[HTML][HTML] Soil water erosion susceptibility assessment using deep learning algorithms

K Khosravi, F Rezaie, JR Cooper, Z Kalantari… - Journal of …, 2023 - Elsevier
Accurate assessment of soil water erosion (SWE) susceptibility is critical for reducing land
degradation and soil loss, and for mitigating the negative impacts of erosion on ecosystem …

Enhancing flood susceptibility modeling using multi-temporal SAR images, CHIRPS data, and hybrid machine learning algorithms

M Riazi, K Khosravi, K Shahedi, S Ahmad, C Jun… - Science of The Total …, 2023 - Elsevier
Flood susceptibility maps are useful tool for planners and emergency management
professionals in the early warning and mitigation stages of floods. In this study, Sentinel-1 …

[HTML][HTML] From regional to parcel scale: A high-resolution map of cover crops across Europe combining satellite data with statistical surveys

AN Fendrich, F Matthews, E Van Eynde… - Science of the Total …, 2023 - Elsevier
Abstract The reformed Common Agricultural Policy of 2023–2027 aims to promote a more
sustainable and fair agricultural system in the European Union. Among the proposed …

A hybrid wavelet–machine learning model for qanat water flow prediction

S Samani, M Vadiati, M Delkash, H Bonakdari - Acta Geophysica, 2023 - Springer
In many parts of semiarid and arid regions, qanats are the leading supplier of water demand
for agricultural and drinking usage. Qanat is an ancient collecting water system, and qanat …

Land subsidence susceptibility mapping using Interferometric Synthetic Aperture Radar (InSAR) and machine learning models in a semiarid region of Iran

H Gharechaee, AN Samani, SK Sigaroodi… - Land, 2023 - mdpi.com
Most published studies identify groundwater extraction as the leading cause of land
subsidence (LS). However, the causes of LS are not only attributable to groundwater …

Uncovering the impacts of depleting aquifers: A remote sensing analysis of land subsidence in Iran

M Haghshenas Haghighi, M Motagh - Science Advances, 2024 - science.org
Intensive groundwater pumping, previously unrecognized in its full extent, is blamed for
aquifer degradation and widespread land subsidence in Iran. We use a 100-meter …

ENVINet5 deep learning change detection framework for the estimation of agriculture variations during 2012–2023 with Landsat series data

G Singh, N Dahiya, V Sood, S Singh… - Environmental Monitoring …, 2024 - Springer
Remote sensing is one of the most important methods for analysing the multitemporal
changes over a certain period. As a cost-effective way, remote sensing allows the long-term …

[HTML][HTML] Land subsidence susceptibility mapping based on InSAR and a hybrid machine learning approach

AA Alesheikh, Z Chatrsimab, F Rezaie, S Lee… - The Egyptian Journal of …, 2024 - Elsevier
Land subsidence (LS) due to natural processes or human activity can irreparably damage
the environment. This study employed the quasi-permanent scatterer method to detect areas …

A Hybrid convolution neural network for the classification of tree species using hyperspectral imagery

J Wang, Y Jiang - Plos one, 2024 - journals.plos.org
In recent years, the advancement of hyperspectral remote sensing technology has greatly
enhanced the detailed mapping of tree species. Nevertheless, delving deep into the …

Monthly streamflow forecasting based on meteorological data from a nearby station

E Nohani, A Karimipour, S Khazaei… - Water Practice & …, 2024 - iwaponline.com
Monthly streamflow forecasting is critical for improving water resource management. In this
study, several base-classifier data-mining algorithms–conjunctive rule (CR), isotonic …