A Comprehensive Evaluation of Surface Air Temperature Reanalyses over China against Urbanization Bias–Adjusted Observations

The goal of this study is to compare the differences in surface air temperature (SAT) between observational and reanalysis data in mainland China from 1961–2015 for evaluating the reliability and applicability of the reanalysis datasets, based on an observational dataset of 763 stations which has been adjusted for urbanization bias, and 8 reanalysis datasets. The time series, anomaly correlations, standard deviations, climate state, and linear trends of the reanalysis data are evaluated against the observations. The reanalysis data are consistent with the observational climate characteristics to a large extent. The correlation and standard deviation ratio between the reanalysis data and observations exhibited highly consistent inter–annual variability and dispersion, with the inter–annual SAT variability of JRA55 and ERA5 the closest to the observations for the periods 1961–2015 and 1979–2015, and the dispersions of 20CRV3 and NCEPV1 the most consistent with the observations for the two periods. The annual mean SAT of the reanalyses is generally 0–2.0°C lower than the observations, while the linear trends of all datasets exhibited clear warming. The biases in the SAT climatology of 20CRV3 and CRA40 are lower than other reanalysis datasets, and the linear trends of NCEPV1 and 20CRV3 are closer to the observations. With increasing elevation, the biases of the reanalysis data in terms of correlation, standard deviation, climate state, and linear trend all increased. Overall, in terms of the similarity of multiple measures to the urbanization bias–adjusted observations, CRA40 and JRA55 show the best performance of the products in reproducing various aspects of climatological and climate change features in mainland China for the period 1979–2015 and 1961–2015 respectively.

Penggerombolan Hasil Ujian Nasional Menggunakan K-Rataan Samar

National examination scores can be a basis for the government to make a mapping of education quality in order to increase it. The mapping can be done by using fuzzy cluster analysis. The objective of this experiment is to cluster districts/cities in Indonesia based on national examination score in natural and social science in 2014/2015 until 2017/2018 school year by using the fuzzy c-means method. The evaluation criteria that will be used are the standard deviation ratio, silhouette coefficient, and Xie Beni index. The best cluster size is two clusters, A and B. The clustering result shows cluster A has a higher mean from each subject than cluster B. Therefore, cluster A will be categorized as good, whereas cluster B as bad. The proportion of districts/cities that belong to cluster A decreased each year. The final cluster result can be determined by the mean of its degree of membership from those four school years. The analysis results show that the distribution of education quality is dominated in Java Island and squatter cities. East Nusa Tenggara, West Sulawesi, Central Sulawesi, and North Kalimantan don’t have any districts/cities belong to cluster A.

Village Grouping In Southwest Maluku District Based On Poverty Characteristics Using Self Organizing Maps (SOM) Methods

Poverty is one of the fundamental problems that has become the center of attention of the Maluku Provincial government, especially Southwest Maluku Regency. This study aims to provide information to the government about village grouping based on poverty characteristics in Southwest Maluku Regency using the Self Organizing Map network method. In this network, a layer containing neurons will arrange itself based on the input of a certain value in a group known as a cluster. In the grouping process, 3 results were obtained with the best grouping II results because they had the smallest standard deviation ratio value.

Pengelompokkan Kabupaten/Kota Di Sulawesi Berdasarkan Indikator Pendidikan Menggunakan Analisis Klaster Average Linkage Dan Median Linkage

Cluster analysis is included in the method of multivariate analysis of interdependence. Cluster analysis is a multivariate technique that classifies objects into different groups between one group and another group. This research is applied to the case of education indicators, education is important for improving the quality of human resources. Educational indicators are a measuring tool used to see how well the quality of education. Educational indicators are classified using average linkage and median linkage. The results of the analysis showed that the median linkage obtained a standard deviation ratio value of 0.061 smaller than the standard deviation ratio average linkage value of 0.078. The method that has the smallest ratio is the method with the best performance. So that grouping City Districts in Sulawesi based on education indicators in 2017 is better to use the median linkage and obtained 5 clusters formed.

Pengklasteran Kabupaten/Kota di Jawa Tengah berdasarkan Tenaga Kesehatan dengan Menggunakan Metode Ward dan K-Means

Analisis klaster merupakan suatu metode yang digunakan untuk mengelompokkan objek (kasus) ke dalam klaster (kelompok) yang relatif sama. Tujuan penelitian ini untuk mengklasterkan Kabupaten/Kota di Provinsi Jawa Tengah berdasarkan tenaga kesehatan tahun 2015 seperti tenaga medis, tenaga keperawatan, tenaga kebidanan, tenaga kefarmasian dan tenaga kesehatan lainnya dengan menggunakan metode Ward dan K-Means. Hasil penelitian menunjukkan ada tiga klaster terbentuk dimana metode Ward menghasilkan nilai rasio simpangan baku sebesar 0,3019% lebih besar jika dibandingkan dengan nilai rasio simpangan baku pada metode K-Means yaitu 0,2974%. Pada kasus ini, metode K-Means merupakan metode yang lebih baik dibandingkan metode Ward. [Cluster analysis is a method used to group objects (cases) into clusters (groups) that are relatively the same. The purpose of this study is to classify districts/cities in Central Java Province based on health worker in 2015 such as medical personnel, nursing staff, midwifery staff, pharmacy personnel and health workers using the Ward and K-Means methods. The results show that there are three clusters formed where the Ward method produce a standard deviation ratio of 0.3019% greater than the standard deviation ratio in the K-Means method, which is 0.2974%. In this case, the K-Means method is a better method than the Ward method.]

Evaluation of the SWAT model performance for simulating river discharge in the Himalayan and tropical basins of Asia

This study evaluated the Soil and Water Assessment Tool (SWAT) model performance for 11 basins located in two contrasting climatic regions of Asia: the Himalayan and the Southeast Asian tropics. A large variation existed among the case study basins in relation to basin size (330–78,529 km2), topography (377–4,310 metres above sea level) and annual rainfall (1,273–2,500 mm). Performance of the model was evaluated using R2 and wR2 for a low discharge event; Nash–Sutcliffe efficiency (NSE), R2 and RMSE-observation standard deviation ratio (RSR) for high discharge events; and NSE, R2, PBIAS, RSR, NSErel and wR2 for the overall hydrographs. SWAT was found to be suitable for both climatic regions but yielded better performance in the Himalayan basins (NSE 0.72–0.81 at calibration) compared to the tropical basins (NSE 0.36–0.72 at calibration). Although most of the models underperformed in either low or high discharge events, a few of those remaining showed a balance between the extremes, proving that it is possible to achieve a balanced hydrograph with the SWAT model. The consistency of model performance across numerous Himalayan and tropical basins in the area confirmed the versatility and reliability of SWAT as a hydrological model and suitable tool for water resources planning and management.

Optimal Pixel-to-Shift Standard Deviation Ratio for Training 2-Layer Perceptron on Shifted 60 × 80 Images with Pixel Distortion in Classifying Shifting-Distorted Objects

An optimization problem of classifying shifting-distorted objects is studied. The classifier is 2-layer perceptron, and the object model is monochrome 60 × 80 image. Based on the fact that previously the perceptron has successfully been attempted to classify shifted objects with a pixel-to-shift standard deviation ratio for training, the ratio is optimized. The optimization criterion is minimization of classification error percentage. A classifier trained under the found optimal ratio is optimized additionally. Then it effectively classifies shifting-distorted images, erring only in one case from eight takings at the maximal shift distortion. On average, classification error percentage appears less than 2.5 %. Thus, the optimized 2-layer perceptron outruns much slower neocognitron. And the found optimal ratio shall be nearly the same for other object classification problems, when the number of object features varies about 4800, and the number of classes is between two and three tens.

PEMODELAN DAERAH TANGKAPAN AIR WADUK KELILING DENGAN MODEL SWAT (Keliling Reservoir Catchment Area Modeling Using SWAT Model)

This study aimed to model watershed area of Keliling Reservoir using SWAT model. The reservoir is located in Aceh Besar District, Province of Aceh. The model was setup using 90m x 90m digital elevation model, land use data extracted from remote sensing data and soil characteristic obtained from laboratory analysis on soil samples. Model was calibrated using observed daily reservoir volume and the model performance was analyzed using RMSE-observations standard deviation ratio (RSR), Nash-Sutcliffe efficiency (NSE) and percent bias (PBIAS). The model delineated the study area into 3,448 Ha having 13 subwatersheds and 76 land units (HRUs). The watershed is mostly covered by forest (53%) and grassland (31%). The analysis revealed the 10 most sensitive parameters i.e. GW_DELAY, CN2, REVAPMN, ALPHA_BF, SOL_AWC, GW_REVAP, GWQMN, CH_K2 and ESCO. Model performances were categorized into very good for monthly reservoir volume with ENS 0.95, RSR 0.23, and PBIAS 2.97. The model performance decreased when it used to analyze daily reservoir inflow with ENS 0.55, RSR 0.67, and PBIAS 3.46.Keywords: Keliling Reservoir, SWAT, Watershed ABSTRAKPenelitian ini bertujuan untuk untuk memodelkan daerah tangkapan air Waduk Keliling dengan menggunakan Model SWAT. Waduk Keliling terletak di Kabupaten Aceh Besar, Propinsi Aceh. Dalam penelitian ini Model SWAT dikembangkan berdasarkan data digital elevasi model resolusi 90 m x90 m, tata guna lahan yang diperoleh dari intepretasi citra satelit dan data soil dari hasil analisa sampel tanah yang diperoleh di daerah penelitian. Model dikalibrasi dengan data volume waduk dan kinerja model dianalisa menggunakan parameter rasio akar rata-rata kuadrat error dan standard deviasi observasi (RSR), efesiensi Nash-Sutcliffe (NSE) dan persentase bias (PBIAS). Hasil deleniasi untuk daerah penelitian menghasilkan suatu DAS dengan luas 3,448 Ha dan memiliki 13 Sub DAS yang dikelompokkan menjadi 76 unit lahan. Sebagian besar wilayah study ditutupi oleh hutan (53%), dan pandang rumput (31%). Hasil analisa menunjukkan bahwa 10 parameter model yang sangat mempengaruhi debit adalah GW_DELAY, CN2, REVAPMN, ALPHA_BF, SOL_AWC, GW_REVAP, GWQMN, CH_K2 dan ESCO. Kinerja model sangat baik dalam memprediksikan volume tampungan waduk bulanan dengan nilai ENS 0,95, RSR 0,23, dan PBIAS 2,97. Namun, kinerja model menurun ketika mensimulasikan debit inflow harian dengan nilai-nilai ENS 0,55, RSR 0,67, dan PBIAS 3,46.Kata kunci: Waduk Keliling, SWAT, Daerah Tangkapan Air