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Your search keyword '"Alipour, Reza Saleh"' showing total 3 results
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2. Impacts of DEM Type and Resolution on Deep Learning-Based Flood Inundation Mapping

Author

Fereshtehpour, Mohammad, Esmaeilzadeh, Mostafa, Alipour, Reza Saleh, and Burian, Steven J.

Subjects
Computer Science - Computational Engineering, Finance, and Science
Abstract

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 an established benchmark, the 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.

Published
2023
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3. A Remote Sensing Driven Soil Moisture Estimator: Uncertain Downscaling With Geostatistically Based Use of Ancillary Data.

Author

Karamouz, Mohammad, Alipour, Reza Saleh, Roohinia, Mahnoor, and Fereshtehpour, Mohammad

Subjects
SOIL moisture, REMOTE sensing, NORMALIZED difference vegetation index, DOWNSCALING (Climatology), STANDARD deviations, KRIGING
Abstract

In recent years, remote sensing satellites, including Soil Moisture Active Passive (SMAP), continuously monitor SM of the earth. However, the spatial resolution of the remotely sensed SM is insufficient for some hydrological applications. In this paper, a data‐driven model is proposed based on a hybrid approach called artificial neural network kriging (ANNK) to capture the interaction between SM and ancillary data (AD), including normalized difference vegetation index (NDVI), altitude, slope, Antecedent Moisture Condition, AMC (5‐day cumulative precipitation), and daily air temperature (DAT)/land surface temperature (LST). By co‐regionalization of the estimated SM from the ANNK and SMAP 36‐km product, SM in any part of the study area is estimated more accurately and on a finer resolution (1‐km). Finally, through a linear relationship between in‐situ SM with the up‐to‐date AD including AMC (previous 5‐day cumulative precipitation), LST or DAT, slope, NDVI, and the downscaled satellite SM, the soil moisture estimates are adjusted by also adding an error term. Estimation of the final SM product in terms of the Unbiased Root Mean Square Error of less than 0.04 m3/m3 meets the accuracy requirements of the SMAP SM retrieval. Moreover, the results of SM validation show that the proposed technique has a potential for real‐time SM estimations in developing areas. Key Points: A combined data‐driven geostatistically based model is developed for uncertain downscaling Soil Moisture Active Passive (SMAP) soil moisture (SM) data utilizing ancillary data (AD)Proposing a platform for uncertain SM estimation with 5‐day antecedent moisture condition that utilizes land cover and an error termA remarkable estimation of 1 km downscaled SM data by estimating missing data in 36 km SMAP readings using cokriging and AD [ABSTRACT FROM AUTHOR]

Published
2022
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