Can Multispectral Information Improve Remotely Sensed Estimates of Total Suspended Solids? A Statistical Study in Chesapeake Bay

Total suspended solids (TSS) is an important environmental parameter to monitor in the Chesapeake Bay due to its effects on submerged aquatic vegetation, pathogen abundance, and habitat damage for other aquatic life. Chesapeake Bay is home to an extensive and continuous network of in situ water quality monitoring stations that include TSS measurements. Satellite remote sensing can address the limited spatial and temporal extent of in situ sampling and has proven to be a valuable tool for monitoring water quality in estuarine systems. Most algorithms that derive TSS concentration in estuarine environments from satellite ocean color sensors utilize only the red and near-infrared bands due to the observed correlation with TSS concentration. In this study, we investigate whether utilizing additional wavelengths from the Moderate Resolution Imaging Spectroradiometer (MODIS) as inputs to various statistical and machine learning models can improve satellite-derived TSS estimates in the Chesapeake Bay. After optimizing the best performing multispectral model, a Random Forest regression, we compare its results to those from a widely used single-band algorithm for the Chesapeake Bay. We find that the Random Forest model modestly outperforms the single-band algorithm on a holdout cross-validation dataset and offers particular advantages under high TSS conditions. We also find that both methods are similarly generalizable throughout various partitions of space and time. The multispectral Random Forest model is, however, more data intensive than the single band algorithm, so the objectives of the application will ultimately determine which method is more appropriate.

Download Full-text

Developing an Advanced PM2.5 Exposure Model in Lima, Peru

Remote Sensing ◽

10.3390/rs11060641 ◽

2019 ◽

Vol 11 (6) ◽

pp. 641 ◽

Cited By ~ 9

Author(s):

Bryan Vu ◽

Odón Sánchez ◽

Jianzhao Bi ◽

Qingyang Xiao ◽

Nadia Hansel ◽

...

Keyword(s):

Random Forest ◽

Spatial Resolution ◽

Cross Validation ◽

Fine Particulate Matter ◽

Epidemiological Studies ◽

Random Forest Model ◽

Exposure Model ◽

Validation Dataset ◽

Good Precision ◽

Forest Model

It is well recognized that exposure to fine particulate matter (PM2.5) affects health adversely, yet few studies from South America have documented such associations due to the sparsity of PM2.5 measurements. Lima’s topography and aging vehicular fleet results in severe air pollution with limited amounts of monitors to effectively quantify PM2.5 levels for epidemiologic studies. We developed an advanced machine learning model to estimate daily PM2.5 concentrations at a 1 km2 spatial resolution in Lima, Peru from 2010 to 2016. We combined aerosol optical depth (AOD), meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF), parameters from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and land use variables to fit a random forest model against ground measurements from 16 monitoring stations. Overall cross-validation R2 (and root mean square prediction error, RMSE) for the random forest model was 0.70 (5.97 μg/m3). Mean PM2.5 for ground measurements was 24.7 μg/m3 while mean estimated PM2.5 was 24.9 μg/m3 in the cross-validation dataset. The mean difference between ground and predicted measurements was −0.09 μg/m3 (Std.Dev. = 5.97 μg/m3), with 94.5% of observations falling within 2 standard deviations of the difference indicating good agreement between ground measurements and predicted estimates. Surface downwards solar radiation, temperature, relative humidity, and AOD were the most important predictors, while percent urbanization, albedo, and cloud fraction were the least important predictors. Comparison of monthly mean measurements between ground and predicted PM2.5 shows good precision and accuracy from our model. Furthermore, mean annual maps of PM2.5 show consistent lower concentrations in the coast and higher concentrations in the mountains, resulting from prevailing coastal winds blown from the Pacific Ocean in the west. Our model allows for construction of long-term historical daily PM2.5 measurements at 1 km2 spatial resolution to support future epidemiological studies.

Download Full-text

Kualitas perairan Natuna pada musim transisi

DEPIK ◽

10.13170/depik.3.1.1277 ◽

2014 ◽

Vol 3 (1) ◽

Cited By ~ 1

Author(s):

Mariska Astrid Kusumaningtyas ◽

Rikha Bramawanto ◽

August Daulat ◽

Widodo S. Pranowo

Keyword(s):

Water Quality ◽

Suspended Solids ◽

Total Suspended Solids ◽

Secchi Disk

Abstrak. Perairan Natuna, khususnya pada Kawasan Konservasi Laut Daerah (KKLD) I diprioritaskan untuk mendukung kegiatan perikanan berkelanjutan, sehingga penting diketahui kualitasperairannya. Penelitian ini bertujuan untuk mengetahui karakteristik kualitas air berdasarkan parameter fisika maupun kimia sebagai basis data terkini mengenai kualitas perairan Natuna pada musim transisi. Penelitian dilakukan di 31 stasiun pada bulan November 2012. Parameter kualitas air yang diukur antara lain kecerahan, derajat keasaman (pH), oksigen terlarut, suhu, salinitas, padatan tersuspensi total atau Total Suspended Solids (TSS) dan nutrien (nitrat, fosfat, silikat). Parameter pH, oksigen terlarut, suhu, dan salinitas diukur secara in-situ menggunaan alat water quality meter (TOA-DKK), kecerahan diukur menggunakan secchi disk, sedangkan sampel air di bawa ke laboratorium untuk dianalisis konsentrasi nutrien dan TSS. Hasil penelitian menunjukkan nilai kisaran kecerahan yaitu 2-20,9 (m), pH 8,09-8,27, oksigen terlarut 6,34-7,96 (mg/l), suhu 29,2-30,6 (°C), salinitas 27,9-30,4 (PSU), TSS <3-26 (mg/l), nitrat 0,005-0,078 (mg/l), fosfat <0,005-0,015 (mg/l) dan silikat 0,045-0,704 (mg/l). Hasil penelitian dibandingkan dengan baku mutu air laut untuk biota laut berdasarkan Keputusan Menteri Lingkungan Hidup Nomor 51 Tahun 2004. Berdasarkan hasil penelitian, kondisi perairan Natuna masih tergolong baik untuk menunjang kehidupan biota laut.Kata kunci: Parameter kimia; Parameter fisika; Natuna; musim transisi

Download Full-text

Study on Water quality of Narmada River by analyzing physicochemical and biological parameters using random forest model

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i1.826831 ◽

2019 ◽

Vol 7 (1) ◽

pp. 826-831

Author(s):

Sanjeev Gour ◽

Mamta Gour

Keyword(s):

Water Quality ◽

Random Forest ◽

Random Forest Model ◽

Biological Parameters ◽

Forest Model ◽

Narmada River

Download Full-text

Seagrass depth range and water quality in southern Moreton Bay, Queensland, Australia

Marine and Freshwater Research ◽

10.1071/mf9960763 ◽

1996 ◽

Vol 47 (6) ◽

pp. 763 ◽

Cited By ~ 95

Author(s):

EG Abal ◽

WC Dennison

Keyword(s):

Water Quality ◽

Chlorophyll A ◽

Suspended Solids ◽

Light Attenuation ◽

River Mouth ◽

Total Suspended Solids ◽

Quality Parameters ◽

Depth Range ◽

Moreton Bay ◽

A Site

Correlations between water quality parameters and seagrass depth penetration were developed for use as a biological indicator of integrated light availability and long-term trends in water quality. A year-long water quality monitoring programme in Moreton Bay was coupled with a series of seagrass depth transects. A strong gradient between the western (landward) and eastern (seaward) portions of Moreton Bay was observed in both water quality and seagrass depth range. Higher concentrations of chlorophyll a, total suspended solids, dissolved and total nutrients, and light attenuation coefficients in the water column and correspondingly shallower depth limits of the seagrass Zostera capricorni were observed in the western portions of the bay. Relatively high correlation coefficient values (r2 > 0.8) were observed between light attenuation coefficient, total suspended solids, chlorophyll a, total Kjeldahl nitrogen and Zostera capricorni depth range. Low correlation coefficient values (r2 < 0.8) between seagrass depth range and dissolved inorganic nutrients were observed. Seagrasses had disappeared over a five-year period near the mouth of the Logan River, a turbid river with increased land use in its watershed. At a site 9 km from the river mouth, a significant decrease in seagrass depth range corresponded to higher light attenuation, chlorophyll a, total suspended solids and total nitrogen content relative to a site 21 km from the river mouth. Seagrass depth penetration thus appears to be a sensitive bio-indicator of some water quality parameters, with application for water quality management.

Download Full-text

Spatial modeling of gully head erosion on the Loess Plateau using a certainty factor and random forest model

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.147040 ◽

2021 ◽

Vol 783 ◽

pp. 147040

Author(s):

Chengcheng Jiang ◽

Wen Fan ◽

Ningyu Yu ◽

Enlong Liu

Keyword(s):

Random Forest ◽

Loess Plateau ◽

Spatial Modeling ◽

Random Forest Model ◽

Certainty Factor ◽

The Loess Plateau ◽

Forest Model ◽

Gully Head

Download Full-text

Clinical trial registries as Scientometric data: A novel solution for linking and deduplicating clinical trials from multiple registries

Scientometrics ◽

10.1007/s11192-021-04111-w ◽

2021 ◽

Author(s):

Christian Thiele ◽

Gerrit Hirschfeld ◽

Ruth von Brachel

Keyword(s):

Clinical Trials ◽

Random Forest ◽

Random Forest Model ◽

Scientometric Analysis ◽

Data Set ◽

The Public ◽

Forest Model ◽

Clinical Trial Registries ◽

Multiple Primary ◽

Clinical Trials Registry

AbstractRegistries of clinical trials are a potential source for scientometric analysis of medical research and serve important functions for the research community and the public at large. Clinical trials that recruit patients in Germany are usually registered in the German Clinical Trials Register (DRKS) or in international registries such as ClinicalTrials.gov. Furthermore, the International Clinical Trials Registry Platform (ICTRP) aggregates trials from multiple primary registries. We queried the DRKS, ClinicalTrials.gov, and the ICTRP for trials with a recruiting location in Germany. Trials that were registered in multiple registries were linked using the primary and secondary identifiers and a Random Forest model based on various similarity metrics. We identified 35,912 trials that were conducted in Germany. The majority of the trials was registered in multiple databases. 32,106 trials were linked using primary IDs, 26 were linked using a Random Forest model, and 10,537 internal duplicates on ICTRP were identified using the Random Forest model after finding pairs with matching primary or secondary IDs. In cross-validation, the Random Forest increased the F1-score from 96.4% to 97.1% compared to a linkage based solely on secondary IDs on a manually labelled data set. 28% of all trials were registered in the German DRKS. 54% of the trials on ClinicalTrials.gov, 43% of the trials on the DRKS and 56% of the trials on the ICTRP were pre-registered. The ratio of pre-registered studies and the ratio of studies that are registered in the DRKS increased over time.

Download Full-text

Discrimination of the geographic origins and varieties of wine grapes using high-throughput sequencing assisted by a random forest model

LWT ◽

10.1016/j.lwt.2021.111333 ◽

2021 ◽

pp. 111333

Author(s):

Feifei Gao ◽

Guihua Zeng ◽

Bin Wang ◽

Jing Xiao ◽

Liang Zhang ◽

...

Keyword(s):

Random Forest ◽

High Throughput ◽

High Throughput Sequencing ◽

Random Forest Model ◽

Wine Grapes ◽

Forest Model ◽

Geographic Origins

Download Full-text

Multi-Scenario Prediction of Intra-Urban Land Use Change Using a Cellular Automata-Random Forest Model

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10080503 ◽

2021 ◽

Vol 10 (8) ◽

pp. 503

Author(s):

Hang Liu ◽

Riken Homma ◽

Qiang Liu ◽

Congying Fang

Keyword(s):

Land Use ◽

Random Forest ◽

Cellular Automata ◽

Land Use Change ◽

Urban Land ◽

Urban Land Use ◽

Random Forest Model ◽

Growth Trend ◽

Related Factors ◽

Forest Model

The simulation of future land use can provide decision support for urban planners and decision makers, which is important for sustainable urban development. Using a cellular automata-random forest model, we considered two scenarios to predict intra-land use changes in Kumamoto City from 2018 to 2030: an unconstrained development scenario, and a planning-constrained development scenario that considers disaster-related factors. The random forest was used to calculate the transition probabilities and the importance of driving factors, and cellular automata were used for future land use prediction. The results show that disaster-related factors greatly influence land vacancy, while urban planning factors are more important for medium high-rise residential, commercial, and public facilities. Under the unconstrained development scenario, urban land use tends towards spatially disordered growth in the total amount of steady growth, with the largest increase in low-rise residential areas. Under the planning-constrained development scenario that considers disaster-related factors, the urban land area will continue to grow, albeit slowly and with a compact growth trend. This study provides planners with information on the relevant trends in different scenarios of land use change in Kumamoto City. Furthermore, it provides a reference for Kumamoto City’s future post-disaster recovery and reconstruction planning.

Download Full-text