SUPPORT PRACTICE CLASSIFICATION MODEL BASE ON SELF ORGANIZING MAP IN RUSLE EROSION ESTIMATION MODEL (Studi Area: Cidadap Sub-District and Lembang Sub-District - Bandung Basin) - YUSUP SAPTARI (NIM : 35110002); Pembimbing : Dr. Ir. S. Hendriatiningsih., MS; Dr. Ir. Agung Bu, ASEP
Support Practice or P factor is one of the crucial factors in estimating erosion rate by using Revised Universal Soil Loss Equation (RUSLE). RUSLE is an erosion model designed to estimate annual erosions. P factor in the RUSLE erosion estimating model is computed when determining the types of acts on land that may decrease or increase erosion rate. The determination of Self Organizing Map (SOM)-based P factor value is a method of applied to find the P factor value of a land area automatically by utilizing the capacity of SOM Artificial Neural Network, so as to expedite a policy making on determining the extent of human act effect on land that may result in an accelerated erosion rate. The classification of the values of P factor produced is then used to find out the spread of erosion rate in an area by using RUSLE model. The problem thus far is that the observation of P factor should be performed directly in the field, and thus it will take a long time if mass observations are to be made in case the area observed is very wide and there are a large variety of P factor types to observe. This is because P factor must be classified systematically to be eligible as a factor in estimating RUSLE erosion automatically. In the present research, a P factor classification model that is fast, accurate, and may be performed massively by analyzing the data of land surface of Digital Terrain Model (DTM) data was created. A morphometric parameterization process was carried out to produce morphometric parameters such as: slope, maximum curvature, minimum curvature, and cross sectional curvature (4 dimensions) by using DTM data. By using an artificial neural network method, SOM was conducted to reduce morphometric parameters into 2 dimensions by a morphometric parameter training, steadily keeping their topological relations. SOM training was applied in data quantization stage to produce neuron as Best Matching Unit (BMU), a stage well known as self organize. BMU is a winning neuron in a concept of competitive learning used as a reference of other neurons in establishing a classification of morphometric parameter. In a neuron classification stage known as mapping, the result of morphometric c classification was transformed into P factor through an analysis of Wischmeier and Smith's P factor table. Data of rain fall, soil type, and slope length and gradient, and data of vegetation cover were derived from NDVI obtained from landsat satellite image data, implemented into a RUSLE erosion estimation model in Cidadap and Lembang Sub-districts.