To elucidate trends of hypolimnetic oxygen concentrations, vertical distributions of dissolved oxygen were measured in eight deep tropical bodies of water (one natural lake with two basins, five natural lakes, and one reservoir) in Indonesia. A comparison of those concentrations with previously reported data revealed that shoaling of hypolimnetic oxygen-deficient (around a few decimeters to a few meter per year) water had occurred in all of the lakes. Calculated areal hypolimnetic oxygen depletion rates were 0.046–5.9 g m−2 y−1. The oligomictic or meromictic characteristics of the bodies of water suppressed circulation and mixing in the hypolimnions and thus resulted in continuous shoaling of the uppermost oxygen-deficient layers. In some lakes, millions of fish sometimes died suddenly, probably owing to upward movement of oxygen-deficient water to near the surface during periods of strong winds. In the future, the rate of shoaling will be accelerated by human impacts in the basins and by climate warming, the influence of which has already been manifested by rising water temperatures in these lakes. Appropriate monitoring and discussions of future restoration challenges are urgently needed to prevent the hypolimnions of the lakes from becoming completely anoxic.
Abstract. Problems of high turbidity, sedimentation, water pollution and siltation occur at Limboto Lake, Gorontalo, Indonesia. The objective of this study was to analyze the rainfall-discharge relationship and its implications for water quality conditions. Secchi disk (water transparency), chlorophyll-a (chl-a), and total organic matter (TOM) were measured in May 2012, September 2012 and March 2013 at three sites of the lake (L-1, L-2 and L-3) to observe the impacts on the surrounding catchment. Based on representative stations for rainfall data from 2004 to 2013, monthly averages of rainfall in March-May (166.7 mm) and September (76.4 mm) were used to represent the wet and dry period, respectively. Moreover, sediment traps at these three sites were installed in September 2012. Based on the analysis it is suggested that rainfall magnitude and land use change at the Alopohu River catchment influenced the amount of materials flowing into the lake, degrading the water quality. Specifically, the higher average rainfall in May (184.5 mm) gave a higher average total sediment load (4.41 g/L/day). In addition, water transparency decreased with increasing chl-a. This indicates that the concentrations of sediment and nutrients, reflected by the high amount of chl-a, influenced the water quality conditions.
<p>This study aims to describe in general the performance of Wonolangan Sugar Factory and to analyze the comparison of sugarcane farming with a non-sugarcane commodity in the working area of Wonolangan Sugar Factory. Wonolangan Sugar Factory is located in Probolinggo Regency, almost 90% of sugarcane raw material is obtained from Lumajang Regency. Commodities that become competitors and the main choice of farmers to be cultivated are rice, maize, and onion. The data used in this study are primary and secondary data. The sampling method used is purposive with in-depth interview technique. Portrait performance of Wonolangan Sugar Factory in the last 5 years is very fluctuating caused by various factors such as climate impacts that are less supportive, the motivation of farmers to grow sugarcane, government policy in the sugar industry. To maintain the smoothness of the mill in the implementation of milling, Wonolangan Sugar Factory has several strategies both on the farm (garden) and off-farm (sugarcane processing at the factory). Based on the comparison of Sugar Business Result (SHU) of sugarcane and non-sugarcane planting pattern, it appears that the analysis shows that sugarcane farming with one Ratoon Cane (RC) category in paddy field can only compete with the pattern of non-cane maize farming throughout the year in South Sugar Factory Wonolangan.</p>
This study is proposed to compare which are the better method to classify Batik image between K-Nearest neighbor and Support Vector Machine using minimum features of GLCM. The proposed steps are started by converting image to grayscale and extracting colour feature using four features of GLCM. The features include Energy, Entropy, Contras, Correlation and 0o, 45o, 90o, and 135o. The classifier features consist of 16 features in total. In the experimental result, there exist comparison of previous works regarding the classification KNN and SVM using multi texton histogram (MTH). The experiments are carried out in the form of calculation of accuracy with data sharing and cross-validation scenario. From the test results, the average accuracy for KNN is 78.3% and 92.3% for SVM in the cross-validation scenario. The scenario for the highest accuracy of data sharing is at 70% for KNN and at 100% for SVM. Thus, it is apparent that the application of the GLCM and SVM method for extracting and classifying batik motifs has been effective and better than previous work.
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