Assessment of trends of land surface vegetation distribution, snow cover and temperature over entire Himachal Pradesh using MODIS datasets

Haq, Mohd Anul; Baral, Prashant; Yaragal, Shivaprakash; Rahaman, G. M. Atiqur

doi:10.1111/nrm.12262

Cited by 25 publications

(17 citation statements)

References 36 publications

(48 reference statements)

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“…The present investigation contributes to the environmental modeling knowledge based on three important environmental variables and their associated responses. The present study highlights the effect of changing climate on snow cover and vegetation 3 . Additionally, the current investigation performed statistical trends change analysis to know the temporal influence of these three important environmental parameters which serve as important indicators of climate change effects.…”

Section: Introductionmentioning

confidence: 70%

Analysis of environmental factors using AI and ML methods

Haq

Ahmed

Khan

et al. 2022

Sci Rep

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The main goal of this research paper is to apply a deep neural network model for time series forecasting of environmental variables. Accurate forecasting of snow cover and NDVI are important issues for the reliable and efficient hydrological models and prediction of the spread of forest. Long Short Term Memory (LSTM) model for the time series forecasting of snow cover, temperature, and normalized difference vegetation index (NDVI) are studied in this research work. Artificial neural networks (ANN) are widely used for forecasting time series due to their adaptive computing nature. LSTM and Recurrent neural networks (RNN) are some of the several architectures provided in a class of ANN. LSTM is a kind of RNN that has the capability of learning long-term dependencies. We followed a coarse-to-fine strategy, providing reviews of various related research materials and supporting it with the LSTM analysis on the dataset of Himachal Pradesh, as gathered. Environmental factors of the Himachal Pradesh region are forecasted using the dataset, consisting of temperature, snow cover, and vegetation index as parameters from the year 2001–2017. Currently, available tools and techniques make the presented system more efficient to quickly assess, adjust, and improve the environment-related factors analysis.

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Section: Introductionmentioning

confidence: 70%

Analysis of environmental factors using AI and ML methods

Haq

Ahmed

Khan

et al. 2022

Sci Rep

Self Cite

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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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“…Statistics of Mann-Kendall S value [22,23] were evaluated for chronologically placed observations in the time series Eq. (11). The observations VAR(S) variance in the time series was also estimated as per Eq.…”

Section: Trend Analysismentioning

confidence: 99%

“…A study by [10] provides a comprehensive report on climate change in the Himalayas and suggests modeling future climate status, especially for the Himalayan region. [11,12] applied Dl techniques such as DNN and RNN for weather forecasting; however, the scope and data used in their investigation were limited. However, significant issues with these studies are rigorous parameter tuning, cross-validation of the model on different data, and computational efficiency.…”

Section: Introductionmentioning

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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Assessment of trends of land surface vegetation distribution, snow cover and temperature over entire Himachal Pradesh using MODIS datasets

Cited by 25 publications

References 36 publications

Analysis of environmental factors using AI and ML methods

Analysis of environmental factors using AI and ML methods

Fusion-Based Deep Learning Model for Hyperspectral Images Classification

CDLSTM: A Novel Model for Climate Change Forecasting

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