Citation: Li, J.; Zhao, Y.; He, N.; Gurkalo, F. Feature Extraction Algorithm of Massive Rainstorm Debris Flow Based on Ecological Environment Telemetry. Water 2023, 15, 3807. https://doi.org/10.3390/ w15213807 Academic Editors: Guido D’Urso and Barry T. Hart Received: 21 August 2023 Revised: 7 October 2023 Accepted: 26 October 2023 Published: 31 October 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). water Article Feature Extraction Algorithm of Massive Rainstorm Debris Flow Based on Ecological Environment Telemetry Jun Li 1,2 , Yuandi Zhao 1,3 , Na He 4, * and Filip Gurkalo 4 1 School of Civil Engineering, Sichuan University of Science & Engineering, Zigong 643000, China; jglijun@suse.edu.cn (J.L.) 2 Gongqing Institute of Science and Technology, Gongqingchengshi 332020, China 3 School of Civil Engineering, University Teknologi MARA, Shah Alam 40450, Selangor, Malaysia 4 School of Civil Engineering, Henan Polytechnic University, Jiaozuo 454003, China; f.gurkalo@gmail.com * Correspondence: hn61886@163.com Abstract: In order to accurately extract the characteristics of debris flow caused by group rainstorms, effectively identify the on-site information of debris flow, and provide a scientific basis for debris flow monitoring, early warning and disaster control, this paper proposes a method for extracting the characteristics of heavy rainstorm debris flow using multiregional ecological environment remote sensing. In the ecological environment where debris flows occur frequently, remote sensing data of heavy rainstorm debris flows are preprocessed using remote sensing technology, providing an important basis for the feature extraction of debris flows. The kernel principal component analysis method and Gabor filters are innovatively used to extract the spectral and texture features of rainstorm and debris flow remote sensing images, and the convolutional neural network structure is improved based on the open source deep learning framework, integrating multilevel features to generate debris flow feature maps. The improved convolution neural network is then used to extract the secondary features of the fusion feature map, and the feature extraction of heavy rainstorm debris flow is realized. The experiment shows that this method can accurately extract the characteristics of heavy rainstorm debris flow. Fused remote sensing images of debris flow effectively ameliorate the problem of insufficient informational content in a single image and improve image clarity. When the Gabor kernel function has eight different directions, the feature extraction effect of the debris flow image in each direction of the heavy rainstorm is the best. Keywords: ecological environment in frequent areas; massive rainstorm debris; feature extraction; spectral characteristics; texture features; feature fusion 1. Introduction Most mudslides occur alongside mountain floods [1]. The difference between mud- slides and general floods is that the former contain sufficient amounts of solid debris. This includes sand and stones, with such components comprising between 15% and 80% of the mudslide volume. The entire flow process can last up to several hours, making these events more destructive than floods. The most extreme form of this disaster sees mass debris flow caused by a local rainstorm [2]. The presence of multiple gullies makes it easier for such events to inflict superimposed damage and cause more serious harm [3]. According to statistics, under the excitation of a high-intensity rainstorm, the losses from mass debris flow disasters caused by rainstorms can reach between 90% and 95% of the total disaster losses [4]. It can be said that massive rainstorm debris flow is extremely destructive due to the characteristics of having many points and wide areas during the disaster formation process. Dealing with this has become the difficulty of debris flow prevention [5]. Therefore, in order to prevent and reduce disasters, it is very important to extract the relevant features of debris flow caused by massive rainstorms. Water 2023, 15, 3807. https://doi.org/10.3390/w15213807 https://www.mdpi.com/journal/water