Evaluation of CCTV Data For Estimating Rainfall Condition

Abstract Rainfall characteristics of Indonesia's tropical climate have high variability according to space and time, so to determine the rainfall pattern of a location, an in situ rainfall measuring instrument (AWS = automatic weather station) is needed with high density. The existence of AWS also requires relatively high maintenance costs and a standard placement location (according to the rules of WMO = World Meteorological Organization) which is relatively broad and is not obstructed by other objects that can make the result of rainfall data is not representative. With the concept of computer vision, research will be carried out to estimate the rainfall condition from the CCTV cameras. The CCTV camera data which have qualitative characteristic into rainfall data which have quantitative characteristics. This research is also motivated by the large number of CCTVs that are placed in a lot of locations by local governments along with the Smart City program in districts and cities throughout Indonesia. The preliminary research was conducted in Center for Atmospheric Science and Technology office in Bandung. Rainfall data from AWS was used to validate CCTV data which placed in same location. The process of converting CCTV data into rainfall data goes through 6 stages. The first is reading the image mapping data and AWS (in rainfall accumulation data form). Second, read the image data in grayscale. Third, extract the features. Fourth, split the reference and sample data. Fifth, conducts the K-NN Mapping Reference Image and rainfall accumulation data. Sixth is to praise K-NN Testing. The accuracy is calculate with comparing the estimated number of CCTV cameras that are correct with the total sample size. The evaluation result states that the highest accuracy is obtained with K = 1. When K=1, the accuracy percentage reaching 94.8%. Accuracy decreases with increasing value of K and drastically decreases with K> 2. In the 1-10 days reference data, the highest accuracy is obtained by the number of reference data for 10 days, which is around 97%, stable until the value of K = 8. While the lowest accuracy is obtained when the reference data is 1 day with an accuracy value of about 43%. Based on the results of this study, it can be concluded that rain data from CCTV can be used to estimate the rainfall data. The best result happened when K-value is equal to 1.

Download Full-text

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

Remote Sensing ◽

10.3390/rs13071238 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1238

Author(s):

Jere Kaivosoja ◽

Juho Hautsalo ◽

Jaakko Heikkinen ◽

Lea Hiltunen ◽

Pentti Ruuttunen ◽

...

Keyword(s):

Reference Data ◽

Measurement Techniques ◽

Synthetic Data ◽

Ground Truth ◽

Precision Farming ◽

Aerial Imaging ◽

Aerial Vehicle ◽

Uav Images ◽

Reference Measurements

The development of UAV (unmanned aerial vehicle) imaging technologies for precision farming applications is rapid, and new studies are published frequently. In cases where measurements are based on aerial imaging, there is the need to have ground truth or reference data in order to develop reliable applications. However, in several precision farming use cases such as pests, weeds, and diseases detection, the reference data can be subjective or relatively difficult to capture. Furthermore, the collection of reference data is usually laborious and time consuming. It also appears that it is difficult to develop generalisable solutions for these areas. This review studies previous research related to pests, weeds, and diseases detection and mapping using UAV imaging in the precision farming context, underpinning the applied reference measurement techniques. The majority of the reviewed studies utilised subjective visual observations of UAV images, and only a few applied in situ measurements. The conclusion of the review is that there is a lack of quantitative and repeatable reference data measurement solutions in the areas of mapping pests, weeds, and diseases. In addition, the results that the studies present should be reflected in the applied references. An option in the future approach could be the use of synthetic data as reference.

Download Full-text

Brief communication "Determination of urban growth in catchment areas in Cyprus using multi-temporal remotely sensed data: risk assessment study"

Natural Hazards and Earth System Science ◽

10.5194/nhess-10-2235-2010 ◽

2010 ◽

Vol 10 (11) ◽

pp. 2235-2240 ◽

Cited By ~ 15

Author(s):

D. G. Hadjimitsis

Keyword(s):

Risk Assessment ◽

Catchment Area ◽

Image Data ◽

Remotely Sensed ◽

Remotely Sensed Data ◽

Aerial Photos ◽

Area Of Interest ◽

Catchment Areas ◽

Multi Temporal

Abstract. The aim of this study is to quantify the actual urbanization activity near the catchment area in the urban area of interest located in the vicinity of the Agriokalamin River area of Kissonerga Village in Paphos District. Remotely sensed data such as aerial photos, Landsat-5/7 TM/ETM+ and Quickbird image data have been used to track the urbanization activity from 1963 to 2008. In-situ GPS measurements have been used to locate in-situ the boundaries of the catchment area. The results clearly illustrate that tremendous urban development has taken place ranging from 0.9 to 33% from 1963 to 2008, respectively. A flood risk assessment and hydraulic analysis were also performed.

Download Full-text

Pengaruh Faktor Keuangan Dan Karakteristik Pemerintah Daerah Terhadap Financial Distress

JURNAL EKSPLORASI AKUNTANSI ◽

10.24036/jea.v2i3.276 ◽

2020 ◽

Vol 2 (3) ◽

pp. 3160-3178

Author(s):

Yoli Wulandari ◽

Fefri Indra Arza

Keyword(s):

Financial Distress ◽

Local Governments ◽

Binary Logistic Regression ◽

Sampling Technique ◽

Total Sample ◽

Binary Logistic Regression Analysis ◽

Financial Independence ◽

Growth Ratio ◽

Audit Opinion ◽

West Sumatra

This study aims to determine the effect of Financial Factors (Effectiveness Ratios, Efficiency Ratios, And Growth Ratios) and Local Government Characteristics (Financial Independence Of Local Governments, Population, Area, And Audit Opinion) on the Financial Distress on the Districts/ Cities in West Sumatra Province in 2016-2018. The data in this study use secondary from BPK and BPS. The sampling technique uses a total sampling method with a total sample of 19 districts / cities wtih a period of time of 4 years. Analysis of the data using binary logistic regression analysis. The results of this study indicate that (1) ratio of effectiveness has a negative and not significant effect on financial distress, (2) Efficiency ratio has a positive and not significant effect on financial distress, (3) growth ratio has a positive and not significant effect on financial distress, (4) The financial independence of local governments has a negative and not significant effect on financial distress, (5) population has a negative and significant effect on financial distress, (6) Area has a positive and significant effect on financial distress, (7) Audit opinion has a negative and not significant effect on financial distress.

Download Full-text

Modified ferron assay for speciation characterization of hydrolyzed Al(III): a precise k value based judgment

Water Science & Technology ◽

10.2166/wst.2009.066 ◽

2009 ◽

Vol 59 (4) ◽

pp. 823-832 ◽

Cited By ~ 3

Author(s):

Ye Changqing ◽

Wang Dongsheng ◽

Wu Xiaohong ◽

Qu Jiuhui ◽

John Gregory

Keyword(s):

Kinetic Constant ◽

Nonlinear Least Squares ◽

Precise Determination ◽

K Value ◽

First Order ◽

First Order Kinetics ◽

Parallel Reactions

The speciation of Al-OH complexes in terms of Ala, Alb and Alc could be achieved by traditional ferron assay and Alb is generally considered as Al13, however, the inherent correlation between them remains an enigma. This paper presents a modified ferron assay to get precise determination of Al13 using nonlinear least squares analysis, and to clarify the correlation between Alb and Al13. Two parallel reactions conforming to pseudo-first-order kinetics can simulate the complicate reactions between polynuclear complexes and ferron successfully. Four types of experimental kinetic constant (k value) of Al-OH complexes can be observed by this method when investigating three typical aluminium solutions. Comparing with the results of 27Al NMR, the species with moderate kinetics around 0.001 s−1 can be confirmed to resemble to Al13 polycation. The other types of kinetics are also well-regulated in partially neutralized aluminium solutions with various OH/Al ratios (b values) in the range 0 ∼ 2.5. It would provide potential means to trace the in-situ formation of Al13 in dilute solutions such as coagulation with Al-based coagulants

Download Full-text

Towards the effective use of Artificial Neural Networks for accessing rainfall thresholds for rainfall-induced landslides, a study based on in-situ and satellite merged rainfall data

10.5194/egusphere-egu21-1623 ◽

2021 ◽

Author(s):

Luísa Vieira Lucchese ◽

Guilherme Garcia de Oliveira ◽

Olavo Correa Pedrollo

Keyword(s):

Neural Network ◽

Rainfall Event ◽

Rainfall Data ◽

Extreme Rainfall Event ◽

Ann Model ◽

Antecedent Rainfall ◽

Rainfall Thresholds ◽

Satellite Rainfall ◽

Artificial Neural

Rainfall-induced landslides have caused destruction and deaths in South America. Accessing its triggers can help researchers and policymakers to understand the nature of the events and to develop more effective warning systems. In this research, triggering rainfall for rainfall-induced landslides is evaluated. The soil moisture effect is indirectly represented by the antecedent rainfall, which is an input of the ANN model. The area of the Rolante river basin, in Rio Grande do Sul state, Brazil, is chosen for our analysis. On January 5th, 2017, an extreme rainfall event caused a series of landslides and debris flows in this basin. The landslide scars were mapped using satellite imagery. To calculate the rainfall that triggered the landslides, it was necessary to compute the antecedent rainfall that occurred within the given area. The use of satellite rainfall data is a useful tool, even more so if no gauges are available for the location and time of the rainfall event, which is the case. Remote sensing products that merge the data from in situ stations with satellite rainfall data are increasingly popular. For this research, we employ the data from MERGE (Rozante et al., 2010), that is one of these products, and is focused specifically on Brazilian gauges and territory. For each 12.5x12.5m raster pixel, the rainfall is interpolated to the points and the rainfall volume from the last 24h before the event is accumulated. This is added as training data in our Artificial Neural Network (ANN), along with 11 terrain attributes based on ALOS PALSAR (ASF DAAC, 2015) elevation data and generated by using SAGA GIS. These attributes were presented and analyzed in Lucchese et al. (2020). Sampling follows the procedure suggested in Lucchese et al. (2021, in press). The ANN model is a feedforward neural network with one hidden layer consisting of 20 neurons. The ANN is trained by backpropagation method and cross-validation is used to ensure the correct adjustment of the weights. Metrics are calculated on a separate sample, called verification sample, to avoid bias. After training, and provided with relevant information, the ANN model can estimate the 24h-rainfall thresholds in the region, based on the 2017 event only. The result is a discretized map of rainfall thresholds defined by the execution of the trained ANN. Each pixel of the resulting map should represent the volume of rainfall in 24h necessary to trigger a landslide in that point. As expected, lower thresholds (30 - 60 mm) are located in scarped slopes and the regions where the landslides occurred. However, lowlands and the plateau, which are areas known not to be prone to landslides, show higher rainfall thresholds, although not as high as expected (75 - 95 mm). Mean absolute error for this model is 16.18 mm. The inclusion of more variables and events to the ANN training should favor achieving more reliable outcomes, although, our results are able to show that this methodology has potential to be used for landslide monitoring and prediction.

Download Full-text

Evaluation of the AMSR2 L2 soil moisture product of JAXA on the Mongolian Plateau over seven years (2012–2018)

SN Applied Sciences ◽

10.1007/s42452-019-1488-y ◽

2019 ◽

Vol 1 (11) ◽

Cited By ~ 1

Author(s):

Ichirow Kaihotsu ◽

Jun Asanuma ◽

Kentaro Aida ◽

Dambaravjaa Oyunbaatar

Keyword(s):

Soil Moisture ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Surface Soil ◽

Surface Soil Moisture ◽

Mongolian Plateau ◽

Moisture Measurement ◽

Rainfall Condition ◽

Soil Moisture Measurement

Abstract This study evaluated the Advanced Microwave Scanning Radiometer 2 (AMSR2) L2 soil moisture product (ver. 3) using in situ hydrological observational data, acquired over 7 years (2012–2018), from a 50 × 50 km flat area of the Mongolian Plateau covered with bare soil, pasture and shrubs. Although AMSR2 slightly underestimated soil moisture content at 3-cm depth, satisfactory timing was observed in both the response patterns and the in situ soil moisture data, and the differences between these factors were not large. In terms of the relationship between AMSR2 soil moisture from descending orbits and in situ measured soil moisture at 3-cm depth, the values of the RMSE (m3/m3) and the bias (m3/m3) varied from 0.028 to 0.063 and from 0.011 to − 0.001 m3/m3, respectively. The values of the RMSE and bias depended on rainfall condition. The mean value of the RMSE for the 7-year period was 0.042 m3/m3, i.e., lower than the target accuracy 0.050 m3/m3. The validation results for descending orbits were found slightly better than for ascending orbits. Comparison of the Soil Moisture and Ocean Salinity (SMOS) soil moisture product with the AMSR2 L2 soil moisture product showed that AMSR2 could observe surface soil moisture with nearly same accuracy and stability. However, the bias of the AMSR2 soil moisture measurement was slightly negative and poorer than that of SMOS with deeper soil moisture measurement. It means that AMSR2 cannot effectively measure soil moisture at 3-cm depth. In situ soil temperature at 3-cm depth and surface vegetation (normalized difference vegetation index) did not influence the underestimation of AMSR2 soil moisture measurements. These results suggest that a possible cause of the underestimation of AMSR2 soil moisture measurements is the difference between the depth of the AMSR2 observations and in situ soil moisture measurements. Overall, this study proved the AMSR2 L2 soil moisture product has been useful for monitoring daily surface soil moisture over large grassland areas and it clearly demonstrated the high-performance capability of AMSR2 since 2012.

Download Full-text

Automated and Manual Quantification of Tumour Cellularity in Digital Slides for Tumour Burden Assessment

Scientific Reports ◽

10.1038/s41598-019-50568-4 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 5

Author(s):

Shazia Akbar ◽

Mohammad Peikari ◽

Sherine Salama ◽

Azadeh Yazdan Panah ◽

Sharon Nofech-Mozes ◽

...

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Traditional Approach ◽

Quantitative Measure ◽

Image Data ◽

Qualitative Assessment ◽

Residual Cancer Burden ◽

Automated Algorithms ◽

Tumour Cellularity

Abstract The residual cancer burden index is an important quantitative measure used for assessing treatment response following neoadjuvant therapy for breast cancer. It has shown to be predictive of overall survival and is composed of two key metrics: qualitative assessment of lymph nodes and the percentage of invasive or in situ tumour cellularity (TC) in the tumour bed (TB). Currently, TC is assessed through eye-balling of routine histopathology slides estimating the proportion of tumour cells within the TB. With the advances in production of digitized slides and increasing availability of slide scanners in pathology laboratories, there is potential to measure TC using automated algorithms with greater precision and accuracy. We describe two methods for automated TC scoring: 1) a traditional approach to image analysis development whereby we mimic the pathologists’ workflow, and 2) a recent development in artificial intelligence in which features are learned automatically in deep neural networks using image data alone. We show strong agreements between automated and manual analysis of digital slides. Agreements between our trained deep neural networks and experts in this study (0.82) approach the inter-rater agreements between pathologists (0.89). We also reveal properties that are captured when we apply deep neural network to whole slide images, and discuss the potential of using such visualisations to improve upon TC assessment in the future.

Download Full-text

Study on multiple rainfall data applied to debris flow warning in Taiwan

10.5194/egusphere-egu2020-8022 ◽

2020 ◽

Author(s):

Yi-Chao Zeng ◽

Chyan-Deng Jan ◽

Mu-Jung Lin ◽

Ji-Shang Wang ◽

Hsiao-Yuan Yin ◽

...

Keyword(s):

Debris Flow ◽

Observational Data ◽

Water Conservation ◽

Spatial Interpolation ◽

Rain Gauge ◽

Rainfall Data ◽

Mountain Area ◽

Rainfall Condition ◽

Capture Ratio ◽

Spatial Coverage

Due to climate change, precipitation characteristics have been significantly variation and rainfall patterns are presented more concentrated, high-intensity and long-duration trend in the past two decades. Catastrophic debris-flow disaster threaten lives and property of residents. For mitigation impact of debris-flow, SWCB (Soil and Water Conservation Bureau, Taiwan) has had a leading role in sponsoring debris-flow research and developing a rainfall-based debris-flow warning model. Early warning criteria for debris-flow triggered are also determined depending on the historical rainfall data, and the observational data of rain-gauge are adopted to issue debris-flow warning. However, application of rain-gauge rainfall data has some disadvantages such as low density in mountain area, observation failure to properly represent actual rainfall condition, and data transmission likely interrupted during heavy rainfall or Typhoon. In order to improve the efficiency of debris-flow warning system, two types of gridded precipitation are analyzed and discussed in this study, which are the spatial interpolation rainfall of rain-gauge and the radar-estimated rainfall (QPESUMS). For comparison the differents of multiple rainfall data mentioned above with rain-guage, the third quartile is firstly applied to calculate the regional representative rainfall from grid cells within warning issued area. The results show that the spatial interpolation rainfall underestimates the rainfall intensity and cumulative rainfall owing to the influence of complex topography. By contrast, the radar-estimated rainfall has the advantage in comprehension of the rainfall spatial variability and provide a more complete spatial coverage. Besides, for assessing the appropriate and feasibility of multiple rainfall data applied to debris flow warning, the disaster&#8211;capture ratio has been proposed which is defined as the number of debris-flow hazards after issuing warning divided by total number of debris- flow hazards. According to analyis results of historical disaster records from 2012 to 2016, the disaster&#8211;capture ratio are 47.6%, 38.1% and 61.9% as warning issued refer to rain gauge, the spatial interpolation rainfall and the radar-estimated rainfall respectively. By the aforementioned process, we realize that the application of radar-estimated rainfall to debris flow warning is obviously increasing efficiency of debris-flow warning ,and gives assistance for reducing uncertainty of rainfall observational data, especially in mountain area.

Download Full-text

Online Information as Real-Time Big Data About Heavy Rain Disasters and its Limitations: Case Study of Miyagi Prefecture, Japan, During Typhoons 17 and 18 in 2015

Journal of Disaster Research ◽

10.20965/jdr.2017.p0335 ◽

2017 ◽

Vol 12 (2) ◽

pp. 335-346 ◽

Cited By ~ 2

Author(s):

Shosuke Sato ◽

◽

Shuichi Kure ◽

Shuji Moriguchi ◽

Keiko Udo ◽

...

Keyword(s):

Social Media ◽

Real Time ◽

Local Governments ◽

Expert Knowledge ◽

Water Levels ◽

Rainfall Data ◽

Online Information ◽

Radar Rainfall ◽

Cumulative Rainfall ◽

Miyagi Prefecture

The role of public online information in helping to reduce disaster damage is expected to become increasingly important since it can be used for decision making about disaster response. This paper aims to discuss the effectiveness and limitations of real-time online information about heavy rainfall based on an analysis of data on the disaster caused by Typhoons 17 and 18 in 2015 in Miyagi prefecture, Japan, and on a focus group interview survey with four experts on natural disasters. The results from the interviews showed the following: (1) Landslide alert information is reliable for prediction purposes. However, many people did not monitor it because it was released around midnight. (2) Areas of landslide occurrence and river flooding correspond to areas with heavy cumulative rainfall. Yet cumulative rainfall data are not available on the web. (3) The available radar-rainfall data can be used to predict the situation one hour from the present as long as the person has expert knowledge. (4) It is possible to monitor river water levels at many points. Yet, about half of the observation points have no established “flood danger water level.” (5) Local governments released a great amount of disaster information through social media before flooding occurred on some rivers. However, one must monitor multiple social media accounts and not just the account of one’s hometown.

Download Full-text

Pancreas cancer: Influence of gender and other demographic and clinical parameters on survival.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.15_suppl.e12529 ◽

2013 ◽

Vol 31 (15_suppl) ◽

pp. e12529-e12529 ◽

Cited By ~ 1

Author(s):

Patricia Renee Blank ◽

Thomas D. Szucs

Keyword(s):

Pancreatic Cancer ◽

Gender Difference ◽

Pancreas Cancer ◽

Cox Regression ◽

Total Sample ◽

Cancer Prognosis ◽

Kaplan Meier ◽

Significant Difference ◽

Cox Proportional Hazard Models

e12529 Background: Research suggests that lower excess mortality risk for females compared to men do exist for several cancer types. The primary aim of this study was to investigate whether gender affects pancreatic cancer prognosis. In addition, the relationship of sex and survival adjusted for clinical and demographic factors was assessed. Methods: The Surveillance, Epidemiology, and End Results (SEER) database (version 1973-2009) was used to identify patients with primary histological confirmed pancreas cancer (≥18 years, 80,689 males and 82,356 females). The analysis was stratified by five different stages (in situ, locally invasive, regional, distant, unstaged). The crude effect of gender was assessed in the total sample and in age-stratified Kaplan-Meier Curves (<55 years) in all five stages, respectively. Univariate and multivariate Cox proportional hazard models were run within the local stage. The predictors included in the models were demographic and clinical factors. P-values (2-sided) of 0.05 were assumed as statistical significant. Results: Between 1973 and 2009, 128,645 pancreas cancer-related deaths were reported. The median follow-up time of the censored patients was 13 months. The Kaplan-Meier curves showed a significant difference in survival among men and female in the locally invasive group (median survival in male and female: 7 months; 1-year survival: 35.5% and 35.1% among female and men, respectively; Log-Rank: p=0.0072). Of the remaining strata, all others had non-significant differences, except the unstaged group (Log-Rank: p= <.0001). The univariate Cox-regression indicated a 5.6% (95%CI: 1.4%, 9.9%, p=0.0081) higher rate of dying among men compared to female (local recurrence). Among the younger population (<55 years), the gender difference was significant across all disease stages, except the in situ group. Histology grade, age, race, and marital status was associated with survival from pancreatic cancer in the multivariate analysis. Conclusions: The present study indicates a gender difference in survival among pancreas cancer patients. The study findings are, however, preliminary and hypothesis generating and a matter for further investigation to give a distinct conclusion.

Download Full-text