Karenan terjadi kompetisi yang begitu ketat antar industri mebel, keadaan ini berimplikasi produsen mebel berusaha secara ketat dalam menciptakan produksi yang bermutu dan memenuhi selera konsumen.
Mebel berperan juga sebagai sumber pemasukan keuangan negara di Indonesia terutama penjualan kepada konsumen negara lain. Konsumen memilih menggunakan mebel hasil dari industri karena mebel produksi industri memiliki inovasi disain yang indah. Industri mebel di Indonesia merupakan usaha yang memiliki laju perkembangan sangat pesat. It was also found that the Rs was the most potent input variable for ET0 estimation while Ws was the weakest. The best weather conditions were obtained as 0.029 to 31.814 MJ/m², − 5.8 to 45.7 ☌, and 0.140 to 5.086 m/s for Rs, Tmax, and Ws, respectively. The equation obtained from the MGGP model, for the best-performed combination of Rs-Tmax-Ws, was presented. It was determined that the MGGP model outperformed both the M5Tree and the KNN models. Moreover, Taylor, radar, and boxplot diagrams were created. The model’s performance was evaluated using criteria such as Nash–Sutcliffe efficiency, Kling-Gupta efficiency, relative root mean squared error, mean absolute percentage error, and determination coefficient.
Different input combinations were created and analyzed. The input data consist of monthly solar radiation (Rs), maximum air temperature (Tmax), and wind speed (Ws) derived from 163 meteorological stations in Turkey.
In this study, the predictive power of three different machine learning (ML)-based approaches, namely, multi-gene genetic programming (MGGP), M5 model trees (M5Tree), and K-nearest neighbor algorithm (KNN), for long-term monthly reference evapotranspiration (ET0) prediction were investigated.
0 kommentar(er)
