Meteorological Variables Forecasting System Using Machine Learning and Open-Source Software DOI Open Access

Jenny Aracely Segovia,

Jonathan Fernando Toaquiza,

Jacqueline Llanos

и другие.

Electronics, Год журнала: 2023, Номер 12(4), С. 1007 - 1007

Опубликована: Фев. 17, 2023

The techniques for forecasting meteorological variables are highly studied since prior knowledge of them allows the efficient management renewable energies, and also other applications science such as agriculture, health, engineering, energy, etc. In this research, design, implementation, comparison models have been performed using different Machine Learning part Python open-source software. implemented include multiple linear regression, polynomial random forest, decision tree, XGBoost, multilayer perceptron neural network (MLP). To identify best technique, mean square error (RMSE), absolute percentage (MAPE), (MAE), coefficient determination (R2) used evaluation metrics. most depend on variable to be forecasting, however, it is noted that them, forest XGBoost present better performance. For temperature, performing technique was Random Forest with an R2 0.8631, MAE 0.4728 °C, MAPE 2.73%, RMSE 0.6621 °C; relative humidity, 0.8583, 2.1380RH, 2.50% 2.9003 RH; solar radiation, 0.7333, 65.8105 W/m2, 105.9141 W/m2; wind speed, 0.3660, 0.1097 m/s, 0.2136 m/s.

Язык: Английский

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

V. G. Dhanya,

A. Subeesh,

Nand Lal Kushwaha

и другие.

Artificial Intelligence in Agriculture, Год журнала: 2022, Номер 6, С. 211 - 229

Опубликована: Янв. 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.

Язык: Английский

Процитировано

170
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

и другие.

Environmental Science and Pollution Research, Год журнала: 2023, Номер 30(15), С. 43183 - 43202

Опубликована: Янв. 17, 2023

Язык: Английский

Процитировано

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, Год журнала: 2023, Номер unknown, С. 503 - 520

Опубликована: Янв. 1, 2023

Язык: Английский

Процитировано

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

и другие.

Water Resources Management, Год журнала: 2023, Номер 37(3), С. 1367 - 1399

Опубликована: Фев. 1, 2023

Язык: Английский

Процитировано

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

и другие.

Journal of Cleaner Production, Год журнала: 2024, Номер 444, С. 141035 - 141035

Опубликована: Фев. 8, 2024

Язык: Английский

Процитировано

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

и другие.

Earth Science Informatics, Год журнала: 2025, Номер 18(1)

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

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

и другие.

Land, Год журнала: 2022, Номер 11(11), С. 2040 - 2040

Опубликована: Ноя. 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.

Язык: Английский

Процитировано

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, Год журнала: 2023, Номер 15(2), С. 1109 - 1109

Опубликована: Янв. 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

Язык: Английский

Процитировано

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

и другие.

Pure and Applied Geophysics, Год журнала: 2023, Номер 180(1), С. 335 - 363

Опубликована: Янв. 1, 2023

Язык: Английский

Процитировано

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

и другие.

Civil Engineering Journal, Год журнала: 2023, Номер 9(11), С. 2630 - 2648

Опубликована: Ноя. 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

Язык: Английский

Процитировано

37