Estimating Parameter of Nonlinear Bias Correction Method Using NSGA-II in Daily Precipitation Data

Pratama, Angga Wahyu; Buono, Agus; Hidayat, Rahmat; Harsa, Hastuadi

doi:10.12928/telkomnika.v16i1.6848

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…Recently, machine learning algorithms have also been introduced for satellite precipitation bias correction. Pratama et al (2018) combined genetic algorithm with a power transformation method for satellite precipitation bias correction. Le et al (2020) used a neural network to correct satellite precipitation bias in the Mekong River basin.…”

Section: Introductionmentioning

confidence: 99%

Bias correction method of high-resolution satellite-based precipitation product for Peninsular Malaysia

Iqbal

Shahid

Ahmed

et al. 2022

Theor Appl Climatol

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Satellite-based precipitation (SBP) is emerging as a reliable source for high-resolution rainfall estimates over the globe. However, uncertainty in SBP is still significant, limiting their use without evaluation and often without bias correction. The bias correction of SBP remained a challenge for atmospheric scientists. In this study, the performance of six SBPs, namely, SM2RAIN-ASCAT, IMERG, GsMap, CHIRPS, PERSIANN-CDS and PERSIANN-CSS in replicating observed daily rainfall at 364 stations over Peninsular Malaysia was evaluated. The bias of the most suitable SBP was corrected using a novel machine learning (ML)-based bias-correction method. The proposed bias-correction method consists of an ML classifier to correct the bias in estimating rainfall occurrence and an ML regression model to correct the amount of rainfall during rainfall events. The performance of different widely used ML algorithms for classification and regression were evaluated to select the suitable algorithms. IMERG showed better performance, showing a higher correlation coefficient (R 2 ) of 0.57 and Kling-Gupta Efficiency (KGE) of 0.5 compared to the other products. The performance of random forest (RF) was better than the knearest neighbourhood (KNN) for both classification and regression. RF classified the rainfall events with a skill score of 0.38 and estimated the rainfall amount of a rainfall event with the modified Index of Agreement (md) of 0.56. Comparison of IMERG and bias-corrected IMERG (BIMERG) revealed an average reduction in RMSE by 55% in simulating observed rainfall. The proposed bias correction method performed much better when compared with the conventional bias correction methods such as linear scaling and quantile regression. The BIMERG could reliably replicate the spatial distribution of heavy rainfall events, indicating its potential for hydro-climatic studies like flood and drought monitoring in the study area.

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Section: Introductionmentioning

confidence: 99%

Bias correction method of high-resolution satellite-based precipitation product for Peninsular Malaysia

Iqbal

Shahid

Ahmed

et al. 2022

Theor Appl Climatol

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“…This method has been used to solve many actual UCTP cases. There are several GA models such as informed GA [10], parallel GA [2], NSGA II [11,9], Adaptive Real Coded GA [13], Hybrid Fuzzy and GA [6], Quantum Evolutionary Computing [1], and distributed model GA [14] that have been proposed. This research used the distributed model GA, or what is known usually as Island Model GA [14], out of all these models.…”

Section: Introductionmentioning

confidence: 99%

Reinforced Island Model Genetic Algorithm to Solve University Course Timetabling

Gozali

Fujimura

2018

TELKOMNIKA

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The University Course Timetabling Problem (UCTP) is a scheduling problem of assigning teaching event in certain time and room by considering the constraints of university stakeholders such as students, lecturers, departments, etc. This problem becomes complicated for universities which have immense number of students and lecturers. Therefore, a scalable and reliable timetabling solver is needed. However, current solvers and generic solution failed to meet several specific UCTP. Moreover, some universities implement student sectioning problem with individual student specific constraints. This research introduces the Reinforced Asynchronous Island Model Genetic Algorithm (RIMGA) to optimize the resource usage of the computer. RIMGA will configure the slave that has completed its process to helping other machines that have yet to complete theirs. This research shows that RIMGA not only improves time performance in the computational execution process, it also offers greater opportunity to escape the local optimum trap than previous model.

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“…However, [5] and [3] revealed that CHIRPS still showed systematic bias. [6] performed preliminary study to find out the performance of CHIRPS over Java Island, and concluded that CHIRPS still has systematic bias on statistical value.…”

Section: Introductionmentioning

confidence: 99%

Bias correction of daily satellite precipitation data using genetic algorithm

Pratama

Buono

Hidayat

et al. 2018

IOP Conf. Ser.: Earth Environ. Sci.

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Abstract. Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) was productedby blending Satellite-only Climate Hazards Group InfraRed Precipitation (CHIRP) with Stasion observations data. The blending process was aimed to reduce bias of CHIRP. However, Biases of CHIRPS on statistical moment and quantil values were high during wet season over Java Island. This paper presented a bias correction scheme to adjust statistical moment of CHIRP using observation precipitation data. The scheme combined Genetic Algorithm and Nonlinear Power Transformation, the results was evaluated based on different season and different elevation level. The experiment results revealed that the scheme robustly reduced bias on variance around 100% reduction and leaded to reduction of first, and second quantile biases. However, bias on third quantile only reduced during dry months. Based on different level of elevation, the performance of bias correction process is only significantly different on skewness indicators.

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Estimating Parameter of Nonlinear Bias Correction Method Using NSGA-II in Daily Precipitation Data

Cited by 6 publications

References 15 publications

Bias correction method of high-resolution satellite-based precipitation product for Peninsular Malaysia

Bias correction method of high-resolution satellite-based precipitation product for Peninsular Malaysia

Reinforced Island Model Genetic Algorithm to Solve University Course Timetabling

Bias correction of daily satellite precipitation data using genetic algorithm

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