Analysis of groundwater conservation with cube biopore infiltration holes using the value of soil permeability, the amount of rainfall, and the depth of the groundwater level. The groundwater conservation research method is carried out by making the cavity biopore infiltration holes to absorb the volume of rainwater and domestic waste water into the soil. Conservation is suitable for homes that have enough yard. The main reason is to use cube infiltration holes to accommodate large and small organic waste. Organic waste is trees, grass and domestic waste. For a small house yard, conservation of ground water enough with a cylindrical hole diameter of 10 cm. Benefits of biopore infiltration holes are reducing surface runoff, producing manure, fertilizing soil, reducing waste piles, and conserving ground water. The research objective was to analyze the cube biopore infiltration holes based on soil permeability, rainfall intensity, depth of groundwater level, and volume of organic waste. The results of the analysis can be obtained from the cube biopore infiltration holes which can absorb all the rainwater which can accommodate all organic waste. The results of permeability testing show that the average soil permeability is 0.00112 cm/s and the yield of maximum discharge runoff water in January is 97.57 cm3/second. The ground water level is two meters. While the volume of organic waste is one cubic per month. The result of the analysis showed that the area of cake absorption was 8.73 m2. When converted to a cube absorption field it takes approximately 8 meters wide by 0.5 meters and a depth of 0.5 meters. When divided into four cavity recharge holes LRB1 obtained area of 4 m2, LRB2 area of 2.7 m2, LRB3 area 2 m2, and LRB4 as reserve.
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A boundary element method is utilized to find numerical solutions to boundary value problems of trigonometrically graded media governed by a spatially varying coefficients anisotropic-diffusion convection equation. The variable coefficients equation is firstly transformed into a constant coefficients equation for which a boundary integral equation can be formulated. A boundary element method (BEM) is then derived from the boundary integral equation. Some problems are considered. The numerical solutions justify the validity of the analysis used to derive the boundary element method with accurate and consistent solutions. A FORTRAN script is developed for the computation of the solutions. The computation shows that the BEM procedure elapses very efficient time in producing the solutions. In addition, results obtained from the considered examples show the effect of the anisotropy of the media on the solutions. An example of a layered material is presented as an illustration of the application.
In this paper, interior 2D-BVPs for anisotropic FGMs governed by the Helmholtz equation with Dirichlet and Neumann boundary conditions are considered. The governing equation involves diffusivity and wave number coefficients which are spatially varying. The anisotropy of the material is presented in the diffusivity coefficient. And the inhomogeneity is described by both diffusivity and wave number. Three types of the gradation function considered are quadratic, exponential and trigonometric functions. A technique of transforming the variable coefficient governing equation to a constant coefficient equation is utilized for deriving a boundary integral equation. And a standard BEM is constructed from the boundary integral equation to find numerical solutions. Some particular examples of BVPs are solved to illustrate the application of the BEM. The results show the accuracy of the BEM solutions, especially for large wave numbers. They also show coherence between the flow vectors and scattering solutions, and the effect of the anisotropy and inhomogeneity of the material on the BEM solutions.
The general objective of this study is to project the distribution of settlement land through the development and application of spatial dynamics models using dynamic system methods and neural networks, while the specific objectives are: 1) Compiling spatial data on land characteristics and evaluating settlement land in Maros Regency; 2) Multitemporal mapping of settlement and non-residential land in 2010, 2016 and 2017; 3) Compile Causal loop diagrams and model simulations to determine the dynamics of population projections and the size of residential land needs until 2038; 4) Synchronizing land requirements with the allocation of space utilization in Maros Regency Spatial Planning. 5) Design and build a spatial model to predict the development and direction of settlement land using change prediction of Artificial Neural Network (ANN); and map the projected spatial distribution model of residential land every 5 (five) years. This research conducted a recent approach to multi-temporal observation and spatial optimization studies by integrating dynamic systems with neural network methods in solving settlement land management problems. The approach with the population growth projection model relates to the spatial dynamics of residential land use using the neural network to facilitate policy makers in decision making for settlement land management. The method used consists of 5 stages: stage I. analysis of land characteristics and land use of multi-temporal settlements, through land surveys, laboratory analysis, and spatial characteristics of land; then the land suitability analysis uses matching method; Phase II: mapping multi-temporal land use in 2010, 2016 and 2017 using analysis and extraction of satellite image information. The image used is Landsat medium resolution satellite imagery; stage III: arranging units and interactions and behaviour between units in a system of population growth dynamics, then making causal loop diagrams and then implementing dynamic software systems to simulate population growth used and residential land needs every five years; stage IV: testing the synchronization of the allocation of space utilization in the Spatial Palnning document of the District with the settlement land resulting from the projection; stage V: build a residential land use projection model which begins with change analysis, transition potential, and change prediction with the neural network method; then the projection result of settlement land needs is mapped every five years interval from 2018 - 2038 based on the results of the analysis. The implication of the results of the study will provide an overview of the harmony between the pattern of space and the trend of the development direction of the settlement, so that the resulting method can be used as a basis for revising the Spatial Planning in the future.
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