Weighted Regression Method Research Articles

Heritability of body mass index based on twin studies: a Meta-analysis

Objective: To use Meta analysis to understand the prevalence of the heritability of body mass index (BMI) in twins. Methods: All studies on the heritability of the twins' BMI published before December 31, 2020 were retrieved through the China National Knowledge Network, Wanfang, VIP, PubMed and Web of Science databases. The literature quality was evaluated by using Joanna Briggs Institute Critical Appraisal tools. Stata 16.0 was used to perform subgroup analysis on the outcome indicators (heritability, 95%CI) to explore the source of heterogeneity. The local weighted regression method was used to fit the trend of heritability with age. The publication bias test and the sensitivity analysis of included literatures were also performed by using Stata 16.0. Results: A total of 10 articles meeting the inclusion and exclusion criteria were included with 79 twins' independent estimates of heritability for BMI. Meta-analysis showed that the combined value of BMI heritability was 0.69 (95%CI: 0.65-0.71), the combined value of BMI (0.68, 95%CI: 0.65-0.70) in males was lower than that (0.70, 95%CI: 0.68-0.72) in females. The heritability of BMI (0.72, 95%CI: 0.68-0.76) in childhood and adolescence was higher than that (0.68, 95%CI: 0.66-0.70) in adulthood. The gender specific difference in BMI heritability in twins ≤18 years old was even greater, which was lower in males (0.68, 95%CI: 0.61-0.76) than in females (0.75, 95%CI: 0.69-0.81). The heritability increased with age in childhood, reached peak at about 18 years old, and then slowly decreased with age. Conclusions: The heritability of BMI varied with population characteristics, especially age and gender. In view of the high estimated value of BMI heritability in female children and adolescents, more attention should be paid to the start time of health intervention.

Research on the Spatio-Temporal Impacts of Environmental Factors on the Fresh Agricultural Product Supply Chain and the Spatial Differentiation Issue-An Empirical Research on 31 Chinese Provinces.

Environmental factors in time and space play a critical role in advancing the sustainable development of the fresh agricultural product supply chain. This paper, availing the panel data of 31 Chinese provinces from 2008 to 2019, constructs a system of indicators assessing the development of the fresh agricultural product supply chain, and obtains the comprehensive development level in the Entropy Weight Method (EWM). Furthermore, it establishes a comparison between optimal solutions generated by the Instrumental Variables Method (IVM) and the Generalized Method of Moments (GMM) over the endogeneity issue of variables, creates the comparison between the weighted regression methods of Geographically Weighted Regression (GWR) and Multi-scale Geographic Weighted Regression (MGWR), and obtains the relationship among the 14 environmental factors in their spatio-temporal impacts on the development of the fresh agricultural product supply chain. The results indicate that: (1) the environmental influencing factors in this paper have significant endogenous problems and various environmental factors impact on the fresh agricultural product supply chain in different trends and to different degrees. (2) With different bandwidths, the environmental factors could impact the fresh agricultural product supply chain to greatly varied degrees, demonstrating a strong attribute of regional correlation.

Mixed kernel principal component weighted regression based on just-in-time learning for soft sensor modeling

Soft sensors have been extensively applied for predicting difficult-to-measure quality variables. However, industrial processes are often characterized with the nonlinearity and time variance, which makes it difficult to accurately predict the quality variables. In this paper, a just-in-time learning (JITL) based mixed kernel principal component weight regression (MKPCWR) is proposed for soft sensing of nonlinear and time-variant processes. Firstly, taking advantages of classical principal component analysis and kernel PCA, a mixed kernel principal component analysis (MKPCA) is proposed to extract the nonlinear feature. The raw input space and high-dimensional nonlinear space form a mixed space, from which the MKPCA gets a better nonlinear feature representation for the raw input data. Then, a weighted regression method based on JITL is proposed to deal with the time-variant problem of processes. When building the JITL-based regression model, relevant training samples are assigned correspond weights in the loss function to increase the prediction accuracy of model. Lastly, the effectiveness of the proposed method for soft sensor modeling is demonstrated on two industrial processes. Results show that JITL-based MKPCWR is more effective to solve the nonlinearity and time variance than JITL-based principal component regression and kernel principal component regression.

Identifying Spatial Driving Factors of Energy and Water Consumption in the Context of Urban Transformation

Urban energy and water consumption varies substantially across spatial and temporal scales, which can be attributed to changes of socio-economic variables, especially for a city undergoing urban transformation. Understanding these variations in variables related to resource consumptions would be beneficial to regional resource utilization planning and policy implementation. A geographically weighted regression method with modified procedures was used to explore and visualize the relationships between socio-economic factors and spatial non-stationarity of urban resource consumption to enhance the reliability of predicted results, taking Taichung city with 29 districts as an example. The results indicate that there is a strong positive correlation between socio-economic context and domestic resource consumption, but that there are relatively weak correlations for industrial and agricultural resource consumption. In 2015, domestic water and energy consumption was driven by the number of enterprises followed by population and average income level (depending on the target districts and sectors). Domestic resource consumption is projected to increase by approximately 84% between 2015 and 2050. Again, the number of enterprises outperforms other factors to be the dominant variable responsible for the increase in resource consumption. Spatial regression analysis of non-stationarity resource consumption and its associated variables offers useful information that is helpful for targeting hotspots of dominant resource consumers and intervention measures.

THE Using the Geograhically Weighted Regression (GWR) Method to Estimate the Dominant Factors Affecting the Poor in Jambi Province

Kemiskinan merupakan keadaan individu atau sekelompok orang tidak mampu memenuhi kebutuhan dasar yang dianggap sebagai kebutuhan minimal dan memiliki standar tertentu. Salah satu Provinsi yang tercatat jumlah penduduk miskin yang memiliki penghasilan di bawah standar adalah Provinsi Jambi. Tujuan dari penelitian ini adalah mengetahui faktor apa yang paling dominan yang mempengaruhi kemiskinan di Provinsi Jambi menggunakan metode Geographically weighted Regression (GWR). Variabel yang mempengaruhi kemiskinan adalah populasi penduduk yang memiliki akses air bersih ( , laju pertumbuhan penduduk( , angka harapan hidup( , rata-rata lama sekolah( , angka partisipasi sekolah( , persentase pengangguran( , jumlah penduduk tamat S1/D3 ( dan inflasi( . Data yang digunakan diperoleh dari data SUSENAS 2018. Berdasarkan hasil analisis, dapat disimpulkan bahwa variabel dominan yang mempengaruhi kmiskinan di Provinsi Jambi adalah . Kabupten/Kota dikelompokan berdasarkan variabel yang signifikan pada Kabupaten/Kota tersebut. Tebo, Bungo dipengaruhi oleh ; Merangin, Sarolangun dan Bantanghari di pengaruhi oleh ; Tanjung Jabung Barat dipengaruhi oleh ; Tanjung Jabung Timur dipengaruhi oleh ; sedangkan untuk Muaro Jambi, Sungai Penuh, Jambi dan Kerinci dipengaruhi oleh faktor lain yang tidak terdapat dalam penelitian.

Exploring the Impact of Urban Built Environment on Public Emotions Based on Social Media Data: A Case Study of Wuhan

In the era of public participation in government, public emotions and expectations are important considerations influencing urban construction, planning, and management. A desirable urban environment can make people feel at ease and comfortable and contribute to promoting positive public emotions. However, in the process of rapid urban development, the high-density and overloaded urban built environment has triggered people’s mental tension and anxiety and has contributed to negative emotions. Thus, this study aimed to explore the spatial distribution of public emotions and urban built environments in cities and to thoroughly investigate the correlation between urban built environments and public emotions. Considering the lack of dynamic elements analysis and emotions spatial analysis in previous studies, this study takes Wuhan City as an example, uses social media big data as the basis for text emotion analysis, introduces dynamic traffic elements, and establishes a multidimensional urban built environment measurement index system from five aspects: land use, spatial form, road and traffic, green space and open space, and daily life service facilities. Subsequently, the spatial distribution characteristics of public sentiment and urban built environment elements in Wuhan were analyzed. Finally, a geographically weighted regression method was used to analyze the degree of influence of different urban built environment elements on public emotions. The results showed that public emotions in Wuhan are not homogeneously distributed in terms of score and space and that there are significant differences. The urban built environment has a significant influence on public emotions. Higher land use mix, higher road network density, higher number of public transportation facilities, higher number of public open spaces, lower traffic congestion, and impact of freight transportation play important roles in promoting positive emotions. Therefore, in the process of urban construction, planners and decision makers should purposefully improve the quality of the built environment. Measures can include improving the mix of land functions, alleviating traffic congestion, avoiding the negative effects of freight traffic, rationally constructing green and open spaces, and improving various living facilities. This can help contribute toward improving urban functions and urban environments, and promote the construction of a people-oriented healthy city.

Ground-Motion Model for Crustal Events in Italy by Applying the Multisource Geographically Weighted Regression (MS-GWR) Method

ABSTRACT In this article, we implement a new approach to calibrate ground-motion models (GMMs) characterized by spatially varying coefficients, using the calibration dataset of an existing GMM for crustal events in Italy. The model is developed in the methodological framework of the multisource geographically weighted regression (MS-GWR, Caramenti et al., 2020), which extends the theory of multiple linear regression to the case with model coefficients that are spatially varying, thus allowing for capturing the multiple sources of nonstationarity in ground motion related to event and station locations. In this way, we reach the aim of regionalizing the ground motion in Italy by specializing the model in a nonergodic framework. Such an attempt at regionalization also addresses the purpose of capturing the regional effects in the modeling, which is needed for the Italian country, where ground-motion properties vary significantly across space. Because the proposed model relies on the italian GMM (ITA18) (Lanzano et al., 2019) dataset and functional form, it could be considered the ITA18 nonstationary version, thus allowing one to predict peak ground acceleration and velocity, as well as 36 ordinates of the 5%-damped acceleration response spectra in the period interval T=0.01–10 s. The resulting MS-GWR model shows an improved ability to predict the ground motion locally, compared with stationary ITA18, leading to a significant reduction of the total variability at all periods of about 15%–20%. The article also provides scenario-dependent uncertainties associated with the median predictions to be used as a part of the epistemic uncertainty in the context of probabilistic seismic hazard analyses. Results show that the approach is promising for improving the model predictions, especially on densely sampled areas, although further studies are necessary to resolve the observed trade-off inherent to site and path effects, which limits their physical interpretation.

EXAMINING SPATIAL VARIABILITY IN THE ASSOCIATION BETWEEN SOME FACTORS AND NUMBER OF PRISONERS IN TURKEY: A GWR ANALYSIS

The crime phenomenon requires an economic as well as a sociological analysis in terms of its occurrence, its causes and its consequences. In this study, the effects of socio-economic factors on crime were investigated by using Multiple Linear Regression and Geographical Weighted Regression Methods. The aim of the study is to analyze the factors that have an effect on crime, as well as to analyze which of the regression methods used gives more effective results. Method comparisons were made using R square and Akaike's Information Criterion. As a result of the analysis, it was seen that the Geographic Weighted Regression gave more effective results than the Multiple Linear Regression. In the study, Turkey's 81 provinces were included in the analysis and used the data of the year 2019. The number of prisoners is dependent variable, migration, literacy rate and unemployment rate are independent variables. The data used in this study are taken from the official web address Turkey Statistical Institute. Analyzes were made using the statistical R program.

A spatiotemporal ensemble model to predict gross beta particulate radioactivity across the contiguous United States.

Particulate radioactivity, a characteristic of particulate matter, is primarily determined by the abundance of radionuclides that are bound to airborne particulates. Exposure to high levels of particulate radioactivity has been associated with negative health outcomes. However, there are currently no spatially and temporally resolved particulate radioactivity data for exposure assessment purposes. We estimated the monthly distributions of gross beta particulate radioactivity across the contiguous United States from 2001 to 2017 with a spatial resolution of 32km, via a multi-stage ensemble-based model. Particulate radioactivity was measured at 129 RadNet monitors across the contiguous U.S. In stage one, we built 264 base learning models using six methods, then selected nine base models that provide different predictions. In stage two, we used a non-negative geographically and temporally weighted regression method to aggregate the selected base learner predictions based on their local performance. The results of block cross-validation analysis suggested that the non-negative geographically and temporally weighted regression ensemble learning model outperformed all base learning model with the smallest rooted mean square error (0.094mBq/m3). Our model provided an accurate estimation of particulate radioactivity, thus can be used in future health studies.

Assessment of the Possibility of Vertical Transmission of COVID-19: A Systematic Review and Meta-analysis Protocol

Background: The Coronavirus disease 2019 (COVID-19) outbreak has put a great burden on global health and healthcare systems. There is controversy regarding the possibility of vertical transmission of COVID-19. This proposed systematic review will be done to assess the possibility of vertical transmission of COVID-19 based on currently published literature. Methods: In this study, all published observational studies, including cross-sectional, cohort, and case-control studies, as well as case reports and case series, in peer-reviewed journals in any language until the end of July 2020 will be assessed. Editorials, commentaries, and letters to editors will be excluded from the review. Searching will be conducted using international bibliographic databases, including PubMed, Embase, and Web of Science based on Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) checklist. The search strategy will be improved and finalized based on the results of the primary search. Data extraction of the included articles will be performed by two researchers using the Zotero and review manager (revMan) software. The heterogeneity of the articles will be assessed using DerSimonian & Laird Q test and I2 statistic. The pooled estimated prevalence of vertical transmission of COVID-19 will be performed using the Metaprop command. Publication bias will be assessed using Begg’s rank correlation and the Egger weighted regression methods Results: Based on the reviewers comments the results section was deleted because this manuscript was a systematic review protocol. Conclusion: The findings of this study will help practitioners and health care providers in decision-making for the care and management of COVID-19-infected pregnant women.

Snow cover and vegetation greenness with leaf water content control the global land surface temperature

The land surface temperature (LST) and land use land cover (LULC) are the major components of climate- and environment-related studies. The objective of this study was to assess the relationship of LST with remotely sensed LULC-derived vegetation indices during 2018 at the global, latitudinal and continental scales. Moderate resolution imaging spectroradiometer (MODIS) Aqua daytime LST and eight LULC MODIS indices (NDVI, EVI, LAI, DSI, NDWI, albedo, NDSI, NDBI) were processed using Earth Engine Code Editor. The analysis was conducted using correlation coefficient and significance of the relationship of variables based on 2050 randomly selected points at the global scale. Based on the univariate and geographically weighted regression methods, the research confirmed that vegetation greenness (NDVI), leaf water content (NDWI) and snow cover (DSI) are the codominant drivers of decreasing LST at the global scale including Europe, Asia, South America and North America at the continental scale. Snow cover during winter and vegetation greenness in summer seasons control the global LST. Although albedo shows an inverse relationship, NDBI and NDSI displayed a positive relationship to LST at the global scale. In conclusion, temporal seasonal and inter-annual dynamics of LST in response to snow cover and vegetation properties (greenness, moisture) should be focused on understanding and regulating LST at varying scales.

Travel Behaviours of Sharing Bicycles in the Central Urban Area Based on Geographically Weighted Regression: The Case of Guangzhou, China

Mobile information and communication technologies (MICTs) have fully penetrated everyday life in smart societies; this has greatly compressed time, space, and distance, and consequently, reshaped residents’ travel behaviour patterns. As a new mode of shared mobility, the sharing bicycle offers a variety of options for the daily travel of urban residents. Extant studies have mainly examined the travel characteristics and influencing factors of public bicycles with piles, while the travel patterns for sharing bicycles and their driving mechanisms have been largely ignored. Using one week’s travel data for Mobike, this study investigated the spatial and temporal distribution patterns of sharing bicycle travel behaviours in the central urban area of Guangzhou, China; furthermore, it identified the influences of built environment density factors on sharing bicycle travel behaviours based on the geographically weighted regression method. Obvious morning and evening peaks were observed in the sharing bicycle travel patterns for both weekdays and weekends. The old urban area, which had a high degree of mixed function, dense road networks, and cycling-friendly built environments, was the main travel area that attracted sharing bicycles on both weekdays and weekends. Furthermore, factors including the point of interest (POI) for the density of public transport stations, the functional mixing degree, and the density of residential POIs significantly affected residents’ travel behaviours. These findings could enrich discourse regarding shared mobility with a Chinese case characterised by rapidly developing MICTs and also provide references to local authorities for improving slow traffic environments.

Soil-Quality Assessment during the Dry Season in the Mun River Basin Thailand

The Mun River Basin is one of Thailand’s major grain-producing areas, but the production is insufficient, and most of the cultivated lands are rain-fed and always unused in the dry season. All this makes it necessary to determine the status of soil nutrients and soil quality in the dry season to improve soil conditions, which will be useful for cultivation in the farming period. The aim of this study was to construct a soil-quality assessment based on soil samples, and in the process the minimum data set theory was introduced to screen the assessment indicators. The geographically weighted regression method was used to complete the spatial interpolation process of indicators, and the fuzzy logic model was constructed to evaluate the soil quality. The results showed that the spatial distributions of soil quality and indicators were similar. The soil quality was the best in the upstream while poor in the downstream, and the dry fields in the west and the forests in the east of the basin were better than other areas nearby. However; the soil qualities of paddy fields in the middle and east of the basin were poor due to the lack of soil nutrient supply when the fields were unused

Research on the Weighted Regression Method of Artillery Trajectory Fitting Computation

In order to improve the precision of the heteroscedastic artillery firing table, a fitting computation method for artillery firing table compilation without the assumption of homologous variance is proposed in the paper. The principle of the method is to elevate the regression effect of heteroscedasticity model with using appropriate weights. First, a reasonable method to estimate the variance of disturbance term is explored. On this basis, the variance function of disturbance term is fitted by polynomial basis and B-spline basis respectively. Finally, weighted B-spline regression was performed on the heteroscedasticity model with the variance function as the weight. This method does not need to ignore the existence of heteroscedasticity, nor is it limited by the general assumption in the past. It has positive significance to reflect the operational performance of the new weapon system objectively which is still in the stage of scientific research and design finalization. In addition, the method can be applied in both homoscedastic and heteroscedastic populations.

Dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in multi-access edge computing.

The current computation offloading algorithm for the mobile cloud ignores the selection of offloading opportunities and does not consider the uninstall frequency, resource waste, and energy efficiency reduction of the user's offloading success probability. Therefore, in this study, a dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in a multi-access edge computing environment is proposed (DCO-PSOMO). According to the CPU utilization and the memory utilization rate of the mobile terminal, this method can dynamically obtain the overload time by using a strong, locally weighted regression method. After detecting the overload time, the probability of successful downloading is predicted by the mobile user's dwell time and edge computing communication range, and the offloading is either conducted immediately or delayed. A computation offloading model was established via the use of the response time and energy consumption of the mobile terminal. Additionally, the optimal computing offloading algorithm was designed via the use of a particle swarm with a mutation operator. Finally, the DCO-PSOMO algorithm was compared with the JOCAP, ECOMC and ESRLR algorithms, and the experimental results demonstrated that the DCO-PSOMO offloading method can effectively reduce the offloading cost and terminal energy consumption, and improves the success probability of offloading and the user's QoS.

Fuzzy Clustering Based Regression Model Forecasting: Weighted By Gustafson-Kessel Algorithm

Regression analysis is one of the well-known methods of multivariate analysis and it is efficiently used in many research fields, especially forecasting problems. In order for the results of regression analysis to be effective, some assumptions must be valid. One of these assumptions is the heterogeneity problem. One of the methods used to solve this problem is the weighted regression method. Weighted regression is a method that you can use when the least squares assumption of constant variance in the residuals is violated (heteroscedasticity). With the correct weight, this procedure minimizes the sum of weighted squared residuals to produce residuals with a constant variance (homoscedasticity). In this study, Gustafson-Kessel(GK) method is used to determine weights for weighted regression analysis. GK method is based on the minimization of the sum of weighted squared distances between the data points and the cluster centers. With the fuzzy clustering method, each observation value is bound to the specified clusters in a specific order of membership. These membership degrees will be calculated as weights in the weighted regression analysis and estimation work will be done. In application, 5 simulated and 1 real time data was estimated by the proposed method. The results were interpreted by comparing with Robust Methods (M and S estimator) and Weighted with FCM Regression analysis.

Superpixel-based time-series reconstruction for optical images incorporating SAR data using autoencoder networks

ABSTRACT Time-series reconstruction for cloud/shadow-covered optical satellite images has great significance for enhancing the data availability and temporal change analysis. In this study, we proposed a superpixel-based prediction transformation-fusion (SPTF) time-series reconstruction method for cloud/shadow-covered optical images. Central to this approach is the incorporation between intrinsic tendency from multi-temporal optical images and sequential transformation information from synthetic aperture radar (SAR) data, through autoencoder networks (AE). First, a modified superpixel algorithm was applied on multi-temporal optical images with their manually delineated cloud/shadow masks to generate superpixels. Second, multi-temporal optical images and SAR data were overlaid onto superpixels to produce superpixel-wise time-series curves with missing values. Third, these superpixel-wise time series were clustered by an AE-LSTM (long short-term memory) unsupervised method into multiple clusters (searching similar superpixels). Four, for each superpixel-wise cluster, a prediction-transformation-based reconstruction model was established to restore missing values in optical time series. Finally, reconstructed data were merged with cloud-free regions to produce cloud-free time-series images. The proposed method was verified on two datasets of multi-temporal cloud/shadow-covered Landsat OLI images and Sentinel-1A SAR data. The reconstruction results, showing an improvement of greater than 20% in normalized mean square error compared to three state-of-the-art methods (including a spatially and temporally weighted regression method, a spectral–temporal patch-based method, and a patch-based contextualized AE method), demonstrated the effectiveness of the proposed method in time-series reconstruction for multi-temporal optical images.

PEMODELAN INCIDENT RATE DEMAM BERDARAH DENGUE DI INDONESIA YANG BERKAITAN DENGAN FAKTOR LINGKUNGAN MENGGUNAKAN METODE GEOGRAPHICALLY WEIGHTED REGRESSION (GWR)

Dengue hemorrhagic fever cases in Indonesia often occur in cities and villages. Every year hundreds to thousands of people must be hospitalized due to this disease. There are several factors of the physical environment that directly or indirectly influence the transmission of this disease. Such as rainfall, air temperature, and humidity. In addition to the physical environment there are several other factors that can increase the occurrence of dengue cases, namely population density and the level of larvae free in an area. For this reason, we conducted a study of the above factors and their contribution in the addition of dengue cases that occurred in Indonesia in 2015 using secondary data. The purpose of this study is to identify and make a BDB iricident rate model related to environmental factors such as temperature, humidity, population density, and the amount of rainfall on the number of cases of dengue hemorrhagic fever in Indonesia in 2015. The method used is the Geographically Weighted Regression method. (GWR). In the GWR model the parameter estimation uses Weighted Least Square (WLS) by weighting the gaussian kernel function. The results of the study concluded that modeling with GWR was better than linear regression and the variables were significantly different in each region.

Can professors buy better evaluation with lenient grading? The effect of grade inflation on student evaluation of teaching

At many universities, student evaluations of teaching (SET) are used for determining promotion, tenure, and other financial benefits for professors, which gives incentive for them to try to increase their scores. However, previous research, mainly based on US data, indicates that SET scores not only depend on teaching effectiveness and quality, but also on several other factors, most notably on grades. In this study, we contribute to this stream of literature by investigating the effects of grade inflation on SET scores using four years of data from two leading Central European universities. The SET survey is filled out at these universities after the final grades are known. As a methodological novelty, we use weighted regression methods (ordinary least squares, two-stage least squares, fixed effects panel) to account for the differences in class size. The weighting reflects the importance of the average SET score of a class on the evaluation of the instructor. Our findings suggest that increasing the grade of a student by one will cause them to give approximately 0.2–0.4 higher evaluations for the instructor in the SET survey.

Improved flood simulation accuracy by downscaling remotely sensed precipitation data in the Qixing Wetland Watershed

Runoff is an important factor in maintaining the normal operation of wetland ecosystems. An effective runoff simulation can provide an important decision basis for wetland protection and ecological restoration. Aiming to make the spatial resolution of remotely sensed precipitation products match the existing high-precision digital elevation model (DEM), this study used multiple linear regression (MLR) and geographic weighted regression (GWR) methods to build a downscaling correction method for precipitation products in the Qixing Wetland Watershed. The TRMM 3B42V7 data were downscaled from 0.25° × 0.25° to 0.05° × 0.05°. Compared with the downscaling correction of the nearest neighbor method, the GWR correction performed better and could noticeably reduce the average relative deviation between the corrected precipitation series and the measured precipitation series and increase the correlation coefficient of the two series. For the MLR correction, the two indicators (the average relative deviation and the correlation coefficient) improved slightly. The downscaled precipitation data were input into the semidistributed hydrological model. The flood simulation results revealed that the GWR-corrected rainfall data could improve the accuracy of flood simulation better than the MLR.