Optimalisasi Saluran Komunikasi Berbasis Gelombang Mikro Sebagai Alternatif Sistem Pemantauan Curah Hujan

As a vast archipelagic country with diverse topographic conditions and has an annual average rainfall of more than 2000 mm, Indonesia is prone to hydrometeorological disasters. Based on Indonesia's disaster data, throughout 2021 there were 3,658 incidents of floods and landslides distributed throughout Indonesia. This makes real-time rainfall monitoring with high density indispensable. Indonesia currently has a rainfall monitoring system about 1000 automatic rain gauges, so an increase in the spatial resolution of network is necessary. The increasing density of monitoring equipment using rain gauges and weather radar poses the problem of high procurement and operational costs. Therefore, several alternative rainfall monitoring systems are needed. In this article, we review several studies that focus on the utilization of terrestrial and satellite communication link operating in high frequency bands as an alternative for measuring rainfall. Optimization of the satellite communication system network is more suitable than terrestrial networks to be applied in Indonesia with archipelagic areas because it has a large number of point distributions with wider coverage. The use of artificial intelligence with deep learning techniques such as one dimensional convolutional neural network (1D-CNN) is also very promising to estimate rainfall intensity because it has a high accuracy of 93%.

Rain event detection in commercial microwave link attenuation data using convolutional neural networks

Quantitative precipitation estimation with commercial microwave links (CMLs) is a technique developed to supplement weather radar and rain gauge observations. It is exploiting the relation between the attenuation of CML signal levels and the integrated rain rate along a CML path. The opportunistic nature of this method requires a sophisticated data processing using robust methods. In this study we focus on the processing step of rain event detection in the signal level time series of the CMLs, which we treat as a binary classification problem. This processing step is particularly challenging, because even when there is no rain, the signal level can show large fluctuations similar to that during rainy periods. False classifications can have a high impact on falsely estimated rainfall amounts. We analyze the performance of a convolutional neural network (CNN), which is trained to detect rainfall-specific attenuation patterns in CML signal levels, using data from 3904 CMLs in Germany. The CNN consists of a feature extraction and a classification part with, in total, 20 layers of neurons and 1.4×105 trainable parameters. With a structure inspired by the visual cortex of mammals, CNNs use local connections of neurons to recognize patterns independent of their location in the time series. We test the CNN's ability to recognize attenuation patterns from CMLs and time periods outside the training data. Our CNN is trained on 4 months of data from 800 randomly selected CMLs and validated on 2 different months of data, once for all CMLs and once for the 3104 CMLs not included in the training. No CMLs are excluded from the analysis. As a reference data set, we use the gauge-adjusted radar product RADOLAN-RW provided by the German meteorological service (DWD). The model predictions and the reference data are compared on an hourly basis. Model performance is compared to a state-of-the-art reference method, which uses the rolling standard deviation of the CML signal level time series as a detection criteria. Our results show that within the analyzed period of April to September 2018, the CNN generalizes well to the validation CMLs and time periods. A receiver operating characteristic (ROC) analysis shows that the CNN is outperforming the reference method, detecting on average 76 % of all rainy and 97 % of all nonrainy periods. From all periods with a reference rain rate larger than 0.6 mm h−1, more than 90 % was detected. We also show that the improved event detection leads to a significant reduction of falsely estimated rainfall by up to 51 %. At the same time, the quality of the correctly estimated rainfall is kept at the same level in regards to the Pearson correlation with the radar rainfall. In conclusion, we find that CNNs are a robust and promising tool to detect rainfall-induced attenuation patterns in CML signal levels from a large CML data set covering all of Germany

Rain event detection in commercial microwave link attenuation data using convolutional neural networks

Quantitative precipitation estimation with commercial microwave links (CMLs) is a technique developed to supplement weather radar and rain gauge observations. It is exploiting the relation between the attenuation of CML signal levels and the integrated rain rate along a CML path. The opportunistic nature of this method requires a sophisticated data processing using robust methods. In this study we focus on the processing step of rain event detection in the signal level time series of the CMLs, which we treat as a binary classification problem. This processing step is particularly challenging, because even when there is no rain, the signal level can show large fluctuations similar to that during rainy periods. False classifications can have a high impact on falsely estimated rainfall amounts. We analyze the performance of a convolutional neural network (CNN), which is trained to detect rainfall-specific attenuation patterns in CML signal levels, using data from 3904 CMLs in Germany. The CNN consists of a feature extraction and a classification part with, in total, 20 layers of neurons and 1.4×105 trainable parameters. With a structure inspired by the visual cortex of mammals, CNNs use local connections of neurons to recognize patterns independent of their location in the time series. We test the CNN\u27s ability to recognize attenuation patterns from CMLs and time periods outside the training data. Our CNN is trained on 4 months of data from 800 randomly selected CMLs and validated on 2 different months of data, once for all CMLs and once for the 3104 CMLs not included in the training. No CMLs are excluded from the analysis. As a reference data set, we use the gauge-adjusted radar product RADOLAN-RW provided by the German meteorological service (DWD). The model predictions and the reference data are compared on an hourly basis. Model performance is compared to a state-of-the-art reference method, which uses the rolling standard deviation of the CML signal level time series as a detection criteria. Our results show that within the analyzed period of April to September 2018, the CNN generalizes well to the validation CMLs and time periods. A receiver operating characteristic (ROC) analysis shows that the CNN is outperforming the reference method, detecting on average 76 % of all rainy and 97 % of all nonrainy periods. From all periods with a reference rain rate larger than 0.6 mm h−1, more than 90 % was detected. We also show that the improved event detection leads to a significant reduction of falsely estimated rainfall by up to 51 %. At the same time, the quality of the correctly estimated rainfall is kept at the same level in regards to the Pearson correlation with the radar rainfall. In conclusion, we find that CNNs are a robust and promising tool to detect rainfall-induced attenuation patterns in CML signal levels from a large CML data set covering all of Germany

Hyperlocal weather parameter sensing with mmWave signals

The evolution of mobile communication technologies to achieve higher throughputs has led to the use of higher frequency bands. 5G technologies are working on the mmWave spectrum, which are frequencies between 30 GHz and 300 GHz, and it is expected that 6G would use even higher frequencies. The wavelength of the signals in these bands are like those used in radars, giving the possibility to use the wave for other things be-sides transmitting information. Network sensing is one of the use cases that can be exploited from the mmWave. Signal loss under different weather conditions has been studied and modeled for over 20 years. Based on these models, this thesis develops a deep learning LSTM model that accurately detects precipitation from a mmWave backhaul link

Research on Rainfall Monitoring Based on E-Band Millimeter Wave Link in East China

Accurate rainfall observation data with high temporal and spatial resolution are essential for national disaster prevention and mitigation as well as climate response decisions. This paper introduces a field experiment using an E-band millimeter-wave link to obtain rainfall rate information in Nanjing city, which is situated in the east of China. The link is 3 km long and operates at 71 and 81 GHz. We first distinguish between the wet and the dry periods, and then determine the classification threshold for calculating attenuation baseline in real time. We correct the influence of the wet antenna attenuation and finally calculate the rainfall rate through the power law relationship between the rainfall rate and the rain-induced attenuation. The experimental results show that the correlation between the rainfall rate retrieved from the 71 GHz link and the rainfall rate measured by the raindrop spectrometer is up to 0.9. The correlation at 81 GHz is up to 0.91. The mean relative errors are all below 5%. By comparing with the rainfall rate measured by the laser raindrop spectrometer set up at the experimental site, we verified the reliability and accuracy of monitoring rainfall using the E-band millimeter-wave link.</jats:p

A systematic review of the use of Deep Learning in Satellite Imagery for Agriculture

Agricultural research is essential for increasing food production to meet the requirements of an increasing population in the coming decades. Recently, satellite technology has been improving rapidly and deep learning has seen much success in generic computer vision tasks and many application areas which presents an important opportunity to improve analysis of agricultural land. Here we present a systematic review of 150 studies to find the current uses of deep learning on satellite imagery for agricultural research. Although we identify 5 categories of agricultural monitoring tasks, the majority of the research interest is in crop segmentation and yield prediction. We found that, when used, modern deep learning methods consistently outperformed traditional machine learning across most tasks; the only exception was that Long Short-Term Memory (LSTM) Recurrent Neural Networks did not consistently outperform Random Forests (RF) for yield prediction. The reviewed studies have largely adopted methodologies from generic computer vision, except for one major omission: benchmark datasets are not utilised to evaluate models across studies, making it difficult to compare results. Additionally, some studies have specifically utilised the extra spectral resolution available in satellite imagery, but other divergent properties of satellite images - such as the hugely different scales of spatial patterns - are not being taken advantage of in the reviewed studies.Comment: 25 pages, 2 figures and lots of large tables. Supplementary materials section included here in main pd

Advanced Sensors for Real-Time Monitoring Applications

It is impossible to imagine the modern world without sensors, or without real-time information about almost everything—from local temperature to material composition and health parameters. We sense, measure, and process data and act accordingly all the time. In fact, real-time monitoring and information is key to a successful business, an assistant in life-saving decisions that healthcare professionals make, and a tool in research that could revolutionize the future. To ensure that sensors address the rapidly developing needs of various areas of our lives and activities, scientists, researchers, manufacturers, and end-users have established an efficient dialogue so that the newest technological achievements in all aspects of real-time sensing can be implemented for the benefit of the wider community. This book documents some of the results of such a dialogue and reports on advances in sensors and sensor systems for existing and emerging real-time monitoring applications

Proceedings Of The 18th Annual Meeting Of The Asia Oceania Geosciences Society (Aogs 2021)

The 18th Annual Meeting of the Asia Oceania Geosciences Society (AOGS 2021) was held from 1st to 6th August 2021. This proceedings volume includes selected extended abstracts from a challenging array of presentations at this conference. The AOGS Annual Meeting is a leading venue for professional interaction among researchers and practitioners, covering diverse disciplines of geosciences

Modeling Urban Areas Epidemiological Risk Exposure Using Multispectral Spaceborne Data

In recent decades, the world has been fast urbanizing. More than half of the world’s human population now live in urban areas. Such high density of urban population is resulting in air and water pollution, land degradation, and infectious diseases spread risks prominence. However, the increasing quality (in terms of finer spatial and temporal resolution)and quantity of Earth Observation (EO) satellite data provide new perspectives for analysing these phenomena. Within the specific domain of epidemiological risks dynamics in urban areas which is the focus of this work, the use of multispectral optical EO sensor data has created new opportunities. These data through their visible, near, mid, far and thermal infrared bands provide planetary-­‐scale access to environmental variables such as temperature, humidity, and vegetation types, location and conditions. Since these environmental variables affect the development of vectors causing infectious disease (e.g., mosquitoes), there is the possibility to use EO data to estimate them, and obtain disease risk models. The Ae. aegypti mosquito species transmits Zika, Dengue, and Chikungunya, diseases widespread in more than 100 world countries, and is concentrated in urban areas. The development of this vector depends significantly on local environmental temperature, humidity, precipitation and vegetation. In this regard, multispectral EO data can provide globally consistent and scalable sources to obtain the required environmental variable inputs, and extract significant and consistent monitoring and forecasting models for vector population. The work reported in this thesis about this topic has led to the following results: 1) A method to map vegetation types in urban areas at high spatial resolution using Sentinel2 multispectral EO data. The results show an improvement in the quality of the resulting vegetation maps with respect to what is available by means of state-­of-­the-­art techniques. 2) A method that combines EO-­based spectral indices, temperature layers, and precipitation measurement to model the temporal evolution of the local mean Ae. aegypti population. The approach leverages the random forest (RF) machine learning (ML) technique and its embedded nonlinear features importance ranking (mean decrease impurity, MDI) to rank the effects of environmental variables and explain the resulting model. 3) A weighted generalized linear modeling (GLM) technique to predict Ae. aegypti population using multispectral EO data covariate inputs. GLMs are generally simple to implement and explain, but do not provide the same level of prediction quality as ML methods. The proposed weighted GLM compares well with ML techniques in quality, and provides capability for more explicitly interpretation of the results. 4) A recurrent neural network (RNN) technique for spatio­‐temporal modeling of Ae. Aegypti population at the urban block level using multispectral EO data as inputs. This study is needed because spatial models obscure seasonality effects while temporal model are blind to spatial changes in micro-­climates. The proposed technique shows great promise with respect to the use of free multispectral EO data for spatio-­temporal epidemiological modeling. All the proposed techniques have been applied in the Latin American region where the risk of Ae. aegypti vector transmitted diseases are the highest in the world. They were validated thanks to the long term partnership with the University of Alagoas in Maceio (Brazil) and the Brazilian company: ECOVEC