Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology

Hashim, Roslan; Roy, Chandrabhushan; Motamedi, Shervin; Shamshirband, Shahaboddin; Petković, Dalibor; Gocić, Milan; Lee, Siew Cheng

doi:10.1016/j.atmosres.2015.12.002

Cited by 30 publications

(6 citation statements)

References 58 publications

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“…In [ 80 ], Dou et al used four different machine learning approaches in different terrestrial ecosystems for ET estimation. ANN, support vector machine (SVM), extreme learning machine (ELM) [ 81 ], and adaptive neuro-fuzzy inference system (ANFIS) [ 78 , 82 , 83 , 84 , 85 , 86 ] were compared with each other on estimating ET. In [ 87 ], Torres-Rua et al built a narrowband and broadband emissivities model for UAV thermal imagery using a deep learning (DL) model.…”

Section: Methodsmentioning

confidence: 99%

Evapotranspiration Estimation with Small UAVs in Precision Agriculture

Niu

Hollenbeck

Zhao

et al. 2020

Sensors

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Estimating evapotranspiration (ET) has been one of the most critical research areas in agriculture because of water scarcity, the growing population, and climate change. The accurate estimation and mapping of ET are necessary for crop water management. Traditionally, researchers use water balance, soil moisture, weighing lysimeters, or an energy balance approach, such as Bowen ratio or eddy covariance towers to estimate ET. However, these ET methods are point-specific or area-weighted measurements and cannot be extended to a large scale. With the advent of satellite technology, remote sensing images became able to provide spatially distributed measurements. However, the spatial resolution of multispectral satellite images is in the range of meters, tens of meters, or hundreds of meters, which is often not enough for crops with clumped canopy structures, such as trees and vines. Unmanned aerial vehicles (UAVs) can mitigate these spatial and temporal limitations. Lightweight cameras and sensors can be mounted on the UAVs and take high-resolution images. Unlike satellite imagery, the spatial resolution of the UAV images can be at the centimeter-level. UAVs can also fly on-demand, which provides high temporal imagery. In this study, the authors examined different UAV-based approaches of ET estimation at first. Models and algorithms, such as mapping evapotranspiration at high resolution with internalized calibration (METRIC), the two-source energy balance (TSEB) model, and machine learning (ML) are analyzed and discussed herein. Second, challenges and opportunities for UAVs in ET estimation are also discussed, such as uncooled thermal camera calibration, UAV image collection, and image processing. Then, the authors share views on ET estimation with UAVs for future research and draw conclusive remarks.

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Section: Methodsmentioning

confidence: 99%

Evapotranspiration Estimation with Small UAVs in Precision Agriculture

Niu

Hollenbeck

Zhao

et al. 2020

Sensors

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“…Numerous investigations have been conducted with the aim of enhancing the precision of rainfall prediction by leveraging the potential of soft computing approaches, several of which will be examined in the following discussion. Hashim et al (2016) employed machine learning techniques (ANFIS, ANN, ELM, SVM, and GP) to identify the primary meteorological variables that influence precipitation. To achieve this end , they utilized five input parameters, namely wet day frequency, vapor pressure, maximum and minimum air temperatures, and cloud cover.…”

Section: Related Workmentioning

confidence: 99%

Application of Maximum Overlap Discrete Wavelet Transform and Machine Learning to Improved Daily Rainfall Prediction Modeling

KÜLLAHCI,

ALTUNKAYNAK

2023

Preprint

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Rainfall is an important phenomenon for various aspects of human life and the environment. Accurate prediction of rainfall is crucial for a wide range of sectors, including agriculture, water resources management, energy production, disaster management, and many more. The ability to predict rainfall in an accurate fashion enables stakeholders to make informed decisions and take necessary actions to mitigate the impacts of natural disasters, water scarcity, and other issues related to rainfall. In addition, advances in rainfall prediction technologies have the potential to contribute to sustainable water management and the preservation of water resources by providing the necessary information for decision-makers to plan and implement effective water management strategies. Hence, it is important to continuously improve the accuracy of rainfall prediction. In this paper, the integration of the Maximum Overlap Discrete Wavelet Transform (MODWT) and machine learning algorithms for daily rainfall prediction is proposed. The main objective of this study is to investigate the potential of combining MODWT with various machine-learning algorithms to increase the accuracy of rainfall prediction and extend the forecast time horizon to three days. In addition, the performances of the proposed hybrid models are contrasted with the models hybridized with commonly used discrete wavelet transform (DWT) algorithms in the literature. For this, daily rainfall raw data from 3 rainfall observation stations located in Türkiye are used. The results show that the proposed hybrid MODWT models can effectively improve the accuracy of precipitation forecasting, based on model evaluation measures such as mean square error (MSE) and Nash-Sutcliffe coefficient of efficiency (CE). Accordingly, it can be concluded that the integration of MODWT and machine learning algorithms have the potential to revolutionize the field of daily rainfall prediction.

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“…Recently, nonparametric approaches have been proposed, whereby the explicit functional form to quantify the relationship between input‐output pairs is no longer required (Hill, 2011; Bhuiyan et al., 2018). The nonparametric approaches, such as Bayesian analysis and machine learning, have been increasingly applied in estimating rainfall amounts (e.g., Bhuiyan et al., 2018; Hashim et al., 2016; Ouallouche et al., 2018; Pham et al., 2020; Ziarh et al., 2021). For instance, a two‐stage blending (TSB) method based on a Bayesian theory was proposed to combine satellite‐ and gauge‐based precipitation data, which can eliminate the potential negative effects from the individuals with poor quality (Ma et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

An Assimilating Model Using Broad Learning System for Incorporating Multi‐Source Precipitation Data With Environmental Factors Over Southeast China

Zhou

Tang

et al. 2022

Earth and Space Science

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Accurate estimation of precipitation is of critical importance for hydrometeorological applications and hazard prevention (Gao et al., 2019;Stephens & Kummerow, 2007). In general, precipitation can be derived from ground-based (e.g., rain gauges and radars) observations and satellite-based products, as well as atmospheric reanalysis products (e.g., ERA5;Tarek et al., 2020). Rain gauges and weather radars could measure precipitation with comparatively high credibility. However, rain gauges and radars are often distributed unevenly and sparsely, especially in mountainous areas (Ma et al., 2015), which leads to a deficiency of precipitation data in some regions (Gottschalck et al., 2005). Moreover, in serious natural environments, some gauges may be out of operation resulting in discontinuous rainfall measurements. Errors in the weather radar measurements can hardly be avoided, and the accuracy is influenced by weather conditions, precipitation process, and the spatial distribution of precipitation particles (Joss et al., 1998;Sokol et al., 2021). Remote sensing techniques have been applied to detect precipitation data with near-global coverage and decadal records (Ma et al., 2018;Sunilkumar et al., 2016), which could overcome the spatial and temporal restrictions to some extent. Vast satellite-based precipitation products have been released recently, such as the latest Version-7 Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) near-real-time (3B42RT) & post-real-time (3B42V7) products (Huffman et al., 2010), precipitation products using Climate Prediction Center morphing (CMORPH) method

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Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology

Cited by 30 publications

References 58 publications

Evapotranspiration Estimation with Small UAVs in Precision Agriculture

Evapotranspiration Estimation with Small UAVs in Precision Agriculture

Application of Maximum Overlap Discrete Wavelet Transform and Machine Learning to Improved Daily Rainfall Prediction Modeling

An Assimilating Model Using Broad Learning System for Incorporating Multi‐Source Precipitation Data With Environmental Factors Over Southeast China

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