Iraq has had more than 10000 km2 of geographical low land areas called marshes.
Enriched with great diversity of natural vegetation and wild life. With increasing
climatic changes and passive man interference phenomena, vast areas of these
marshes have deteriorated through drying out processes at an alarming rate.
According to recent survey achieved by several Iraqi ministries marshes areas have
decreased to about quarter of theS original area. The statistical data and geospatial
information are weak. We monitored, assessed the environmental processes and
detect changes using digitally processed landsat MSS (Multispectral Scanner) and
Spot (System Pour Observation) satellite images that transform haur Ibn Najm

Steady conjugate natural convection heat transfers in a two-dimensional enclosure filled with fluid saturated porous medium is studied numerically. The two vertical boundaries of the enclosure are kept isothermally at same temperature, the horizontal upper wall is adiabatic, and the horizontal lower wall is partially heated. The Darcy extended Brinkman Forcheimer model is used as the momentum equation and Ansys Fluent software is utilized to solve the governing equations. Rayleigh number (1.38 ≤ Ra ≤ 2.32), Darcy number (3.9 * 10-8), the ratio of conjugate wall thickness to its height (0.025 ≤ W ≤ 0.1), heater length to the bottom wall ratio (1/4 ≤  ≤ 3/4) and inclination angle (0°, 30° and 60°) are the main consid

     This research aims to predict new COVID-19 cases in Bandung, Indonesia. The system implemented two types of deep learning methods to predict this. They were the recurrent neural networks (RNN) and long-short-term memory (LSTM) algorithms. The data used in this study were the numbers of confirmed COVID-19 cases in Bandung from March 2020 to December 2020. Pre-processing of the data was carried out, namely data splitting and scaling, to get optimal results. During model training, the hyperparameter tuning stage was carried out on the sequence length and the number of layers. The results showed that RNN gave a better performance. The test used the RMSE, MAE, and R2 evaluation methods, with the best numbers being  0.66975075, 0.470