scholarly journals Bulk Processing of Multi-Temporal Modis Data, Statistical Analyses and Machine Learning Algorithms to Understand Climate Variables in the Indian Himalayan Region

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7416
Author(s):  
Mohd Anul Haq ◽  
Prashant Baral ◽  
Shivaprakash Yaragal ◽  
Biswajeet Pradhan
Keyword(s):  
Machine Learning ◽  
Global Climate ◽  
Learning Algorithms ◽  
Remotely Sensed ◽  
Machine Learning Algorithms ◽  
Himalayan Region ◽  
Lapse Rate ◽  
Climate Data ◽  
Modis Data ◽  
Bulk Processing

Studies relating to trends of vegetation, snowfall and temperature in the north-western Himalayan region of India are generally focused on specific areas. Therefore, a proper understanding of regional changes in climate parameters over large time periods is generally absent, which increases the complexity of making appropriate conclusions related to climate change-induced effects in the Himalayan region. This study provides a broad overview of changes in patterns of vegetation, snow covers and temperature in Uttarakhand state of India through bulk processing of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological records and simulated global climate data. Additionally, regression using machine learning algorithms such as Support Vectors and Long Short-term Memory (LSTM) network is carried out to check the possibility of predicting these environmental variables. Results from 17 years of data show an increasing trend of snow-covered areas during pre-monsoon and decreasing vegetation covers during monsoon since 2001. Solar radiation and cloud cover largely control the lapse rate variations. Mean MODIS-derived land surface temperature (LST) observations are in close agreement with global climate data. Future studies focused on climate trends and environmental parameters in Uttarakhand could fairly rely upon the remotely sensed measurements and simulated climate data for the region.

scholarly journals Wetland Change Mapping Using Machine Learning Algorithms, and Their Link with Climate Variation and Economic Growth: A Case Study of Guangling County, China

2021 ◽  
Vol 14 (1) ◽  
pp. 439
Author(s):  
Gadisa Fayera Gemechu ◽  
Xiaoping Rui ◽  
Haiyue Lu
Keyword(s):  
Machine Learning ◽  
Economic Growth ◽  
Learning Algorithms ◽  
Remotely Sensed ◽  
Google Earth ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Kappa Statistics ◽  
Wetland Area ◽  
Kernel Approach

Wetlands are a distinctive terrestrial ecosystem that benefits living things, including people, in various ways. Sustainable wetland ecosystem resources are needed to protect the global environment. Wetlands in China have undergone positive and negative changes in response to several factors, but studies documenting their long-term dynamicity have been few, particularly in Guangling County. This study examines the change of wetlands area based on remotely sensed data while exploring trends associated with climate variations and economic growth in Guangling County, China. Analysis of remotely sensed imagery, mainly in hilly and nonhomogeneous environments is problematic, largely as a result of interference and their high spectral non-homogeneity. We conducted experiments using five classical machine learning algorithms based on the Google Earth Engine (GEE) and obtained the greatest robustness and accuracy using a Support Vector Machine (SVM)—Radial Basis Function (RBF) kernel approach, with overall accuracy and kappa statistics ranging from 86% to 98.1% and from 0.789 to 0.960, respectively. Based on the SVM-RBF model’s outperformance of four other algorithms, we identified spatial distributions of wetland in the study area and associated change trends. We found that 45.71 km2 of wetland area was lost over the past 3.7 decades (January 1984–December 2020), or 81.82% of wetland area coverage. In this paper, we explore how factors such as county economic growth (GDP), humidity, and temperature variations are tightly linked with wetland change.

scholarly journals Annotation-free Learning of Plankton for Classification and Anomaly Detection

2019 ◽  
Author(s):  
Vito P. Pastore ◽  
Thomas G. Zimmerman ◽  
Sujoy Biswas ◽  
Simone Bianco
Keyword(s):  
Machine Learning ◽  
Global Climate ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Marine Phytoplankton ◽  
Supervised Machine Learning ◽  
Woods Hole Oceanographic Institution ◽  
Accurate Detection ◽  
Plankton Species

AbstractThe acquisition of increasingly large plankton digital image datasets requires automatic methods of recognition and classification. As data size and collection speed increases, manual annotation and database representation are often bottlenecks for utilization of machine learning algorithms for taxonomic classification of plankton species in field studies. In this paper we present a novel set of algorithms to perform accurate detection and classification of plankton species with minimal supervision. Our algorithms approach the performance of existing supervised machine learning algorithms when tested on a plankton dataset generated from a custom-built lensless digital device. Similar results are obtained on a larger image dataset obtained from the Woods Hole Oceanographic Institution. Our algorithms are designed to provide a new way to monitor the environment with a class of rapid online intelligent detectors.Author SummaryPlankton are at the bottom of the aquatic food chain and marine phytoplankton are estimated to be responsible for over 50% of all global primary production [1] and play a fundamental role in climate regulation. Thus, changes in plankton ecology may have a profound impact on global climate, as well as deep social and economic consequences. It seems therefore paramount to collect and analyze real time plankton data to understand the relationship between the health of plankton and the health of the environment they live in. In this paper, we present a novel set of algorithms to perform accurate detection and classification of plankton species with minimal supervision. The proposed pipeline is designed to provide a new way to monitor the environment with a class of rapid online intelligent detectors.

scholarly journals Monthly rainfall hind-cast using machine learning algorithms for Coimbatore, Tamil Nadu

MAUSAM ◽  
2022 ◽  
Vol 73 (1) ◽  
pp. 19-26
Author(s):  
V. GEETHALAKSHMI ◽  
S. KOKILAVANI ◽  
S.P. RAMANATHAN ◽  
GA. DHEEBAKARAN ◽  
N.K. SATHYAMOORTHY ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tamil Nadu ◽  
Southern Oscillation ◽  
Global Climate ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Monthly Rainfall ◽  
Research Centre ◽  
Gradient Boosting ◽  
Forecast Performance

  Due to current world climate change, the accuracy of predicting rainfall is critical. This paper presents an approach using four different machine learning algorithms, viz., Decision Tree Regression (DTR), Gradient Boosting (GB), Ada Boost (AB) and Random Forest Regression (RFR) techniques to improve the rainfall forecast performance. When historical events are entered into the model and get validated to realise how well the output suits the known results referred as Hind-cast. Historical monthly weather parameters over a period of 42 years (1976 to 2017) were collected from Agro Climate Research Centre, Tamil Nadu Agricultural University. The global climate driver’s viz., Southern Oscillation Index and Indian Ocean Dipole indices were retrieved from Bureau of Meteorology, Australia. K- means algorithm was employed for centroid identification (which select the rows with unique distinguished features) at 90 per cent of the original data for the period of 42 years by eliminating the redundancy nature of the datawhich were used as training set. The result indicated the supremacy and notable strength of RFR over the other algorithms in terms of performance with 89.2 per cent. The Co-efficient of Determination (R2) for the predicted and observed values was found to be 0.8 for the monthly rainfall from 2015 to 2017.  

scholarly journals ASSESSMENT AND COMPARISON OF MACHINE LEARNING ALGORITHM CAPABILITY IN SPATIAL MODELING OF DENGUE FEVER VULNERABILITY BASED ON LANDSAT IMAGE 8 OLI/TIRS

2021 ◽  
Vol 13 (2) ◽  
pp. 211
Author(s):  
Rahmat Azul Mizan ◽  
Prima Widayani ◽  
Nur Mohammad Farda
Keyword(s):  
Machine Learning ◽  
Dengue Fever ◽  
Spatial Modeling ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Landsat 8 ◽  
Climate Data ◽  
Major Health Problem

The spread of dengue fever in Indonesia has become a major health problem. Spatial modeling for the distribution of dengue fever vulnerability is an important step to support the planning and mitigation of dengue fever in Indonesia. This study aims to assess and compare the capability of two machine learning algorithms to create a spatial model of dengue fever vulnerability. The research was conducted in Baubau City, Southeast Sulawesi Province by taking 129 cases that occurred from 2015 to February 2016. In this study, the model was created using R software and machine learning algorithms including support vector machine (SVM) and random forest (RF). The six modeling variables involved include land use/cover, BLFEI, NDVI, LST, rainfall and humidity extracted from Landsat 8 OLI/TIRS imagery as well as BMKG (Meteorological, Climatological, and Geophysical Agency of Indonesia) and BWS climate data. The model's capability was assessed using the Area Under Curve-Receiver Operating Characteristic (AUC-ROC) curve. The results of the research show that both algorithms provide excellent model accuracy with AUC values of 1 for SVM and 0.997 for RF with SVM as the best algorithm for modeling dengue fever in Baubau City.Keywords: Machine Learning, Vulnerability, Dengue Fever, Landsat 8 Image

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