanice Pinheiro Gomes ◽  
Claudio José Cavalcante Blanco
Keyword(s):  
Hybrid Model ◽  
Daily Rainfall ◽  
Data Series ◽  
Discrete Wavelet ◽  
Historical Series ◽  
Daily Scale ◽  
Homogeneous Regions ◽  
Climate Prediction Center ◽  
Rainfall Estimates

AbstractAnalyses based on precipitation data may be limited by the quality of the data, the size of the available historical series and the efficiency of the adopted methodologies; these factors are especially limiting when conducting analyses at the daily scale. Thus, methodologies are sought to overcome these barriers. The objective of this work is to develop a hybrid model through the maximum overlap discrete wavelet transform (MODWT) to estimate daily rainfall in homogeneous regions of the Tocantins-Araguaia Hydrographic Region (TAHR) in the Amazon (Brazil). Data series from the Climate Prediction Center morphing (CMORPH) satellite products and rainfall data from the National Water Agency (ANA) were divided into seasonal periods (dry and rainy), which were adopted to train the model and for model forecasting. The results show that the hybrid model had a good performance when forecasting daily rainfall using both databases, indicated by the Nash–Sutcliffe efficiency coefficients (0.81–0.95), thus, the hybrid model is considered to be potentially useful for modelling daily rainfall.

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

2013 ◽  
Vol 10 (7) ◽  
pp. 9239-9269 ◽  
Author(s):  
J.-S. Yang ◽  
S.-P. Yu ◽  
G.-M. Liu
Keyword(s):  
Hybrid Model ◽  
Wavelet Transforms ◽  
Great Difficulty ◽  
Single Step ◽  
Data Series ◽  
Discrete Wavelet ◽  
River Stage ◽  
Term Forecast ◽  
Neuro Fuzzy

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

scholarly journals APROVEITAMENTO DA ÁGUA DE CHUVA PARA IRRIGAÇÃO DE ESPAÇOS PÚBLICOS URBANOS ABERTOS: O CASO DA PRAÇA SANTOS DUMONT NO MUNICÍPIO DE UMUARAMA - PR

Irriga ◽  
2018 ◽  
Vol 23 (1) ◽  
pp. 96-107
Author(s):  
Janaina Conversani Botari ◽  
Giuliani Do Prado
Keyword(s):  
Daily Rainfall ◽  
Series Data ◽  
Water Volume ◽  
Data Series ◽  
Return Periods ◽  
Reservoir Volume ◽  
Historical Series ◽  
Reservoir Design ◽  
Public Areas ◽  
E Mail

APROVEITAMENTO DA ÁGUA DE CHUVA PARA IRRIGAÇÃO DE ESPAÇOS PÚBLICOS URBANOS ABERTOS: O CASO DA PRAÇA SANTOS DUMONT NO MUNICÍPIO DE UMUARAMA - PR  JANAÍNA CONVERSANI BOTARI1 E Giuliani do Prado2 1 Arquiteta, Mestre, Departamento de Tecnologia, Universidade Estadual de Maringá, Umuarama - PR, CEP 87.506-370, e-mail: [email protected] Eng. Agrônomo, Doutor, Departamento de Engenharia Agrícola, Universidade Estadual de Maringá, Cidade Gaúcha - PR, CEP 87.820-000, e-mail: [email protected]  1 RESUMO O trabalho objetivou analisar séries históricas de chuvas da região de Umuarama/PR para definir tempos de retorno com fins ao dimensionamento de reservatórios para aproveitamento de água da chuva para irrigação em praças. O dimensionamento do reservatório foi realizado para a Praça Santos Dumont, localizada na cidade de Umuarama/PR. Séries históricas de 36 anos (1977 a 2012) de precipitações diárias de três estações meteorológicas foram empregadas para determinar a precipitação média diária para o local em estudo. A demanda média diária de água para irrigação foi definida com os dados de evapotranspiração de referência da região com base nos anos de 2014 e 2015. A área irrigada de 506 m2 foi estabelecida a partir da disponibilidade (1482 mm) e da demanda (1318 mm) total anual média de água. Ao simular a entrada e saída de água diária, entre os anos de 1977 e 2012, foi estabelecido um reservatório com volume 200 m3 que, ao longo do tempo permanece com 80,1% do seu volume de água. Simulando o volume do reservatório com as séries históricas mensal, quinzenal e decendial, respectivamente, observou-se que esse volume de reservatório é obtido com tempos de retornos de 11,8, 5,5 e 4,3 anos. Palavras-chave: dimensionamento de reservatório, série histórica, tempo de retorno  BOTARI, J. C.; Prado, GRAINWATER STORAGE AND ITS USE FOR IRRIGATION IN OPEN URBAN PUBLIC AREAS: CASE STUDY OF SANTOS DUMONT SQUARE IN UMUARAMA COUNTY, PARANÁ STATE, BRAZIL  2 ABSTRACT This study aimed at analyzing rainfall historical series data of Umuarama County in Paraná State, Brazil, in order to determine return periods for dimension of reservoirs to store  rainwater for irrigation in squares. The reservoir design was made for  Santos Dumont Square, located in Umuarama County in Paraná State. Daily rainfall historical series data covering  36 years (1977 to 2012) from three weather stations were used to estimate the average daily rainfall. The average daily water demand for irrigation was defined with reference evapotranspiration data from the region, based on the years 2014 and 2015. The irrigated area of 506 m2 was established based on  the total annual average water available (1482 mm) and demanded (1318 mm). By simulating the daily input and output of water, between 1977 and 2012, it was computed a reservoir with 200 m3 volume, which remains with 80.1% of its water volume over time. Simulating the reservoir volume with the daily historical series data of 30, 15 and 10 days, respectively, it was observed that this reservoir volume is obtained with return periods of 11.8, 5.5, and 4.3 years. Keywords: reservoir design, historical data series, return period.

scholarly journals Distribution of rainfall probability in the Tapajos River Basin, Amazonia, Brazil

2019 ◽  
Vol 14 (3) ◽  
pp. 1
Author(s):  
Vanessa Conceição dos Santos ◽  
Claudio Blanco ◽  
José Francisco de Oliveira Júnior
Keyword(s):  
River Basin ◽  
Transition Probabilities ◽  
Daily Rainfall ◽  
Probability Of Occurrence ◽  
Rainfall Occurrence ◽  
Order Markov Chain ◽  
Historical Series ◽  
The Matrix ◽  
Geographical Position ◽  
Homogeneous Regions

Studies on the probability of rainfall and its spatiotemporal variations are important for the planning of water resources and optimization of the calendar of agricultural activities. This study identifies the occurrence of rain by first-order Markov Chain (MC) and by two states in the Tapajos River Basin (TRB), Amazon, Brazil. Cluster analysis (CA), based on the Ward method, was used to classify homogeneous regions and select samples for checking the probability of rainfall occurrence by season. The historical series of daily rainfall data of 80 stations were used for the period 1990-2014. The CA technique identified 8 homogeneous regions and their probability of occurrence of rainfall, helping to determine which regions and periods have greater need of irrigation. Results of the probability of occurrence of dry and rainy periods in the TRB were used to define the dry (May thru September) and rainy seasons (October thru April). Elements of the matrix transition probabilities showed variability in relation to time and, in addition, the influence of geographical position of seasonal rainfall in determining dry and rainy periods at specific sites in the TRB.

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

2013 ◽  
Vol 17 (12) ◽  
pp. 4981-4993 ◽  
Author(s):  
J.-S. Yang ◽  
S.-P. Yu ◽  
G.-M. Liu
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Wavelet Transforms ◽  
Wavelet Neural Network ◽  
Single Step ◽  
Data Series ◽  
Discrete Wavelet ◽  
Term Forecast ◽  
Neuro Fuzzy

Abstract. In order to increase the accuracy of serial-propagated 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-step-ahead-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.

scholarly journals Evaluation of generalized extreme value and Gumbel distributions for estimating maximum daily rainfall

2021 ◽  
pp. 1-11
Author(s):  
Álvaro José Back ◽  
Fernanda Martins Bonfante
Keyword(s):  
Standard Error ◽  
Probability Distributions ◽  
Daily Rainfall ◽  
Extreme Value ◽  
Mitigation Measures ◽  
Data Series ◽  
Generalized Extreme Value ◽  
Historical Series ◽  
Anderson Darling ◽  
Maximum Daily Rainfall

Extreme rain events can cause social and economic impacts in various sectors. Knowing the risk of occurrences of extreme events is fundamental for the establishment of mitigation measures and for risk management. The analysis of frequencies of historical series of observed rain through theoretical probability distributions is the most commonly used method. The generalized extreme value (GEV) and Gumbel probability distributions stand out among those applied to estimate the maximum daily rainfall. The indication of the best distribution depends on characteristics of the data series used to adjust parameters and criteria used for selection. This study compares GEV and Gumbel distributions and analyzes different criteria used to select the best distribution. We used 224 series of annual maximums of rainfall stations in Santa Catarina (Brazil), with sizes between 12 and 90 years and asymmetry coefficient ranging from -0.277 to 3.917. We used the Anderson–Darling, Kolmogorov-Smirnov (KS), and Filliben adhesion tests. For an indication of the best distribution, we used the standard error of estimate, Akaike’s criterion, and the ranking with adhesion tests. KS test proved to be less rigorous and only rejected 0.25% of distributions tested, while Anderson–Darling and Filliben tests rejected 9.06% and 8.8% of distributions, respectively. GEV distribution proved to be the most indicated for most stations. High agreement (73.7%) was only found in the indication of the best distribution between Filliben tests and the standard error of estimate.

scholarly journals Assessment of Near-Real-Time Satellite Precipitation Products from GSMaP in Monitoring Rainfall Variations over Taiwan

2021 ◽  
Vol 13 (2) ◽  
pp. 202
Author(s):  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Jie Hsu ◽  
Xiuzhen Li ◽  
Liping Deng
Keyword(s):  
Real Time ◽  
Complex Terrain ◽  
Daily Rainfall ◽  
Rainfall Estimation ◽  
Rainfall Events ◽  
Satellite Precipitation ◽  
Different Seasons ◽  
Daily Scale ◽  
Rainfall Variations ◽  
Southwestern Taiwan

This study assessed four near-real-time satellite precipitation products (NRT SPPs) of Global Satellite Mapping of Precipitation (GSMaP)—NRT v6 (hereafter NRT6), NRT v7 (hereafter NRT7), Gauge-NRT v6 (hereafter GNRT6), and Gauge-NRT v7 (hereafter GNRT7)— in representing the daily and monthly rainfall variations over Taiwan, an island with complex terrain. The GNRT products are the gauge-adjusted version of NRT products. Evaluations for warm (May–October) and cold months (November–April) were conducted from May 2017 to April 2020. By using observations from more than 400 surface gauges in Taiwan as a reference, our evaluations showed that GNRT products had a greater error than NRT products in underestimating the monthly mean rainfall, especially during the warm months. Among SPPs, NRT7 performed best in quantitative monthly mean rainfall estimation; however, when examining the daily scale, GNRT6 and GNRT7 were superior, particularly for monitoring stronger (i.e., more intense) rainfall events during warm and cold months, respectively. Spatially, the major improvement from NRT6 to GNRT6 (from NRT7 to GNRT7) in monitoring stronger rainfall events over southwestern Taiwan was revealed during warm (cold) months. From NRT6 to NRT7, the improvement in daily rainfall estimation primarily occurred over southwestern and northwestern Taiwan during the warm and cold months, respectively. Possible explanations for the differences between the ability of SPPs are attributed to the algorithms used in SPPs. These findings highlight that different NRT SPPs of GSMaP should be used for studying or monitoring the rainfall variations over Taiwan for different purposes (e.g., warning of floods in different seasons, studying monthly or daily precipitation features in different seasons, etc.).

scholarly journals An innovative image fusion algorithm based on wavelet transform and discrete fast curvelet transform

2011 ◽  
Vol 1 (3) ◽  
Author(s):  
T. Sumathi ◽  
M. Hemalatha
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Curvelet Transform ◽  
Relevant Information ◽  
Good Time ◽  
Discrete Wavelet ◽  
Fusion Algorithm ◽  
Time Frequency ◽  
Fused Image

AbstractImage fusion is the method of combining relevant information from two or more images into a single image resulting in an image that is more informative than the initial inputs. Methods for fusion include discrete wavelet transform, Laplacian pyramid based transform, curvelet based transform etc. These methods demonstrate the best performance in spatial and spectral quality of the fused image compared to other spatial methods of fusion. In particular, wavelet transform has good time-frequency characteristics. However, this characteristic cannot be extended easily to two or more dimensions with separable wavelet experiencing limited directivity when spanning a one-dimensional wavelet. This paper introduces the second generation curvelet transform and uses it to fuse images together. This method is compared against the others previously described to show that useful information can be extracted from source and fused images resulting in the production of fused images which offer clear, detailed information.

FUSION OF SURVEILLANCE IMAGES IN INFRARED AND VISIBLE BAND USING CURVELET, WAVELET AND WAVELET PACKET TRANSFORM

2010 ◽  
Vol 08 (02) ◽  
pp. 271-292 ◽  
Author(s):  
PARUL SHAH ◽  
S. N. MERCHANT ◽  
U. B. DESAI
Keyword(s):  
Wavelet Packet ◽  
Curvelet Transform ◽  
Visual Quality ◽  
Wavelet Packet Transform ◽  
Discrete Wavelet ◽  
Discrete Wavelet Packet Transform ◽  
Fused Image ◽  
Fusion Methods ◽  
Visible Images

This paper presents two methods for fusion of infrared (IR) and visible surveillance images. The first method combines Curvelet Transform (CT) with Discrete Wavelet Transform (DWT). As wavelets do not represent long edges well while curvelets are challenged with small features, our objective is to combine both to achieve better performance. The second approach uses Discrete Wavelet Packet Transform (DWPT), which provides multiresolution in high frequency band as well and hence helps in handling edges better. The performance of the proposed methods have been extensively tested for a number of multimodal surveillance images and compared with various existing transform domain fusion methods. Experimental results show that evaluation based on entropy, gradient, contrast etc., the criteria normally used, are not enough, as in some cases, these criteria are not consistent with the visual quality. It also demonstrates that the Petrovic and Xydeas image fusion metric is a more appropriate criterion for fusion of IR and visible images, as in all the tested fused images, visual quality agrees with the Petrovic and Xydeas metric evaluation. The analysis shows that there is significant increase in the quality of fused image, both visually and quantitatively. The major achievement of the proposed fusion methods is its reduced artifacts, one of the most desired feature for fusion used in surveillance applications.

scholarly journals Image Denoising Using Discrete Wavelet Transform : A Theoretical Framework

2018 ◽  
Vol 7 (2.16) ◽  
pp. 120
Author(s):  
Praveen Bhargava ◽  
Shruti Choubey ◽  
Rakesh Kumar Bhujade ◽  
Nilesh Jain
Keyword(s):  
Image Denoising ◽  
Ultra Sound ◽  
Discrete Wavelet ◽  
Transform Domain ◽  
Hybrid Filter ◽  
Edge Sharpness ◽  
In Series ◽  
Salt And Pepper ◽  
Image Type

Noise is a random variation in brightness and color in image or simply we can say that unwanted signals are called noise. The noise is mixed with original signal and cause may troubles. Due to the presence of noise, quality of image is reduced and other features like edge sharpness and pattern recognition are badly affected. In image denoising methods to improve the results a hybrid filter is used for better visualization. The hybrid filter is composed with the combination of three filters connected in series. The hybridization has performed much better in case of salt and pepper type of noise and for most of the medical image type, either MRI, CT, SPECT, Ultra Sound. PSNR values show major improvement in comparison of other existing methods. Future, the results obtained from the presented denoising experiments would be tried to be improved further by using this method with other transform domain methods. Finally, the results are concluded that the proposed approach in terms of PSNR, MSE improvement is outperformed. 

scholarly journals Performance of TRMM TMPA 3B42 V7 in Replicating Daily Rainfall and Regional Rainfall Regimes in the Amazon Basin (1998–2013)

2018 ◽  
Vol 10 (12) ◽  
pp. 1879 ◽  
Author(s):  
Véronique Michot ◽  
Daniel Vila ◽  
Damien Arvor ◽  
Thomas Corpetti ◽  
Josyane Ronchail ◽  
...  
Keyword(s):  
Amazon Basin ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Spatial Coverage ◽  
Rainfall Regimes ◽  
Trmm 3B42 ◽  
Good Agreement

Knowledge and studies on precipitation in the Amazon Basin (AB) are determinant for environmental aspects such as hydrology, ecology, as well as for social aspects like agriculture, food security, or health issues. Availability of rainfall data at high spatio-temporal resolution is thus crucial for these purposes. Remote sensing techniques provide extensive spatial coverage compared to ground-based rainfall data but it is imperative to assess the quality of the estimates. Previous studies underline at regional scale in the AB, and for some years, the efficiency of the Tropical Rainfall Measurement Mission (TRMM) 3B42 Version 7 (V7) (hereafter 3B42) daily product data, to provide a good view of the rainfall time variability which is important to understand the impacts of El Nino Southern Oscilation. Then our study aims to enhance the knowledge about the quality of this product on the entire AB and provide a useful understanding about his capacity to reproduce the annual rainfall regimes. For that purpose we compared 3B42 against 205 quality-controlled rain gauge measurements for the period from March 1998 to July 2013, with the aim to know whether 3B42 is reliable for climate studies. Analysis of quantitative (Bias, Relative RMSE) and categorical statistics (POD, FAR) for the whole period show a more accurate spatial distribution of mean daily rainfall estimations in the lowlands than in the Andean regions. In the latter, the location of a rain gauge and its exposure seem to be more relevant to explain mismatches with 3B42 rather than its elevation. In general, a good agreement is observed between rain gauge derived regimes and those from 3B42; however, performance is better in the rainy period. Finally, an original way to validate the estimations is by taking into account the interannual variability of rainfall regimes (i.e., the presence of sub-regimes): four sub-regimes in the northeast AB defined from rain gauges and 3B42 were found to be in good agreement. Furthermore, this work examined whether TRMM 3B42 V7 rainfall estimates for all the grid points in the AB, outgoing longwave radiation (OLR) and water vapor flux patterns are consistent in the northeast of AB.

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