Estimating Photosynthetically Active Euphotic Layer in Major Lakes of Kumaun Region Using Secchi Depth

Khan, Zainab; Ali, Sk Ajim; Mohsin, Mohammad; Shamim, Syed Kausar; Mankovskaya, Ekaterina; Parvin, Farhana; Bano, Nikhat; Ahmad, Ateeque; Baloch, Muhammad Yousuf Jat

doi:10.1007/s11270-023-06612-1

Cited by 3 publications

(1 citation statement)

References 58 publications

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“…Chemicals and microbial contamination frequently challenge water safety [9][10][11]. Groundwater has taken over as the primary water supply; it is the preferred sole source, particularly in arid and semi-arid regions where precipitation is inadequate and surface water resources are lacking [12][13][14][15][16][17]; also, waste material including sewage sludge on soil can increase crop production but contaminate groundwater [18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms for Predicting the Water Quality Index

Hussein,

Jat Baloch,

Nigar

et al. 2023

Water

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Groundwater is one of the water resources used to preserve natural water sources for drinking, irrigation, and several other purposes, especially in industrial applications. Human activities related to industry and agriculture result in groundwater contamination. Therefore, investigating water quality is essential for drinking and irrigation purposes. In this work, the water quality index (WQI) was used to identify the suitability of water for drinking and irrigation. However, generating an accurate WQI requires much time, as errors may be made during the sub-index calculations. Hence, an artificial intelligence (AI) prediction model was built to reduce both time and errors. Eighty data samples were collected from Sakrand, a city in the province of Sindh, to investigate the area’s WQI. The classification learners were used with raw data samples and the normalized data to select the best classifier among the following decision trees: support vector machine (SVM), k-nearest neighbors (K-NN), ensemble tree (ET), and discrimination analysis (DA). These were included in the classification learner tool in MATLAB. The results revealed that SVM was the best raw and normalized data classifier. The prediction accuracy levels for the training data were 90.8% and 89.2% for the raw and normalized data, respectively. Meanwhile, the prediction accuracy levels for the testing data were 86.67 and 93.33% for the raw and normalized data, respectively.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms for Predicting the Water Quality Index

Hussein,

Jat Baloch,

Nigar

et al. 2023

Water

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Temporal trends and future projections: Analysing land surface temperature in the Kumaun Himalayas using spatial time series analysis

Khan,

Ali,

Ahmad

et al. 2025

Remote Sensing Applications: Society and Environment

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Spatial Time Series Analysis of Warming Patterns in the Kumaun Himalayas: A Future Perspective

Khan,

Ahmad,

Shamim

2024

Preprint

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In this groundbreaking study, we introduce a novel approach to forecasting Land Surface Temperature (LST) in the Kumaun Himalayas, an area critical for understanding regional impacts of global warming. The novelty of our research lies in the utilization of spatial time series analysis, a method not previously applied for future LST prediction. Combined adoption of remote sensing and advanced statistical techniques such as the Simple Moving Average (SMA), Sen’s Slope, and z-statistics with excellent statistical power, our study analyses LST trends from 1990 to 2030 using comprehensive Landsat data. Notably, the application of z-statistics provides a robust framework for assessing temperature changes, with significant findings such as a z-statistics value of -15.04 for spring, indicating a marked shift in temperature patterns. Similarly, for autumn, the z-statistics value of -21.41 underscores a drastic deviation from historical norms i.e., from 1990 to 2020. These values highlight a future that is significantly warmer than the present, bringing into sharp focus the urgency of climate change mitigation and adaptation strategies in this ecologically sensitive region. The study also suggests differential rate of seasonal warming. The study is not only pivotal for local climate policy but also contribute significantly to the broader understanding of climate dynamics in mountainous terrains is seasonal variation in warming rates. Despite challenges like rugged terrain and variable cloud cover affecting data accuracy, our approach offers a scalable model for similar climatic studies in other regions, marking a significant advancement in the field of climate change.

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Estimating Photosynthetically Active Euphotic Layer in Major Lakes of Kumaun Region Using Secchi Depth

Cited by 3 publications

References 58 publications

Machine Learning Algorithms for Predicting the Water Quality Index

Machine Learning Algorithms for Predicting the Water Quality Index

Temporal trends and future projections: Analysing land surface temperature in the Kumaun Himalayas using spatial time series analysis

Spatial Time Series Analysis of Warming Patterns in the Kumaun Himalayas: A Future Perspective

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