A support vector machine based drought index for regional drought analysis DOI Creative Commons

Mohammed Alshahrani,

Muhammad Laiq, Muhammad Noor‐ul‐Amin

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: April 29, 2024

Abstract The increased global warming has the likelihood of recurrent drought hazards. Potential links between frequency extreme weather events and have been suggested by earlier research. spatial variability meteorological factors over short distances can cause distortions in conclusions or limit scope analysis a particular region when values predominate. Therefore, it is challenging to make trustworthy judgments regarding spatiotemporal characteristics regional drought. This study aims improve quality accuracy characterization process continuous monitoring. new indicator presented this called Support Vector Machine based index (SVM-DI). It created adding different weights an SVM-based X-bar chart that displayed with precipitation aggregate data. SVM-DI application site located Pakistan's northern area. Using Pearson correlation coefficient for pairwise comparison, compares Regional Standard Precipitation Index (RSPI). Interestingly, compared RSPI, shows more pronounced its correlations other stations, significantly lower Coefficient Variation. These results confirm useful tool analysis. methodology offers unique way reduce impact outliers aggregating

Language: Английский

Deep learning based computer vision approaches for smart agricultural applications DOI Creative Commons

V. G. Dhanya,

A. Subeesh,

Nand Lal Kushwaha

et al.

Artificial Intelligence in Agriculture, Journal Year: 2022, Volume and Issue: 6, P. 211 - 229

Published: Jan. 1, 2022

The agriculture industry is undergoing a rapid digital transformation and growing powerful by the pillars of cutting-edge approaches like artificial intelligence allied technologies. At core intelligence, deep learning-based computer vision enables various activities to be performed automatically with utmost precision enabling smart into reality. Computer techniques, in conjunction high-quality image acquisition using remote cameras, enable non-contact efficient technology-driven solutions agriculture. This review contributes providing state-of-the-art technologies based on learning that can assist farmers operations starting from land preparation harvesting operations. Recent works area were analyzed this paper categorized (a) seed quality analysis, (b) soil (c) irrigation water management, (d) plant health (e) weed management (f) livestock (g) yield estimation. also discusses recent trends such as generative adversarial networks (GAN), transformers (ViT) other popular architectures. Additionally, study pinpoints challenges implementing farmer’s field real-time. overall finding indicates convolutional neural are corner stone modern their architectures provide across terms accuracy. However, success approach lies building model dataset real-time solutions.

Language: Английский

Citations

167
Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models DOI
Ahmed Elbeltagi,

Chaitanya B. Pande,

Manish Kumar

et al.

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(15), P. 43183 - 43202

Published: Jan. 17, 2023

Language: Английский

Citations

74
Application of Hyperspectral Remote Sensing Role in Precision Farming and Sustainable Agriculture Under Climate Change: A Review DOI

Chaitanya B. Pande,

Kanak N. Moharir

Springer climate, Journal Year: 2023, Volume and Issue: unknown, P. 503 - 520

Published: Jan. 1, 2023

Language: Английский

Citations

52
Comparative Assessment of Improved SVM Method under Different Kernel Functions for Predicting Multi-scale Drought Index DOI

Chaitanya B. Pande,

Nand Lal Kushwaha, Israel R. Orimoloye

et al.

Water Resources Management, Journal Year: 2023, Volume and Issue: 37(3), P. 1367 - 1399

Published: Feb. 1, 2023

Language: Английский

Citations

51
Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development DOI

Chaitanya B. Pande,

Johnbosco C. Egbueri, Romulus Costache

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 444, P. 141035 - 141035

Published: Feb. 8, 2024

Language: Английский

Citations

41
Comparison of LSTM and SVM methods through wavelet decomposition in drought forecasting DOI
Türker Tuğrul, Mehmet Ali Hınıs, Sertaç Oruç

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(1)

Published: Jan. 1, 2025

Language: Английский

Citations

3
Forecasting of SPI and Meteorological Drought Based on the Artificial Neural Network and M5P Model Tree DOI Creative Commons

Chaitanya B. Pande,

Nadhir Al‐Ansari, Nand Lal Kushwaha

et al.

Land, Journal Year: 2022, Volume and Issue: 11(11), P. 2040 - 2040

Published: Nov. 14, 2022

Climate change has caused droughts to increase in frequency and severity worldwide, which attracted scientists create drought prediction models mitigate the impacts of droughts. One most important challenges addressing is developing accurate predict their discrete characteristics, i.e., occurrence, duration, severity. The current research examined performance several different machine learning models, including Artificial Neural Network (ANN) M5P Tree forecasting widely used measure, Standardized Precipitation Index (SPI), at both time scales (SPI 3, SPI 6). model was developed utilizing rainfall data from two stations India (i.e., Angangaon Dahalewadi) for 2000–2019, wherein first 14 years are employed training, while remaining six validation. subset regression analysis performed on 12 input combinations choose best combination 3 6. sensitivity carried out given find effective parameter forecasting. all ANN (4, 5), (5, 6), (6, 7), assessed through statistical indicators, namely, MAE, RMSE, RAE, RRSE, r. results revealed that (t-1) sensitive parameters with highest values β = 0.916, 1.017, respectively, SPI-3 SPI-6 7 (SPI-1/SPI-3/SPI-4/SPI-5/SPI-8/SPI-9/SPI-11) 4 (SPI-1/SPI-2/SPI-6/SPI-7) based higher R2 Adjusted lowest MSE values. It clear r lesser RMSE as compared 7) models. Therefore, superior other stations.

Language: Английский

Citations

57
Prediction of Streamflow Drought Index for Short-Term Hydrological Drought in the Semi-Arid Yesilirmak Basin Using Wavelet Transform and Artificial Intelligence Techniques DOI Open Access
Okan Mert Katipoğlu

Sustainability, Journal Year: 2023, Volume and Issue: 15(2), P. 1109 - 1109

Published: Jan. 6, 2023

The prediction of hydrological droughts is vital for surface and ground waters, reservoir levels, hydroelectric power generation, agricultural production, forest fires, climate change, the survival living things. This study aimed to forecast 1-month lead-time in Yesilirmak basin. For this purpose, support vector regression, Gaussian process regression tree, ensemble tree models were used alone combination with a discrete wavelet transform. Streamflow drought index values determine droughts. data divided into 70% training (1969–1998) 30% (1999–2011) testing. performance was evaluated according various statistical criteria such as mean square error, root means absolute determination coefficient. As result, it determined that obtained by decomposing subcomponents transform optimal. In addition, most effective drought-predicting model using db10 MGPR algorithm squared error 0.007, 0.08, 0.04, coefficient (R2) 0.99 at station 1413. weakest stand-alone FGSV (RMSE 0.88, RMSE 0.94, MAE 0.76, R2 0.14). Moreover, revealed main more accurate predicting short-term than other wavelets. These results provide essential information decision-makers planners manage

Language: Английский

Citations

38
Application of Innovative Machine Learning Techniques for Long-Term Rainfall Prediction DOI
Suman Markuna, Pankaj Kumar, Rawshan Ali

et al.

Pure and Applied Geophysics, Journal Year: 2023, Volume and Issue: 180(1), P. 335 - 363

Published: Jan. 1, 2023

Language: Английский

Citations

38
Enhancing Environmental Sustainability in a Critical Region: Climate Change Impacts on Agriculture and Tourism DOI Open Access
Kazem Javan,

Mehrdad Mirabi,

Sajad Ahmad Hamidi

et al.

Civil Engineering Journal, Journal Year: 2023, Volume and Issue: 9(11), P. 2630 - 2648

Published: Nov. 1, 2023

The Ardabil Plain is pivotal in the national agricultural sector and ranks among leading horticultural production provinces. primary objective of this study to enhance environmental sustainability critical vulnerable region, particularly face imminent droughts climate change. examines impacts change on agriculture tourism area. It puts forward suggestions for implementing sustainable practices safeguard well-being local population. results indicate a 38% reduction precipitation, especially autumn season, with possible alteration timing strength rainfall. Also, notable decline volume, specific region plain, has been observed. currently produces 284,182 tons wheat, 204,980 from irrigated crops 79,202 rain-fed crops. However, projected future scenario indicates decrease total wheat 209,196 tons, 160,125 49,071 This expected lead net income loss approximately -$75,389,059, -$45,095,663 attributed -$30,293,396 findings suggest that availability water sources certain regions may prompt shift farming land north south plain promote sustainability. demographic could have significant financial social implications region's growth prosperity. Moreover, increasing temperatures western northern pose flood risks uncomfortable travel conditions, concerning given reliance potential unemployment consequences. becomes imperative adopt manage resources effectively ensure resilience prosperity challenges. Doi: 10.28991/CEJ-2023-09-11-01 Full Text: PDF

Language: Английский

Citations

37