scholarly journals Estimating District-Level Electricity Consumption Using Remotely Sensed Data in Eastern Economic Corridor, Thailand

2021 ◽  
Vol 13 (22) ◽  
pp. 4654
Author(s):  
Sirikul Hutasavi ◽  
Dongmei Chen
Keyword(s):  
Machine Learning ◽  
Industrial Development ◽  
Statistical Data ◽  
Local Level ◽  
Electricity Consumption ◽  
District Level ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Landsat 8 ◽  
Remotely Sensed Data

The intensive industrial development in special economic zones, such as Thailand’s Eastern Economic Corridor, increases energy consumption, leading to an imbalance of energy supply and a challenge for energy management. Electricity consumption at a local level is crucial for utility planners to manage and invest in the electrical grid. With this study, we propose an electricity consumption estimation model at the district level using machine learning with publicly available statistical data and built-up area (BU), area of lit (AL), and sum of light intensity (SL) data extracted from Landsat 8 and Suomi NPP satellite nighttime light images. The models created from three machine learning algorithms, which included Multiple Linear Regression (MR), Decision Tree (DT), and Support Vector Regression (SVR), were compared. The results show that (1) electricity consumption is highly correlated with SL, AL, and BU; and (2) the DT model demonstrated a better performance in predicting local electricity consumption when compared to MR and SVR with the lowest error rate and highest R2. The local government in developing countries with limited data and financial resources can adopt the proposed approach to benefit from utilizing commonly available remote sensing and statistical data with simple machine learning models such as DT (regression method) for sustainable electricity management.

scholarly journals Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms

2020 ◽  
Vol 12 (22) ◽  
pp. 3776
Author(s):  
Andrea Tassi ◽  
Marco Vizzari
Keyword(s):  
Machine Learning ◽  
Central Italy ◽  
Object Oriented ◽  
Google Earth ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Landsat 8 ◽  
Good Reliability ◽  
Google Earth Engine ◽  
Occurrence Matrix

Google Earth Engine (GEE) is a versatile cloud platform in which pixel-based (PB) and object-oriented (OO) Land Use–Land Cover (LULC) classification approaches can be implemented, thanks to the availability of the many state-of-art functions comprising various Machine Learning (ML) algorithms. OO approaches, including both object segmentation and object textural analysis, are still not common in the GEE environment, probably due to the difficulties existing in concatenating the proper functions, and in tuning the various parameters to overcome the GEE computational limits. In this context, this work is aimed at developing and testing an OO classification approach combining the Simple Non-Iterative Clustering (SNIC) algorithm to identify spatial clusters, the Gray-Level Co-occurrence Matrix (GLCM) to calculate cluster textural indices, and two ML algorithms (Random Forest (RF) or Support Vector Machine (SVM)) to perform the final classification. A Principal Components Analysis (PCA) is applied to the main seven GLCM indices to synthesize in one band the textural information used for the OO classification. The proposed approach is implemented in a user-friendly, freely available GEE code useful to perform the OO classification, tuning various parameters (e.g., choose the input bands, select the classification algorithm, test various segmentation scales) and compare it with a PB approach. The accuracy of OO and PB classifications can be assessed both visually and through two confusion matrices that can be used to calculate the relevant statistics (producer’s, user’s, overall accuracy (OA)). The proposed methodology was broadly tested in a 154 km2 study area, located in the Lake Trasimeno area (central Italy), using Landsat 8 (L8), Sentinel 2 (S2), and PlanetScope (PS) data. The area was selected considering its complex LULC mosaic mainly composed of artificial surfaces, annual and permanent crops, small lakes, and wooded areas. In the study area, the various tests produced interesting results on the different datasets (OA: PB RF (L8 = 72.7%, S2 = 82%, PS = 74.2), PB SVM (L8 = 79.1%, S2 = 80.2%, PS = 74.8%), OO RF (L8 = 64%, S2 = 89.3%, PS = 77.9), OO SVM (L8 = 70.4, S2 = 86.9%, PS = 73.9)). The broad code application demonstrated very good reliability of the whole process, even though the OO classification process resulted, sometimes, too demanding on higher resolution data, considering the available computational GEE resources.

Latest Advances in Fractional Snow Cover Mapping on MODIS Data by Machine Learning Algorithms

2020 ◽  
Author(s):  
Semih Kuter ◽  
Zuhal Akyurek
Keyword(s):  
Machine Learning ◽  
Snow Cover ◽  
General Circulation ◽  
Snow Water Equivalent ◽  
Machine Learning Algorithms ◽  
Multivariate Adaptive Regression Splines ◽  
Support Vector ◽  
Landsat 8 ◽  
European Alps ◽  
Fractional Snow Cover

<p>Spatial extent of snow has been declared as an essential climate variable. Accurate modeling of snow cover is crucial for the better prediction of snow water equivalent and, consequently, for the success of general circulation and weather forecasting models as well as climate change and hydrological studies. This presentation mainly focuses on the representation of the latest findings of our efforts in fractional snow cover mapping on MODIS images by data-driven machine learning methodologies. For this purpose, a dataset composed of 20 MODIS - Landsat 8 image pairs acquired between Apr 2013 and Dec 2016 over European Alps were employed. Artificial neural networks (ANN), multivariate adaptive regression splines (MARS), support vector regression (SVR) and random forest (RF) models were trained and tested by using reference FSC maps generated from higher spatial resolution Landsat 8 binary snow maps. ANN, MARS, SVR and RF models exhibited quite good performance with average R ≈ 0.93, whereas the agreement between the reference FSC maps and the MODIS’ own product MOD10A1 (C5) was slightly poorer with R ≈ 0.88.</p>

scholarly journals A COMPARISON OF MACHINE-LEARNING REGRESSION ALGORITHMS FOR THE ESTIMATION OF LAI USING LANDSAT - 8 SATELLITE DATA

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 679-683
Author(s):  
V. P. Yadav ◽  
R. Prasad ◽  
R. Bala ◽  
A. K. Vishwakarma ◽  
S. A. Yadav ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Vegetation Index ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Accurate Estimation ◽  
Support Vector ◽  
Landsat 8 ◽  
Area Index ◽  
Global Circulation Models

Abstract. The leaf area index (LAI) is one of key variable of crops which plays important role in agriculture, ecology and climate change for global circulation models to compute energy and water fluxes. In the recent research era, the machine-learning algorithms have provided accurate computational approaches for the estimation of crops biophysical parameters using remotely sensed data. The three machine-learning algorithms, random forest regression (RFR), support vector regression (SVR) and artificial neural network regression (ANNR) were used to estimate the LAI for crops in the present study. The three different dates of Landsat-8 satellite images were used during January 2017 – March 2017 at different crops growth conditions in Varanasi district, India. The sampling regions were fully covered by major Rabi season crops like wheat, barley and mustard etc. In total pooled data, 60% samples were taken for the training of the algorithms and rest 40% samples were taken as testing and validation of the machinelearning regressions algorithms. The highest sensitivity of normalized difference vegetation index (NDVI) with LAI was found using RFR algorithms (R2 = 0.884, RMSE = 0.404) as compared to SVR (R2 = 0.847, RMSE = 0.478) and ANNR (R2 = 0.829, RMSE = 0.404). Therefore, RFR algorithms can be used for accurate estimation of LAI for crops using satellite data.

scholarly journals Comparative Analysis of Machine Learning Algorithms in Automatic Identification and Extraction of Water Boundaries

2021 ◽  
Vol 11 (21) ◽  
pp. 10062
Author(s):  
Aimin Li ◽  
Meng Fan ◽  
Guangduo Qin ◽  
Youcheng Xu ◽  
Hailong Wang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Water Bodies ◽  
Support Vector ◽  
Landsat 8 ◽  
Transfer Performance ◽  
Remote Sensing Images

Monitoring open water bodies accurately is important for assessing the role of ecosystem services in the context of human survival and climate change. There are many methods available for water body extraction based on remote sensing images, such as the normalized difference water index (NDWI), modified NDWI (MNDWI), and machine learning algorithms. Based on Landsat-8 remote sensing images, this study focuses on the effects of six machine learning algorithms and three threshold methods used to extract water bodies, evaluates the transfer performance of models applied to remote sensing images in different periods, and compares the differences among these models. The results are as follows. (1) Various algorithms require different numbers of samples to reach their optimal consequence. The logistic regression algorithm requires a minimum of 110 samples. As the number of samples increases, the order of the optimal model is support vector machine, neural network, random forest, decision tree, and XGBoost. (2) The accuracy evaluation performance of each machine learning on the test set cannot represent the local area performance. (3) When these models are directly applied to remote sensing images in different periods, the AUC indicators of each machine learning algorithm for three regions all show a significant decline, with a decrease range of 0.33–66.52%, and the differences among the different algorithm performances in the three areas are obvious. Generally, the decision tree algorithm has good transfer performance among the machine learning algorithms with area under curve (AUC) indexes of 0.790, 0.518, and 0.697 in the three areas, respectively, and the average value is 0.668. The Otsu threshold algorithm is the optimal among threshold methods, with AUC indexes of 0.970, 0.617, and 0.908 in the three regions respectively and an average AUC of 0.832.

scholarly journals Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2769 ◽  
Author(s):  
Tri Dev Acharya ◽  
Anoj Subedi ◽  
Dong Ha Lee
Keyword(s):  
Machine Learning ◽  
Surface Water ◽  
Recursive Partitioning ◽  
Learning Algorithms ◽  
High Elevation ◽  
Machine Learning Algorithms ◽  
Water Extraction ◽  
Support Vector ◽  
Landsat 8 ◽  
Water Index

With over 6000 rivers and 5358 lakes, surface water is one of the most important resources in Nepal. However, the quantity and quality of Nepal’s rivers and lakes are decreasing due to human activities and climate change. Despite the advancement of remote sensing technology and the availability of open access data and tools, the monitoring and surface water extraction works has not been carried out in Nepal. Single or multiple water index methods have been applied in the extraction of surface water with satisfactory results. Extending our previous study, the authors evaluated six different machine learning algorithms: Naive Bayes (NB), recursive partitioning and regression trees (RPART), neural networks (NNET), support vector machines (SVM), random forest (RF), and gradient boosted machines (GBM) to extract surface water in Nepal. With three secondary bands, slope, NDVI and NDWI, the algorithms were evaluated for performance with the addition of extra information. As a result, all the applied machine learning algorithms, except NB and RPART, showed good performance. RF showed overall accuracy (OA) and kappa coefficient (Kappa) of 1 for the all the multiband data with the reference dataset, followed by GBM, NNET, and SVM in metrics. The performances were better in the hilly regions and flat lands, but not well in the Himalayas with ice, snow and shadows, and the addition of slope and NDWI showed improvement in the results. Adding single secondary bands is better than adding multiple in most algorithms except NNET. From current and previous studies, it is recommended to separate any study area with and without snow or low and high elevation, then apply machine learning algorithms in original Landsat data or with the addition of slopes or NDWI for better performance.

scholarly journals Research on the Application of Machine Learning Algorithms in Credit Risk Assessment of Minor Enterprises

CONVERTER ◽  
2021 ◽  
pp. 696-706
Author(s):  
Huichao Mi
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Industrial Development ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Small Scale ◽  
Scale Data

Under the influence of COVID-19, minor enterprises, especially the manufacturing industry, are facing greater financial pressure and the possibility of non-performing loans is increasing. It is very important for financial institutions to reduce financial risks while providing financial support for minor enterprises to promote industrial development and economic recovery. In order to understand the function of machine learning algorithms in predicting enterprise credit risk, the research designs five models, including Logistic Regression, Decision Tree, Naïve Bayesian, Support Vector Machine and Deep Neural Network, and adopts SMOTE and Undersampling to process imbalanced data. Experiments show that machine learning algorithms have high accuracy for both large-scale data and small-scale data.

scholarly journals Satellite imagery and machine learning for identification of aridity risk in central Java Indonesia

2021 ◽  
Vol 7 ◽  
pp. e415
Author(s):  
Sri Yulianto Joko Prasetyo ◽  
Kristoko Dwi Hartomo ◽  
Mila Chrismawati Paseleng
Keyword(s):  
Machine Learning ◽  
Survey Data ◽  
Vegetation Indices ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Landsat 8 ◽  
Area Index ◽  
Stable Algorithm ◽  
Vegetation Condition ◽  
Vegetation Health

This study aims to develop a software framework for predicting aridity using vegetation indices (VI) from LANDSAT 8 OLI images. VI data are predicted using machine learning (ml): Random Forest (RF) and Correlation and Regression Trees (CART). Comparison of prediction using Artificial Neural Network (ANN), Support Vector Machine (SVM), k-nearest neighbors (k-nn) and Multivariate Adaptive Regression Spline (MARS). Prediction results are interpolated using Inverse Distance Weight (IDW). This study was conducted in stages: (1) Image preprocessing; (2) calculating numerical data extracted from the LANDSAT band imagery using vegetation indices; (3) analyzing correlation coefficients between VI; (4) prediction using RF and CART; (5) comparing performances between RF and CART using ANN, SVM, k-nn, and MARS; (6) testing the accuracy of prediction using Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE); (7) interpolating with IDW. Correlation coefficient of VI data shows a positive correlation, the lowest r (0.07) and the highest r (0.98). The experiments show that the RF and CART algorithms have efficiency and effectivity in determining the aridity areas better than the ANN, SVM, k-nn, and MARS algorithm. RF has a difference between the predicted results and 1.04% survey data MAPE and the smallest value close to zero is 0.05 MSE. CART has a difference between the predicted results and 1.05% survey data MAPE and the smallest value approaching to zero which is 0.05 MSE. The prediction results of VI show that in 2020 most of the study areas were low vegetation areas with the Normalized Difference Vegetation Index (NDVI) < 0.21, had an indication of drought with the Vegetation Health Index (VHI) < 31.10, had a Vegetation Condition Index (VCI) in some areas between 35%–50% (moderate drought) and < 35% (high drought). The Burn Area Index (dBAI) values are between −3, 971 and −2,376 that show the areas have a low fire risk, and index values are between −0, 208 and −0,412 that show the areas are starting vegetation growth. The result of this study shows that the machine learning algorithms is an accurate and stable algorithm in predicting the risks of drought and land fire based on the VI data extracted from the LANDSAT 8 OLL imagery. The VI data contain the record of vegetation condition and its environment, including humidity, temperatures, and the environmental vegetation health.

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

scholarly journals PIXEL BASED LANDSLIDE IDENTIFICATION USING LANDSAT 8 AND GEE

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 721-726
Author(s):  
P. Singh ◽  
V. Maurya ◽  
R. Dwivedi
Keyword(s):  
Machine Learning ◽  
Research Work ◽  
Volcanic Eruptions ◽  
Google Earth ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Geological Survey ◽  
Support Vector ◽  
Landsat 8 ◽  
Multi Band

Abstract. Landslide is one of the most common natural disasters triggered mainly due to heavy rainfall, cloud burst, earthquake, volcanic eruptions, unorganized constructions of roads, and deforestation. In India, field surveying is the most common method used to identify potential landslide regions and update the landslide inventories maintained by the Geological Survey of India, but it is very time-consuming, costly, and inefficient. Alternatively, advanced remote sensing technologies in landslide analysis allow rapid and easy data acquisitions and help to improve the traditional method of landslide detection capabilities. Supervised Machine learning algorithms, for example, Support Vector Machine (SVM), are challenging to conventional techniques by predicting disasters with astounding accuracy. In this research work, we have utilized open-source datasets (Landsat 8 multi-band images and JAXA ALOS DSM) and Google Earth Engine (GEE) to identify landslides in Rudraprayag using machine learning techniques. Rudraprayag is a district of Uttarakhand state in India, which has always been the center of attention of geological studies due to its higher density of landslide-prone zones. For the training and validation purpose, labeled landslide locations obtained from landslide inventory (prepared by the Geological Survey of India) and layers such as NDVI, NDWI, and slope (generated from JAXA ALOS DSM and Landsat 8 satellite multi-band imagery) were used. The landslide identification has been performed using SVM, Classification and Regression Trees (CART), Minimum Distance, Random forest (RF), and Naïve Bayes techniques, in which SVM and RF outperformed all other techniques by achieving an 87.5% true positive rate (TPR).

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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