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The increasing availability of hydrological and physiographic spatiotemporal data has boosted machine learning's role in rapid flood mapping. Yet, data scarcity, especially high-resolution DEMs, challenges regions with limited access. This paper examines how DEM type and resolution affect flood prediction accuracy, utilizing a cutting-edge deep learning (DL) method called 1D convolutional neural network (CNN). It utilizes synthetic hydrographs as training input and water depth data obtained from LISFLOOD-FP, a 2D hydrodynamic model, as target data. This study investigates digital surface models (DSMs) and digital terrain models (DTMs) derived from a 1 m LIDAR-based DTM, with resolutions from 15 to 30 m. The methodology is applied and assessed in a established benchmark, city of Carlisle, UK. The models' performance is then evaluated and compared against an observed flood event using RMSE, Bias, and Fit indices. Leveraging the insights gained from this region, the paper discusses the applicability of the methodology to address the challenges encountered in a data-scarce flood-prone region, exemplified by Pakistan. Results indicated that utilizing a 30 m DTM outperformed a 30 m DSM in terms of flood depth prediction accuracy by about 21% during the flood peak stage, highlighting the superior performance of DTM at lower resolutions. Increasing the resolution of DTM to 15 m resulted in a minimum 50% increase in RMSE and a 20% increase in fit index across all flood stages. The findings emphasize that while a coarser resolution DEM may impact the accuracy of machine learning models, it remains a viable option for rapid flood prediction. However, even a slight improvement in data resolution in data-scarce regions would provide significant added value, ultimately enhancing flood risk management. [ABSTRACT FROM AUTHOR]

Among other grain-based foods, rice is an important and favorite food of Pakistan. Its demand has increased significantly in the recent era. The type of rice grains is very important for export and import. Therefore, it becomes necessary to distinguish rice types to avoid their fraudulent labeling. With similar aims, a dataset consisting of seven rice varieties, i.e., Basmati 2000, Chenab basmati, KSK 133, Kissan basmati, KSK 434, PK 1121 aromatic, and Punjab basmati, mostly cultivated in Pakistan have been collected and used for rice seed classification. Images of rice seed have been obtained using a self-designed setup, which consists of different seeded, i.e., 1-seeded, 5-seeded, 10-seeded, 15-seeded, and 20-seeded, images. A new model has been designed specifically for rice seed classification using convolutional neural networks, which consists of 18 layers. Standard evaluation measures based on results obtained through the proposed model have been evaluated and compared with both recent state-of-the-art published studies, and state-of-the-art CNN models, i.e., VGG-19, ResNet50, and GoogleNet (Inception-V3). Experiments proved that the proposed model achieved a perfect classification rate, i.e., 100% for each of the Pakistani grown rice seeds. This study is currently integrated with the agro-technology industry for the auto-classification of rice kernel seeds as a demo version. [ABSTRACT FROM AUTHOR]