Implementation of Information System on the Planting Time Prediction Based on Climate Modelling

Changes in rainfall patterns due to climate change have resulted in losses of farmers in Indramayu. Farmers has loss of economic due to failing of planting and harvesting every year in this location. Therefore, farmers need information on future rainfall prediction to plant rice accurately and on time. The information system should be able to be easily accessed by farmers and extension. This paper discuss research result on rainfall prediction system and planting rice with high resolution in dasarian (10-days) scale. The information system includes predictive technology using Geographic Information System (GIS) with input from Smart Climate Modelling using stochastic approach. Information delivery system was developed in line with the predictions of rainfall information and feedback planting season comes to evaluate the predicted results. Such information will be dynamically run through overlaying with Google maps on the web server. This system can be accessed by the public and was designed to automatically generate maps of rainfall and planting prediction that can be viewed directly in the Google maps application. This research result will be effort to adapt to climate change that has impacted to agriculture sector in Indonesia

Kerangka Konseptual Pengembangan Sistem Informasi Cerdas Agribisnis (SICA) di Indonesia Berbasis Prediksi Iklim

Sebagai negara agraris, Indonesia memiliki potensi besar di pasar pertanian. Di sisi lain, pengaruh iklim terhadap pola pertanian di Indonesia sangat signifikan. Dengan dukungan sistem pendukung keputusan dalam kalender tanam berbasis prediksi iklim, petani dapat menghasilkan panen dengan baik karena mempertimbangkan pola iklim dalam strategi tanamnya. Tetapi di sisi lain, dengan petani mengetahui waktu terbaik untuk menanam tanaman, maka permintaan benih, pestisida, air, dan pasokan pupuk menjadi sangat tinggi dan tidak bisa dipenuhi sepenuhnya karena pasar tidak punya waktu dalam mempersiapkan semua kebutuhan petani tersebut. Penelitian ini bertujuan untuk mengembangkan kerangka kerja konseptual untuk memenuhi kebutuhan pasar untuk mengetahui permintaan petani pada waktu tertentu untuk mempersiapkan pasokan di wilayah tertentu dengan sudah dikembangkannya Sistem Informasi Cerdas Agribisnis (SICA) dalam platform website dan android. Sistem dirancang untuk mengintegrasikan kalender penanaman tanaman berbasis prediksi iklim dengan penawaran dan permintaan kebutuhan petani dalam aktivitas tanam. Selanjutnya, dengan menggunakan sistem ini, petani dapat mengetahui harga dan permintaan terbaik dari pasar untuk produksi tanaman mereka

Regional Typhoon Track Prediction Using Ensemble k-Nearest Neighbor Machine Learning in the GIS Environment

This paper presents a novel approach for typhoon track prediction that potentially impacts a region using ensemble k-Nearest Neighbor (k-NN) in a GIS environment. In this work, the past typhoon tracks are zonally split into left and right classes by the current typhoon track and then grouped as an ensemble member containing three (left-center-right) typhoons. The proximity of the current typhoon to the left and/or right class is determined by using a supervised classification k-NN algorithm. The track dataset created from the current and similar class typhoons is trained by using the supervised regression k-NN to predict current typhoon tracks. The ensemble averaging is performed for all typhoon track groups to obtain the final track prediction. It is found that the number of ensemble members does not necessarily affect the accuracy; the determination of similarity at the beginning, however, plays an important key role. A series of tests yields that the present method is able to produce a typhoon track prediction with a fast simulation time, high accuracy, and long duration

Regional Typhoon Track Prediction Using Ensemble k-Nearest Neighbor Machine Learning in the GIS Environment

This paper presents a novel approach for typhoon track prediction that potentially impacts a region using ensemble k-Nearest Neighbor (k-NN) in a GIS environment. In this work, the past typhoon tracks are zonally split into left and right classes by the current typhoon track and then grouped as an ensemble member containing three (left-center-right) typhoons. The proximity of the current typhoon to the left and/or right class is determined by using a supervised classification k-NN algorithm. The track dataset created from the current and similar class typhoons is trained by using the supervised regression k-NN to predict current typhoon tracks. The ensemble averaging is performed for all typhoon track groups to obtain the final track prediction. It is found that the number of ensemble members does not necessarily affect the accuracy; the determination of similarity at the beginning, however, plays an important key role. A series of tests yields that the present method is able to produce a typhoon track prediction with a fast simulation time, high accuracy, and long duration

Development of Hydro-Meteorological Hazard Early Warning System in Indonesia

This paper discusses the result of the development of a hydro-meteorological hazard early warning system (H-MHEWS) that combines weather prediction from Weather Research and Forecasting (WRF) and the hydrometeorological hazard index from the National Disaster Management Authority (BNPB), Indonesia. In its current development phase, the hazards that H-MHEWS predicts are floods, landslides, and extreme weather events. Potential hazard indices are obtained by using an overlay approach and resampling so that the data have a 100-m spatial resolution. All indices are classified into 4 status categories: "No alert", "Advisory", "Watch", and "Warning". Flood potential is produced by overlaying rainfall prediction at 3-hour intervals with the flood index. Landslide potential is produced by overlaying rainfall prediction with the landslide index. Extreme weather potential is divided into 3 categories, i.e. heavy rain, strong winds, and extreme ocean waves. The whole prediction is dynamic, following weather predictions at 3-hour intervals. The hazard prediction results will trigger a 'Warning' alert in case of emergency status. This alert will be set up in a notification system to make it easier for the user to identify the most dangerous hydrometeorological hazard events