DEVELOPMENT OF CHLOROPHYLL-A STATUS MODEL IN SAGULING RESERVOIR BASED ON PHYSICOCHEMICAL PARAMETERS AND LANDSAT SATELLITE IMAGERY USING ARTIFICIAL NEURAL NETWORK

• Main Author: Hadi, Misbul
• Format: Theses
• Language: Indonesia
• Subjects: Teknik saniter dan perkotaan; teknik perlindungan lingkungan
• Online Access: https://digilib.itb.ac.id/gdl/view/80296
• Institution: Institut Teknologi Bandung

Water body quality management is one of the important parameters in maintaining the availability of clean water sources. The entry of various pollutants originating from domestic and non-domestic activities due to uncontrolled human activities has resulted in damage to water bodies. Monitoring activities by taking water samples have been carried out for many years in Saguling Reservoir, but this is certainly a burden for the reservoir manager. Modeling is one of the shortcuts for reservoir managers to monitor sustainable water quality, through the use of satellite imagery. The presence of chlorophyll-a is one of the important parameters that is an indicator of ecosystem fertility that acts as primary productivity and is responsible for the sustainability of the aquaculture food chain. However, poor management with the influx of various kinds of pollutants makes the reservoir become excess nutrient eutrophication events. This research proposes an Artificial Neural Network (ANN) method of multilayer perceptron model with back-propagation algorithm type consisting of physicochemical data variabels such as: PO4, NH3-N, NO2-N, NO3-N, transparency, NH3, temperature, pH, phytoplankton, chlorophyll-a, BOD, COD, turbidity, DO and light intensity and Landsat satellite data from 2013-2023 are used as input variabels to model chlorophyll-a in the reservoir. The evaluation results of regression analysis (R2 ), Root Mean Square Error (RMSE) and Mean Square Error (MSE) are used to see the performance of the ANN model in predicting chlorophyll-a. The ANN prediction model is expected to show better prediction ability when other parameter levels. The results of the prediction test of observed chlorophyll-a with predicted chlorophyll-a were carried out with two models, namely the prediction model with physicochemical data and a combination of landsat bands, where the physicochemical model was R 2 0,974, RMSE 0,106, MSE 0.106 and %Error 2,13% while the landsat band combination model was R2 0,85, RMSE 0,142, MSE 0,106 and %Error 2,62%.Based on the prediction results and comparison with field observation data, the overall condition of the reservoir is in ultra-microtrophic to mesotrophic conditions, which means that the average value of chlorophyll-a concentration scattered in the reservoir area is 0 - 5 mg/m3.