River sensing: the inclusion of red band in predicting reach-scale types using machine learning algorithms

Olusola, Adeyemi; Onafeso, Olumide; Fashae, Olutoyin Adeola; Adelabu, Samuel

doi:10.1080/02626667.2022.2098752

Cited by 4 publications

(3 citation statements)

References 77 publications

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“…If the model RMSE is significantly reduced due to the introduction of feature variables, such features are retained for their eventual participation in the modeling; otherwise, such variables are removed. RF-RFE quantifies the modeling relevance of each feature based on its classification contribution [63][64][65].…”

Section: Feature Preference and Importance Rankingmentioning

confidence: 99%

Forest Tree Species Diversity Mapping Using ICESat-2/ATLAS with GF-1/PMS Imagery

Yang

Shu

Zhang

et al. 2023

Forests

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Forest ecosystems depend on species of tree variety. Remote sensing for obtaining large-scale spatial distribution information of tree species diversity is a geoscience research hotspot to overcome the limitations of conventional tree species diversity survey approaches. Airborne LiDAR or synergy with airborne optical imagery has been used to model and estimate tree species diversity for specific forest communities, with many revealing results. However, the data collection for such research is costly, the breadth of monitoring findings is limited, and obtaining information on the geographical pattern is challenging. To this end, we propose a method for mapping forest tree species diversity by synergy satellite optical remote sensing and satellite-based LiDAR based on the spectral heterogeneity hypothesis and structural variation hypothesis to improve the accuracy of the remote sensing monitoring of forest tree species diversity while considering data cost. The method integrates horizontal spectral variation from GF-1/PMS image data with vertical structural variation from ICESat-2 spot data to estimate the species diversity of trees. The findings reveal that synergistic horizontal spectral variation and vertical structural variation overall increase tree species diversity prediction accuracy compared to a single remote sensing variation model. The synergistic approach improved Shannon and Simpson indices prediction accuracy by 0.06 and 0.04, respectively, compared to the single horizontal spectral variation model. The synergistic model, single vertical structural variation model, and single horizontal spectral variation model were the best prediction models for Shannon, Simpson, and richness indices, with R2 of 0.58, 0.62, and 0.64, respectively. This research indicates the potential of synergistic satellite-based LiDAR and optical remote sensing in large-scale forest tree species diversity mapping.

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Section: Feature Preference and Importance Rankingmentioning

confidence: 99%

Forest Tree Species Diversity Mapping Using ICESat-2/ATLAS with GF-1/PMS Imagery

Yang

Shu

Zhang

et al. 2023

Forests

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“…Rainfall has a significant influence on downstream hydrology and flooding resulting from runoff with a range of complications for water quality, land-use structures, agriculture, sewage system, tourism, and in general impacts on the quality of life [5]. With these, early warning of such is both critical and imperative in managing water resources [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

“…Our study is motivated by [11]- [14] noting that: i) many models still have the issues of calibration and model validation resulting from the limited availability of datasets vis-à-vis the heterogeneity of the rainfall scheme that poises the model to relearn feats and parameters that are often difficult to understudy [15], [16] and ii) formulating such optimization tasks often requires carefully selected parameters-and yield an outcome that may amend previously considered variables. The careful selection of hyperparameters will yield an optimal solution, devoid a model of over-parameterization, and overfitting as well as vary with each problem domain [5], [17]. To overcome the stated pitfalls, we propose hybrid deep-learning runoff ensemble with the Benin-Owena River Basin development authority (BORDA) dataset retrieved from the National Metrological Centre in Lagos State, Nigeria.…”

Section: Introductionmentioning

confidence: 99%

Predicting rainfall runoff in Southern Nigeria using a fused hybrid deep learning ensemble

Ojugo,

Ejeh,

Odiakaose

et al. 2024

IJ-ICT

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Rainfall as an environmental feat can change fast and yield significant influence in downstream hydrology known as runoff with a variety of implications such as erosion, water quality, and infrastructures. These, in turn impact the quality of life, sewage systems, agriculture, and tourism of a nation to mention a few. It chaotic, complex, and dynamic nature has necessitated studies in the quest for future direction of such runoff via prediction models. With little successes in use of knowledge driven models, many studies have now turned to data-driven models. Dataset is retrieved from Metrological Center in Lagos, Nigeria for the period 1999-2019 for the Benin-Owena River Basin. Data is split: 70% for train and 30% for test. Our study adapts a spatial-temporal profile hidden Markov trained deep neural network. Result yields a sensitivity of 0.9, specificity 0.19, accuracy of 0.74, and improvement rate of classification of 0.12. Other ensembles underperformed when compared to proposed model. The study reveals annual rainfall is an effect of variation cycle. Models will help simulate future floods and provide lead time warnings in flood management.

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Predicting Rainfall Onset and Cessation Within the West African Sahel Region Using Echo State Network

Olusola,

Ogunjo,

Olusegun

2024

Advances in Science, Technology &Amp; Innovation

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River sensing: the inclusion of red band in predicting reach-scale types using machine learning algorithms

Cited by 4 publications

References 77 publications

Forest Tree Species Diversity Mapping Using ICESat-2/ATLAS with GF-1/PMS Imagery

Forest Tree Species Diversity Mapping Using ICESat-2/ATLAS with GF-1/PMS Imagery

Predicting rainfall runoff in Southern Nigeria using a fused hybrid deep learning ensemble

Predicting Rainfall Onset and Cessation Within the West African Sahel Region Using Echo State Network

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