Performance comparison of SVM and ANN for aerobic granular sludge

To comply with growing demand for high effluent quality of Domestic Wastewater Treatment Plant (WWTP), a simple and reliable prediction model is thus needed. The wastewater treatment technology considered in this paper is an Aerobic Granular Sludge (AGS). The AGS systems are fundamentally complex due to uncertainty and non-linearity of the system makes it hard to predict. This paper presents model predictions and optimization as a tool in predicting the performance of the AGS. The input-output data used in model prediction are (COD, TN, TP, AN, and MLSS). After feature analysis, the prediction of the models using Support Vector Machine (SVM) and Feed-Forward Neural Network (FFNN) are developed and compared. The simulation of the model uses the experimental data obtained from Sequencing Batch Reactor under hot temperature of 50˚C. The simulation results indicated that the SVM is preferable to FFNN and it can provide a useful tool in predicting the effluent quality of WWTP

Peramalan curah hujan sebagai pendukung kalender tanam padi di Kabupaten Bojonegoro menggunakan Metode Arima, Support Vector Regression dan Genetic Algorithm-Support Vector Regression

Kabupaten Bojonegoro dikenal sebagai lumbung padi di Jawa Timur. Sebanyak 33,31% lahan Bojonegoro digunakan se-bagai lahan sawah. Pada beberapa tahun terakhir produksi padi Kabupaten Bojonegoro mengalami fluktuasi yang salah satu pe-nyebabnya adalah iklim ekstrem. Iklim ekstrem dapat meng-akibatkan efek musim kemarau yang panjang serta adanya hujan ekstrem yang mengakibatkan petani mengalami kerugian. Oleh ka-rena itu, keberhasilan produksi padi sangat bergantung pada in-formasi mengenai data curah hujan yang tersusun dalam kalender tanam. Dalam penelitian ini dilakukan peramalan curah hujan da-sarian di Pos Cawak dan Kedungadem menggunakan metode ARIMA, Support Vector Regression (SVR) dan Genetic Algorithm-SVR (GA-SVR). Berdasarkan RMSE dan SMAPE metode GA-SVR menghasilkan peramalan yang lebih akurat. Berdasarkan forecast 6 bulan selanjutnya akan dibuat kalender tanam padi. Hasil ka-lender tanam padi pada bulan Juli 2016- Desember 2016 me-nunjukkan kebutuhan air untuk penanaman padi sawah tidak dapat terpenuhi. Petani dapat mengganti padi dengan menanam pala-wija Jika tetap menanam padi, maka petani dan pemerintah harus memastikan tersedianya cadangan air. ====================================================================================== Bojonegoro District known as a granary in East Java. A total of 33.31% of the land in Bojonegoro is used as a wetland. In recent years the production of rice in Bojonegoro fluctuated, which one of them caused by extreme climate. Extreme climate can caused continuously dry season and extreme rainfall can caused farmers suffer losses. Therefore, the success of rice production is highly dependent on information of the rainfall data which arranged in a planting calendar. In this study, rainfall forecasting is done per 10 days in Cawak and Kedungadem Station using ARIMA method, Support Vector Regression (SVR) and Genetic Algorithm-SVR (GA-SVR). Based on RMSE and SMAPE values, GA-SVR method gave better forecast accuracy. Rice planting calendar will be made based on the forecasting result of the next 6 months. The result of rice planting calendar on July 2016 – December 2016 indicates that the water requirement for rice cultivation cannot be fulfilled. Farmers may substitute it with palawija which require less water, but if they keep doing the rice planting, the farmers and government must ensure the availability of water reserves