Environmental degradation as a result of deforestation carried out in the Catchment resulted in a decrease in its ability to store water. This has the effect of increasing the amount of sediment discharge. The process of estimating sediment discharges is very difficult because the data input variables are many and varied, usually, the data are very limited, because the erosion process occurs until the sediment discharge mechanism is quite complex. The process of sediment discharges in Catchment s is influenced by rain and surface runoff and is represented in the storage type. In this study, an approach using the Tank Model was conducted. The purpose of this study is to develop a tank model for sediment discharge analysis in disaster mitigation. The steps are setting the field experiment for collecting rain and discharge sediment data as the model input and setting the model analysis by making the structure and formulation of the tank model. There are 3 proposed tank models namely Tank Model 1 (three tanks, series, and cascade), Tank Model 2 (two cascade tanks), and Tank Model 3 (three cascade tanks). Model parameters are determined using the Genetic Algorithm (AG) method optimization approach. The analysis shows that Tank Model 3, composed of 3 (three) cascade tanks, represents a Catchment better than the other 2 tank models. This can be seen from the value of the accuracy of the model, namely the value of volume error (VE), the value of relative error (RE), the value of the mean least square error (RMSE), and the value of the correlation coefficient (R). But still has a range of differences for the value of sediment discharges, the cause may be a factor in the pattern of rain spread in the hydrological process, synchronization of the measurement process and data length, and the possible assumptions of the model parameters.
Uncontrolled erosion would cause considerable damages, such as soil fertility decline, water structures damage and reservoirs sedimentation. As the data for the sedimentation rate are limited, several models have been developed to predict the surface erosion and the rate of sedimentation. However, the availability of sufficient, diverse and extensive data is needed for the implementation of the models, both for the model calibration and the verification. The result of the analysis shows that both of the Water Tank Models that represent the erosion-sedimentation rate process, in which Water Tank 1 being the three-tank cascade system and Water Tank 2 being two-tank cascade system, are not optimum. This can be observed from the values of volume error (VE), relative error (RE), root-mean-square error (RMSE) and correlation coefficient (R) that show the effect of 1.5 hours of rain period in the sedimentation rate. The field condition shows considerable sedimentation, on the other hand, the models’ simulations show decreasing sedimentation rates. The optimum model’s parameters for Water Tank 1 and Water Tank 2 are 924.51%-1049.26% for the relative error, 50.81% - 121.42% for the volume error, 0.9 for the correlation coefficient and 6703.59-17,297.85 for the root-mean-square error. The parameters and constant’s values of the models are different relative to the drainage basins’ condition.
Learning will be effective if the teacher understands the needs of learning well. The purpose of this study was to explore the needs of teachers in literacy learning on reading comprehension competence in elementary schools. The method used in this research is exploratory qualitative which was conducted at the State Elementary School in Central Magelang Sub-District, Magelang City, Central Java Province, Indonesia. The results of the study indicate that there are several problems in literacy learning of reading comprehension competence so teachers need several learning innovations as solutions, namely in the learning model by combining problem-based learning models with the SQ3R model, in teaching materials by developing integrated teaching materials with innovative learning models, and on the assessment instrument by developing an assessment instrument oriented towards high order thinking skills (HOTS). The results of this needs analysis will be used to conduct further research as a form of literacy learning innovation in reading comprehension competence.
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