Ecological Informatics最新文献

{"title":"A novel model for mapping soil organic matter: Integrating temporal and spatial characteristics","authors":"Xinle Zhang ,&nbsp;Guowei Zhang ,&nbsp;Shengqi Zhang ,&nbsp;Hongfu Ai ,&nbsp;Yongqi Han ,&nbsp;Chong Luo ,&nbsp;Huanjun Liu","doi":"10.1016/j.ecoinf.2024.102923","DOIUrl":"10.1016/j.ecoinf.2024.102923","url":null,"abstract":"<div><div>Mapping the spatial distribution of soil organic matter (SOM) content is crucial for land management decisions, yet its accurate mapping faces challenges due to complex soil-environment relationships and temporal feature capture limitations in machine learning models. This study focuses on the typical black soil region in Northeast China, specifically using Youyi Farm as the main research area and Heshan Farm as the transfer research area. A novel approach is proposed that combines the CNN-LSTM model with a Cosine Annealing Warm Restarts learning rate (CNN-LSTM-CAWR) to enhance the accuracy of SOM mapping. In this model, the Convolutional Neural Network (CNN) extracts spatial context features from static variables (e.g., climate and terrain variables), while the Long Short-Term Memory (LSTM) network captures temporal features from dynamic variables (e.g., Sentinel-2 time series from April to October). The incorporation of the CAWR learning rate helps alleviate overfitting issues. Comparing the CNN-LSTM model, CNN model, and traditional RF model, the results show that the CNN-LSTM-CAWR model achieves the highest accuracy within research Area 1 (R² = 0.64, RMSE = 0.54 %) and maintains strong performance in the transfer research area (R² = 0.60, RMSE = 0.57 %). CNN-LSTM-CAWR demonstrates faster convergence, thereby improving mapping precision and effectively utilizing temporal information from features to enhance overall model performance. This study underscores the significant potential of the hybrid CNN-LSTM with CAWR model, highlighting the valuable information for SOM mapping contained within Sentinel-2 time series data.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102923"},"PeriodicalIF":5.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond observation: Deep learning for animal behavior and ecological conservation","authors":"Lyes Saad Saoud,&nbsp;Atif Sultan,&nbsp;Mahmoud Elmezain,&nbsp;Mohamed Heshmat,&nbsp;Lakmal Seneviratne,&nbsp;Irfan Hussain","doi":"10.1016/j.ecoinf.2024.102893","DOIUrl":"10.1016/j.ecoinf.2024.102893","url":null,"abstract":"<div><div>Recent advancements in deep learning have profoundly impacted the field of animal behavioral research, offering researchers powerful tools for understanding the complexities of animal movements and cognition. This comprehensive review is dedicated to an in-depth examination of the latest techniques, tools, and applications of deep learning in this domain. This study examines the principles of deep-learning-based tracking, pose estimation, and behavioral analysis, emphasizing their respective strengths, limitations, and practical implementation. From markerless pose tracking to multi-animal behavior classification, we present a variety of methodologies that facilitate high-throughput and precise behavioral quantification across diverse species and settings. Furthermore, emerging trends, such as the integration of drones and computer vision for the study of group dynamics in natural environments, as well as advancements in semi-supervised and unsupervised learning for robust behavioral segmentation and classification, were also examined. Given the pivotal role of responsible research, we address the pivotal challenges of scalability, robustness, and ethical considerations, paving the way for future research. By synthesizing insights from seminal works in neuroscience, computer vision, and artificial intelligence, this study provides researchers with a comprehensive understanding of the powerful tools and methodologies available to unlock the secrets of animal behavior and make promising discoveries across the vast animal kingdom.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102893"},"PeriodicalIF":5.8,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning approaches for bias correction in WRF model outputs for enhanced solar and wind energy estimation: A case study in East and West Malaysia","authors":"Abigail Birago Adomako ,&nbsp;Ehsan Jolous Jamshidi ,&nbsp;Yusri Yusup ,&nbsp;Emad Elsebakhi ,&nbsp;Mohd Hafiidz Jaafar ,&nbsp;Muhammad Izzuddin Syakir Ishak ,&nbsp;Hwee San Lim ,&nbsp;Mardiana Idayu Ahmad","doi":"10.1016/j.ecoinf.2024.102898","DOIUrl":"10.1016/j.ecoinf.2024.102898","url":null,"abstract":"<div><div>Accurate estimation of wind and solar energy potentials is crucial for successfully integrating renewable energy into power grids. Traditional numerical weather prediction models, such as the Weather Research and Forecasting (WRF) model, often suffer from biases that lead to inaccurate energy forecasts. This study employs advanced deep learning (DL) techniques to correct these biases in WRF model outputs, specifically to enhance wind and solar energy estimations in East and West Malaysia. Unlike previous studies, this research integrates a diverse array of DL models: Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Feedforward Neural Networks (FNN), to address both temporal and spatial prediction challenges. The models were trained and tested using historical weather data and ground-based measurements to improve the accuracy of wind speed and solar radiation predictions. Evaluation metrics, root mean square error (RMSE), mean bias error (MBE), and mean absolute error (MAE), demonstrate the better performance of CNN and FNN models over the sole WRF approach. The findings reveal that CNN achieved the lowest RMSE in wind speed estimation (0.91 in CEMACS and 0.97 in Kuching compared to WRF RMSEs of 1.92 and 1.39). At the same time, FNN significantly improved solar radiation prediction (RMSE of 86.86 in Kuching and 99.23 in CEMACS compared to WRF RMSEs of 154.44 and 370.66). Given the low wind speeds, the corrected data from CNN was used to estimate wind energy at 536 kWh at Kuching and 0 kWh at CEMACS. FNN-corrected data was also used to estimate solar energy at 19 kWh and 18 kWh at Kuching and CEMACS, respectively. This research not only shows the effectiveness of DL in mitigating biases in numerical weather prediction models but also contributes a novel methodology for reliable renewable energy assessments.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102898"},"PeriodicalIF":5.8,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shrinking horizons: Climate-induced range shifts and conservation status of hickory trees (Carya Nutt.)","authors":"Winnie W. Mambo ,&nbsp;Guang-Fu Zhu ,&nbsp;Richard I. Milne ,&nbsp;Moses C. Wambulwa ,&nbsp;Oyetola O. Oyebanji ,&nbsp;Boniface K. Ngarega ,&nbsp;Daniel Carver ,&nbsp;Jie Liu","doi":"10.1016/j.ecoinf.2024.102910","DOIUrl":"10.1016/j.ecoinf.2024.102910","url":null,"abstract":"<div><div>Understanding the intricate interplay between the geographic distributions of species and the dynamics of environmental factors is crucial for effective biodiversity management. Crop wild relatives are important resources for the improvement of cultivated plants. However, our understanding of how these species might respond to future climatic changes and their implications for conservation remains incomplete. In this study, we focus on the ecologically and economically significant hickory trees to address this knowledge gap. We employed the <em>Biomod2</em> ensemble model to predict the potential distributions of 12 North American and five East Asian <em>Carya</em> species based on 13,643 occurrence points and 26 environmental variables. We analyzed the distribution range dynamics of hickory trees across the past, present, and future emission scenarios (2090; SSP126 and SSP585), assessed their conservation status, and conducted a preliminary threat assessment. Our results indicate that most <em>Carya</em> species expanded their habitat range from the Last Glacial Maximum to the present, with substantial contraction projected under both future scenarios. A northward migration shift to high elevations was observed for most species from the LGM to the future. Sixteen species were categorized as “medium priority” for further conservation action, and only one (<em>C. tonkinensis</em>) as “high priority”. Preliminary threat assessment classified one species (<em>C. luana</em>) as critically endangered, eight endangered, four vulnerable, and five as least concern. This study underscores the potential effects of climate change on the distribution of <em>Carya</em> species, providing crucial insights for their conservation and highlighting the broader impacts of climate change on crop wild relatives.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102910"},"PeriodicalIF":5.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds","authors":"Jonathan Henrich ,&nbsp;Jan van Delden ,&nbsp;Dominik Seidel ,&nbsp;Thomas Kneib ,&nbsp;Alexander S. Ecker","doi":"10.1016/j.ecoinf.2024.102888","DOIUrl":"10.1016/j.ecoinf.2024.102888","url":null,"abstract":"<div><div>Laser-scanned point clouds of forests make it possible to extract valuable information for forest management. To consider single trees, a forest point cloud needs to be segmented into individual tree point clouds. Existing segmentation methods are usually based on hand-crafted algorithms, such as identifying trunks and growing trees from them, and face difficulties in dense forests with overlapping tree crowns. In this study, we propose TreeLearn, a deep learning-based approach for tree instance segmentation of forest point clouds. TreeLearn is trained on already segmented point clouds in a data-driven manner, making it less reliant on predefined features and algorithms. Furthermore, TreeLearn is implemented as a fully automatic pipeline and does not rely on extensive hyperparameter tuning, which makes it easy to use. Additionally, we introduce a new manually segmented benchmark forest dataset containing 156 full trees. The data is generated by mobile laser scanning and contributes to create a larger and more diverse data basis for model development and fine-grained instance segmentation evaluation. We trained TreeLearn on forest point clouds of 6665 trees, labeled using the Lidar360 software. An evaluation on the benchmark dataset shows that TreeLearn performs as well as the algorithm used to generate its training data. Furthermore, the performance can be vastly improved by fine-tuning the model using manually annotated datasets. We evaluate TreeLearn on our benchmark dataset and the Wytham Woods dataset, outperforming the recent SegmentAnyTree, ForAINet and TLS2Trees methods. The TreeLearn code and all datasets that were created in the course of this work are made publicly available.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102888"},"PeriodicalIF":5.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Changes in vegetation ecosystem carbon sinks and their response to drought in the karst concentration distribution area of Asia","authors":"Shunfu Yang ,&nbsp;Yuan Li ,&nbsp;Yuluan Zhao ,&nbsp;Anjun Lan ,&nbsp;Chunfang Zhou ,&nbsp;Hongxing Lu ,&nbsp;Luanyu Zhou","doi":"10.1016/j.ecoinf.2024.102907","DOIUrl":"10.1016/j.ecoinf.2024.102907","url":null,"abstract":"<div><div>Changes in net ecosystem productivity (NEP) in karst areas can have a significant impact on terrestrial ecosystem carbon cycling, yet quantifying changes in vegetation NEP and its response to factors such as drought and hydroclimate remains a difficult challenge because of its special climatic and hydrological conditions. We used remote sensing data to estimate vegetation NEP in the Asian karst concentrated distribution area (AKC), analyzed its spatial and temporal variations annually (2000−2020) and during rainy season (May–November), established the drought fluorescence monitoring index (DFMI), and used a ridge regression model to explore the response mechanism of vegetation NEP to dry and wet conditions response mechanism. The results showed the following: (1) Compared with the annual changes, the vegetation NEP changes in the rainy season differed significantly on the karst geographic divisions, in which there was a significant increasing trend in Southwest China (SC) and its karst areas, while a significant decreasing trend in the Indochina Peninsula (IP) and its karst areas. (2) DFMI was the main driver of vegetation NEP change, of which the contributions were 38.05 % and 32.82 % at the annual scale and in the rainy season, respectively, which drove the increase in SC vegetation NEP, and the decrease in IP; note that the increase in vapor pressure deficit (VPD) was the key factor causing the decrease in NEP in the IP karst area during the rainy season. (3) In the lagged effect of drought on vegetation NEP, the time scale of the lag was found to be 1 month. The study revealed differences in the changes in the vegetation carbon sinks in different karst geographic divisions. We obtained a new finding: a significant trend of decreasing vegetation NEP in the IP and its karst area was influenced by the long-term effects of changes in DFMI and VPD. Therefore, the variability of different karst areas, as well as changes in drought and water resources, should be considered in carbon-cycle regulation and vegetation restoration efforts in karst areas.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102907"},"PeriodicalIF":5.8,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bottlenose dolphin identification using synthetic image-based transfer learning","authors":"Changsoo Kim ,&nbsp;Byung-Yeob Kim ,&nbsp;Dong-Guk Paeng","doi":"10.1016/j.ecoinf.2024.102909","DOIUrl":"10.1016/j.ecoinf.2024.102909","url":null,"abstract":"<div><div>The Indo-Pacific bottlenose dolphin (IPBD) (<em>Tursiops aduncus</em>) is a key species in marine ecosystems. Photo-identification (photo-ID) is a fundamental method for studying dolphin populations by identifying individuals based on the unique features of their dorsal fins. Despite recent developments in learning-based photo-ID algorithms, the lack of training data for these models has become a bottleneck for improving the accuracy of these algorithms. In this study, we used synthetic image generation and deep learning to improve photography-based IPBD identification. We generated 7500 synthetic dorsal fin images of 30 dolphins and trained a custom triplet neural network using ResNet50 to distinguish individuals. The model achieved 84.8 % accuracy within the top 10-ranked positions and 72.2 % accuracy in the top 5-ranked positions, demonstrating the potential of these technologies to enhance IPBD monitoring and conservation efforts.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102909"},"PeriodicalIF":5.8,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-supervised feature learning for acoustic data analysis","authors":"Ahmet Pala ,&nbsp;Anna Oleynik ,&nbsp;Ketil Malde ,&nbsp;Nils Olav Handegard","doi":"10.1016/j.ecoinf.2024.102878","DOIUrl":"10.1016/j.ecoinf.2024.102878","url":null,"abstract":"<div><div>Acoustic surveys play a pivotal role in fisheries management. During the surveys, acoustic signals are sent into the water and the strength of the reflection, so-called backscatter, is recorded. The collected data are typically annotated manually, a process that is both labor-intensive and time-consuming, to support acoustic target classification (ATC). The primary objective of this study is to develop an annotation-free deep learning model that extracts acoustic features and improves the representation of acoustic data. For this purpose, we adopt a self-supervised method inspired by the Self DIstillation with NO Labels (DINO) model. Extracting useful acoustic features is an intricate task due to the inherent variability and complexity in biological targets, as well as environmental and technical factors influencing sound interactions. The proposed model is trained with three sampling methods: random sampling, which ignores class imbalance present in the acoustic survey data; class-balanced sampling, which ensures equal representation of known categories; and intensity-based sampling, which selects data to capture backscatter variations. The quality of extracted features is then evaluated and compared. We show that the extracted features lead to improvement, in comparison to using the untreated data, in the discriminative power of several machine learning methods (k-nearest neighbor (kNN), linear regression, multinomial logistic regression) for ATC. The improvement was measured through higher accuracy in kNN (77.55% vs. 71.93%), Macro AUC in logistic regression (0.92 vs. 0.80), and <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> in linear regression (0.69 vs. 0.45) when comparing extracted features to the untreated data. Our findings highlight the advantage of applying emerging self-supervised techniques in fisheries acoustics. This study thus contributes to the ongoing efforts to improve the efficiency of acoustic surveys in fisheries management.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102878"},"PeriodicalIF":5.8,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tracing the range shifts of African tree ferns: Insights from the last glacial maximum and beyond","authors":"Mwihaki J. Karichu ,&nbsp;Boniface K. Ngarega ,&nbsp;Joyce M. Jefwa ,&nbsp;Bette A. Loiselle ,&nbsp;Emily B. Sessa","doi":"10.1016/j.ecoinf.2024.102896","DOIUrl":"10.1016/j.ecoinf.2024.102896","url":null,"abstract":"<div><div>African tropical forests are experiencing rapid decline as a result of several factors, including increasing population pressure, recurrent wildfires, selective logging practices, land use changes, intensified agricultural activities, and other social and economic issues. Using MaxEnt, paleoclimatic data, and future climate scenarios, the present study seeks to explore the presence of tree ferns in tropical and Saharan Africa during the Last Glacial Maximum, African Holocene Humid Period (AHHP; ca. 14,500–5000 years ago) and to project the effects of climate change on the distribution of tree ferns in Africa under two future climatic scenarios, Representation Concentration Pathways (RCP) 4.5 and 8.5. Our study reveals that despite a significant increase in precipitation during the AHHP, precipitation distribution was variable and insufficient to support the five tree fern species examined in this study. While some tree fern species have experienced range shifts over time, we found that most of them have maintained their presence within refuge areas that probably endured the late Pleistocene extinction event. These refugia provided a haven for some tree ferns, allowing them to persist and survive amidst challenging and varying environmental conditions. This highlights tree ferns' remarkable adaptability in changing climate as well as the critical importance of these refugial areas in safeguarding their populations during climatic upheaval. Our study further demonstrates that different species respond to climate change differently, with some experiencing minimal range contractions of 2.0 %, up to more than 57.0 % range expansion in other species. Preserving refugia not only safeguards tree fern populations but also contributes to conserving overall forest biodiversity and ecosystem functioning. This knowledge is crucial for implementing targeted conservation actions that promote sustainable forest management and can mitigate the threats posed by climate change and anthropogenic activities in African closed wet forests.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102896"},"PeriodicalIF":5.8,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving the explainability of CNN-LSTM-based flood prediction with integrating SHAP technique","authors":"Hao Huang ,&nbsp;Zhaoli Wang ,&nbsp;Yaoxing Liao ,&nbsp;Weizhi Gao ,&nbsp;Chengguang Lai ,&nbsp;Xushu Wu ,&nbsp;Zhaoyang Zeng","doi":"10.1016/j.ecoinf.2024.102904","DOIUrl":"10.1016/j.ecoinf.2024.102904","url":null,"abstract":"<div><div>Convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) are popular deep learning architectures currently used for rapid flood simulations. However, deep learning algorithms are difficult to explain, like a “black box” that lacks insight. In order to reveal the intrinsic mechanism of prediction by such architectures, we adopted a coupled CNN-LSTM model based on the explainability technique SHapley Additive exPlanations (SHAP) to predict the rainfall-runoff process and identify key input feature factors, and took the Beijiang River Basin in China as an example, so as to improve the explainability and credibility of this black-box model. The results show that the coupled CNN-LSTM model performs better than the flood predictions compared to the individual CNN or LSTM models under the longest foresight period of 25 h. In particular, the Nash-Sutcliffe Efficiency (NSE) of the former model reaches 0.838, while those of the latter two models are 0.737 and 0.745, respectively. The coupled CNN-LSTM model has a high-accuracy prediction capability, consistently exhibiting NSEs greater than 0.8 for different input time steps and foresight periods. The prediction accuracy is mainly influenced by the observed runoff at the downstream hydrological station from previous time points, while the effects of the input time step length and the foresight period are comparatively negligible. This study provides a new perspective for understanding the potential physical mechanism of black-box models for rainfall-runoff prediction and emphasizes the importance and prospect of the application of explainability techniques.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102904"},"PeriodicalIF":5.8,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}