Genetic Programming for the Downscaling of Extreme Rainfall Events on the East Coast of Peninsular Malaysia

Pour, Sahar Hadi; Harun, Sobri; Shahid, Shamsuddin

doi:10.3390/atmos5040914

Cited by 80 publications

(40 citation statements)

References 59 publications

(51 reference statements)

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“…Several authors such as Pour et al [78] reported that in general, extreme events frequently occur at the micro scale, therefore, gridded data may not have the capability to capture those phenomena precisely at point level. Schneider et al [79] argued that extreme events in the gridded data at micro or small scale can only be captured precisely with a greater number of observed stations or dense network of stations over an area.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Gridded Precipitation Datasets over Arid Regions of Pakistan

Ahmed

Shahid

Wang

et al. 2019

Water

Self Cite

101

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The rough topography, harsh climate, and sparse monitoring stations have limited hydro-climatological studies in arid regions of Pakistan. Gauge-based gridded precipitation datasets provide an opportunity to assess the climate where stations are sparsely located. Though, the reliability of these datasets heavily depends on their ability to replicate the observed temporal variability and distribution patterns. Conventional correlation or error analyses are often not enough to justify the variability and distribution of precipitation. In the present study, mean bias error, mean absolute error, modified index of agreement, and Anderson-Darling test have been used to evaluate the performance of four widely used gauge-based gridded precipitation data products, namely, Global Precipitation Climatology Centre (GPCC), Climatic Research Unit (CRU); Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation (APHRODITE), Center for Climatic Research-University of Delaware (UDel) at stations located in semi-arid, arid, and hyper-arid regions in the Balochistan province of Pakistan. The result revealed that the performance of different products varies with climate. However, GPCC precipitation data was found to perform much better in all climatic regions in terms of most of the statistical assessments conducted. As the temporal variability and distribution of precipitation are very important in many hydrological and climatic applications, it can be expected that the methods used in this study can be useful for the better assessment of gauge-based data for various applications.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Gridded Precipitation Datasets over Arid Regions of Pakistan

Ahmed

Shahid

Wang

et al. 2019

Water

Self Cite

101

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“…Coulibaly () used GP to develop models to downscale reanalysis data to daily temperature, Pour et al . () used GP to downscale reanalysis data to daily precipitation indices and Sachindra and Perera () employed GP to downscale reanalysis data to monthly precipitation.…”

Section: Introductionmentioning

confidence: 99%

“…In several statistical downscaling studies, GP has been used for determining relationships between the predictors and the predictand, and simultaneous identification of a subset of potential predictors. Coulibaly (2004) used GP to develop models to downscale reanalysis data to daily temperature, Pour et al (2014) used GP to downscale reanalysis data to daily precipitation indices and employed GP to downscale reanalysis data to monthly precipitation.…”

Section: Introductionmentioning

confidence: 99%

“…Although the GP algorithm is based on a sound theory, it is still subject to a certain degree of uncertainty mainly owing to the random generation of the initial population, how models are selected for genetic operations, termination criterion, mathematical function selection, population size, etc. (e.g., Parasuraman et al, 2007b). The uncertainties associated with the random generation of the initial population and randomness in the selection of models for genetic operations may influence the predictor selection (Tran et al, 2015) and the structure of the predictor-predictand relationships, when the GP algorithm is run repetitively even with the same predictor and predictand data with fixed attributes of GP (e.g., population size).…”

Section: Introductionmentioning

confidence: 99%

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Cautionary note on the use of genetic programming in statistical downscaling

Sachindra

Ahmed

Shahid

et al. 2018

Intl Journal of Climatology

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The selection of inputs (predictors) to downscaling models is an important task in any statistical downscaling exercise. The selection of an appropriate set of predictors to a downscaling model enhances its generalization skills as such set of predictors can reliably explain the catchment‐scale hydroclimatic variable (predictand). Among the predictor selection procedures seen in the literature, the use of genetic programming (GP) can be regarded as a unique approach as it not only selects a set of predictors influential on the predictand but also simultaneously determines a linear or nonlinear regression relationship between the predictors and the predictand. In this short communication, the details of an investigation on the assessment of effectiveness of GP in identifying a unique optimum set of predictors influential on the predictand and its ability to generate a unique optimum predictor–predictand relationship are presented. In this investigation, downscaling models were evolved for relatively wet and dry precipitation stations pertaining to two study areas using two different sets of reanalysis data for each calendar month maintaining the same GP attributes. It was found that irrespective of the climate regime (i.e., wet and dry) and reanalysis data set used, the probability of identification of a unique optimum set of predictors influential on precipitation by GP is quite low. Therefore, it can be argued that the use of GP for the selection of a unique optimum set of predictors influential on a predictand is not effective. However, when run repetitively, GP algorithm selected certain predictors more frequently than others. Also, when run repetitively, the structure of the predictor–predictand relationships evolved by GP varied from one run to another, indicating that the physical interpretation of the predictor–predictand relationships evolved by GP in a downscaling exercise can be unreliable.

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“…The artificial neural network can approximate nonlinear relations between predictors and predictands and their derivatives without prior knowledge of a specific nonlinear function (Gupta et al, 2014), which is helpful for making accurate forecasts of highly nonlinear climate systems (Schoof, 2013). A growing number of computational learning algorithms including tree-based methods (Goyal et al, 2012), genetic programming (Pour et al, 2014), support vector machines (Tripathi et al, 2006), and relevance vector machines (Ghosh and Mujumdar, 2008) were developed as well. Classification methods that were exploited based on expert systems (Dong et al, 2011(Dong et al, , 2012(Dong et al, , 2013(Dong et al, , 2014c, fuzzy rules (Bárdossy et al, 2002;Khan and Valeo, 2016), stepwise cluster analysis (Wang et al, 2013), and selforganizing maps (Huva et al, 2015) were widely applied within a context of downscaling.…”

Section: Introductionmentioning

confidence: 99%

Recursive multivariate principal‐monotonicity inferential climate downscaling

Cheng

Huang

Dong

et al. 2017

Quart J Royal Meteoro Soc

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A recursive multivariate principal‐monotonicity inferential downscaling approach (ReMPMID) is proposed for climate downscaling under complexities of data uncertainties, nonlinear predictor–predictand correspondences, predictand dependencies, non‐normal distributions, spatial homogeneities, and temporal non‐stationarities. This approach is applied to the Athabasca River Basin (ARB) to verify methodological effectiveness. Many findings are revealed. For instance, ReMPMID may enable improvement of statistical downscaling reliability in comparison with selected existing approaches under these complexities at least for the ARB. The overall accuracies of ReMPMID are relatively high for temperature, while being acceptable for precipitation at the multi‐year scale. This approach may overestimate temperature in spring and winter and precipitation in summer and autumn while underestimating temperature in summer and autumn and precipitation in spring and winter to a relatively small extent. The modelling accuracies are not sensitive to one parameter (i.e. the statistical significance level) and significantly vary with another parameter (i.e. the minimum partition row number, Nmin). The calibration accuracies decrease with the climbing of Nmin and there is not a significant monotonic relationship between Nmin and the verification accuracies. The optimal value of Nmin varies with grids and predictands and shows higher uncertainty for temperature compared with precipitation. The uncertainties in ReMPMID simulations increase from summer, autumn, spring to winter for temperature and from winter, spring, autumn to summer for precipitation. These findings are helpful for gaining insights into ReMPMID and the regional climate in the ARB or neighbouring regions.

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Genetic Programming for the Downscaling of Extreme Rainfall Events on the East Coast of Peninsular Malaysia

Cited by 80 publications

References 59 publications

Evaluation of Gridded Precipitation Datasets over Arid Regions of Pakistan

Evaluation of Gridded Precipitation Datasets over Arid Regions of Pakistan

Cautionary note on the use of genetic programming in statistical downscaling

Recursive multivariate principal‐monotonicity inferential climate downscaling

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