Efficiency of artificial neural networks for glacier ice-thickness estimation: a case study in western Himalaya, India

Haq, Mohd Anul; Azam, Mohd Farooq; Vincent, Christian

doi:10.1017/jog.2021.19

Cited by 41 publications

(25 citation statements)

References 68 publications

(119 reference statements)

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“…The developed CNNLVQ demonstrates promising classification results, with an overall accuracy of 99.44%. The novelty of the present investigation was The future scope of the present research will be to collect more images for different areas [24][25][26][27]. Generalization testing of the current high accuracy model will be of more interest to apply cropwise weed detection for different areas.…”

Section: Discussionmentioning

confidence: 99%

CNN Based Automated Weed Detection System Using UAV Imagery

Haq¹

2022

Computer Systems Science and Engineering

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The problem of weeds in crops is a natural problem for farmers. Machine Learning (ML), Deep Learning (DL), and Unmanned Aerial Vehicles (UAV) are among the advanced technologies that should be used in order to reduce the use of pesticides while also protecting the environment and ensuring the safety of crops. Deep Learning-based crop and weed identification systems have the potential to save money while also reducing environmental stress. The accuracy of ML/DL models has been proven to be restricted in the past due to a variety of factors, including the selection of an efficient wavelength, spatial resolution, and the selection and tuning of hyperparameters. The purpose of the current research is to develop a new automated weed detecting system that uses Convolution Neural Network (CNN) classification for a real dataset of 4400 UAV pictures with 15336 segments. Snapshots were used to choose the optimal parameters for the proposed CNN LVQ model. The soil class achieved the user accuracy of 100% with the proposed CNN LVQ model, followed by soybean (99.79%), grass (98.58%), and broadleaf (98.32%). The developed CNN LVQ model showed an overall accuracy of 99.44% after rigorous hyperparameter tuning for weed detection, significantly higher than previously reported studies.

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Section: Discussionmentioning

confidence: 99%

CNN Based Automated Weed Detection System Using UAV Imagery

Haq¹

2022

Computer Systems Science and Engineering

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“…The future scope of the present investigation is to add more data on snow retreat, glacier melt, agricultural yield, and demographics to assess the complete cycle of climate change for the Himalayan region. Another future scope of the present investigation is to implements and assimilate the latest state of the art models for climate modeling and forecasting [30][31][32][33][34].…”

Section: Discussionmentioning

confidence: 99%

CDLSTM: A Novel Model for Climate Change Forecasting

Haq¹

2022

Computers, Materials &Amp; Continua

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Water received in rainfall is a crucial natural resource for agriculture, the hydrological cycle, and municipal purposes. The changing rainfall pattern is an essential aspect of assessing the impact of climate change on water resources planning and management. Climate change affected the entire world, specifically India's fragile Himalayan mountain region, which has high significance due to being a climatic indicator. The water coming from Himalayan rivers is essential for 1.4 billion people living downstream. Earlier studies either modeled temperature or rainfall for the Himalayan area; however, the combined influence of both in a long-term analysis was not performed utilizing Deep Learning (DL). The present investigation attempted to analyze the time series and correlation of temperature (1796-2013) and rainfall changes over the Himalayan states in India. The Climate Deep Long Short-Term Memory (CDLSTM) model was developed and optimized to forecast all Himalayan states' temperature and rainfall values. Facebook's Prophet (FB-Prophet) model was implemented to forecast and assess the performance of the developed CDLSTM model. The performance of both models was assessed based on various performance metrics and shown significantly higher accuracies and low error rates.

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“…To decrease the redundancy of the APs, chiefly when considered in their protracted design, it has been anticipated to practice dimensionality decrease methods. In conclusion, although the FDLM has verified to be operative in the scrutiny of distant distinguishing imageries predominantly for classification, many areas of study remain as a future scope [22][23][24][25][26].…”

Section: Discussionmentioning

confidence: 99%

Fusion-Based Deep Learning Model for Hyperspectral Images Classification

Kriti¹,

Haq²,

Garg³

et al. 2022

Computers, Materials &Amp; Continua

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A crucial task in hyperspectral image (HSI) taxonomy is exploring effective methodologies to effusively practice the 3-D and spectral data delivered by the statistics cube. For classification of images, 3-D data is adjudged in the phases of pre-cataloging, an assortment of a sample, classifiers, post-cataloging, and accurateness estimation. Lastly, a viewpoint on imminent examination directions for proceeding 3-D and spectral approaches is untaken. In topical years, sparse representation is acknowledged as a dominant classification tool to effectually labels deviating difficulties and extensively exploited in several imagery dispensation errands. Encouraged by those efficacious solicitations, sparse representation (SR) has likewise been presented to categorize HSI's and validated virtuous enactment. This research paper offers an overview of the literature on the classification of HSI technology and its applications. This assessment is centered on a methodical review of SR and support vector machine (SVM) grounded HSI taxonomy works and equates numerous approaches for this matter. We form an outline that splits the equivalent mechanisms into spectral aspects of systems, and spectral-spatial feature networks to methodically analyze the contemporary accomplishments in HSI taxonomy. Furthermore, cogitating the datum that accessible training illustrations in the remote distinguishing arena are generally appropriate restricted besides training neural networks (NNs) to necessitate an enormous integer of illustrations, we comprise certain approaches to increase taxonomy enactment, which can deliver certain strategies for imminent learnings on this issue. Lastly, numerous illustrative neural learning-centered taxonomy approaches are piloted on physical HSI's in our experimentations.

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Efficiency of artificial neural networks for glacier ice-thickness estimation: a case study in western Himalaya, India

Cited by 41 publications

References 68 publications

CNN Based Automated Weed Detection System Using UAV Imagery

CNN Based Automated Weed Detection System Using UAV Imagery

CDLSTM: A Novel Model for Climate Change Forecasting

Fusion-Based Deep Learning Model for Hyperspectral Images Classification

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