Automated identification of river hydromorphological features using UAV high resolution aerial imagery

European legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology) along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs) to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN) have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management

Classification and Risk-Mapping of River Water Quality in Surabaya with Semantic Visualitzation

River water pollution is one of the environmental problems that occur in Surabaya. The amount of industrial waste and household waste makes Surabaya River water easily polluted every day, besides that there are also many people who are not aware about the quality of river water in Surabaya. In this paper, we present a new system to classify water quality of river in surabaya. The system involve a semantic visualization of risk-mapping for the river, so that the people of Surabaya are easier to get information about the quality of Surabaya River water. In this paper, we measured the water quality of Surabaya River using Horiba sensor measuring instruments using 5 parameters, namely temperature, PH, DO, Turbidity, TDS. These five parameters are input variables for calculating water quality with the methods applied in this research. We use the Storet Method to determine the quality of Surabaya River water. The results of the Storet Method explained that there were 0.03% of the data on lightly polluted water quality and there were 37.41% of the data being moderately polluted and there were 59.29% of the data heavily polluted. The results of the calculation using the Storet method concluded that the condition of Surabaya River water quality was not good. We also apply the rule of the Storet Method to the Neural Network by using Surabaya River water quality data as learning data and gave performance 70.02% accuracy

CNN기반의 FusionNet 신경망과 농지 경계추출 알고리즘을 이용한 토지피복분류모델 개발

학위논문 (석사) -- 서울대학교 대학원 : 농업생명과학대학 생태조경.지역시스템공학부(지역시스템공학전공), 2021. 2. 송인홍.토지이용이 빠르게 변화함에 따라, 토지 피복에 대한 공간정보를 담고 있는 토지 피복 지도의 신속한 최신화는 필수적이다. 하지만, 현 토지 피복 지도는 많은 시간과 노동력을 요구하는 manual digitizing 방법으로 제작됨에 따라, 토지 피복 지도의 업데이트 및 배포에 긴 시간 간격이 발생하는 실정이다. 본 연구에서는 convolutional neural network (CNN) 기반의 인공신경망을 이용하여 high-resolution remote sensing (HRRS) 영상으로부터 토지 피복을 분류하는 모델을 개발하고, 특히 농지 경계추출 알고리즘을 적용하여 농업지역에서 분류 정확도를 개선하고자 하였다. 개발된 토지 피복 분류모델은 전처리(pre-processing) 모듈, 토지 피복 분류(land cover classification) 모듈, 그리고 후처리(post-processing) 모듈의 세 모듈로 구성된다. 전처리 모듈은 입력된 HRRS 영상을 75%씩 중첩 분할하여 관점을 다양화하는 모듈로, 한 관점에서 토지 피복을 분류할 때 발생할 수 있는 오분류를 줄이고자 하였다. 토지 피복 분류 모듈은 FusionNet model 구조를 바탕으로 개발되었고, 이는 분할된 HRRS 이미지의 픽셀별로 최적 토지 피복을 부여하도록 설계되었다. 후처리 모듈은 픽셀별 최종 토지 피복을 결정하는 모듈로, 분할된 HRRS 이미지의 분류결과를 취합하여 최빈값을 최종 토지 피복으로 결정한다. 추가로 농지에서는 농지경계를 추출하고, 필지별 분류된 토지 피복을 집계하여 한 필지에 같은 토지 피복을 부여하였다. 개발된 토지 피복 분류모델은 전라남도 지역(면적: 547 km2)의 2018년 정사영상과 토지 피복 지도를 이용하여 학습되었다. 토지 피복 분류모델 검증은 학습지역과 시간, 공간적으로 구분된, 2018년 전라남도 수북면과 2016년 충청북도 대소면의 두 검증지역에서 수행되었다. 각 검증지역에서 overall accuracy는 0.81, 0.71로 집계되었고, kappa coefficients는 0.75, 0.64로 산정되어 substantial 수준의 토지 피복 분류 정확도를 확인하였다. 특히, 개발된 모델은 필지 경계를 고려한 농업지역에서 overall accuracy 0.89, kappa coefficient 0.81로 almost perfect 수준의 우수한 분류 정확도를 보였다. 이에 개발된 토지 피복 분류모델은 특히 농업지역에서 현 토지 피복 분류 방법을 지원하여 토지 피복 지도의 빠르고 정확한 최신화에 기여할 수 있을 것으로 생각된다.The rapid update of land cover maps is necessary because spatial information of land cover is widely used in various areas. However, these maps have been released or updated in the interval of several years primarily owing to the manual digitizing method, which is time-consuming and labor-intensive. This study was aimed to develop a land cover classification model using the concept of a convolutional neural network (CNN) that classifies land cover labels from high-resolution remote sensing (HRRS) images and to increase the classification accuracy in agricultural areas using the parcel boundary extraction algorithm. The developed model comprises three modules, namely the pre-processing, land cover classification, and post-processing modules. The pre-processing module diversifies the perspective of the HRRS images by separating images with 75% overlaps to reduce the misclassification that can occur in a single image. The land cover classification module was designed based on the FusionNet model structure, and the optimal land cover type was assigned for each pixel of the separated HRRS images. The post-processing module determines the ultimate land cover types for each pixel unit by summing up the several-perspective classification results and aggregating the pixel-classification result for the parcel-boundary unit in agricultural areas. The developed model was trained with land cover maps and orthographic images (area: 547 km2) from the Jeonnam province in Korea. Model validation was conducted with two spatially and temporally different sites including Subuk-myeon of Jeonnam province in 2018 and Daseo-myeon of Chungbuk province in 2016. In the respective validation sites, the models overall accuracies were 0.81 and 0.71, and kappa coefficients were 0.75 and 0.64, implying substantial model performance. The model performance was particularly better when considering parcel boundaries in agricultural areas, exhibiting an overall accuracy of 0.89 and kappa coefficient 0.81 (almost perfect). It was concluded that the developed model may help perform rapid and accurate land cover updates especially for agricultural areas.Chapter 1. Introduction 1 1.1. Study background 1 1.2. Objective of thesis 4 Chapter 2. Literature review 6 2.1. Development of remote sensing technique 6 2.2. Land cover segmentation 9 2.3. Land boundary extraction 13 Chapter 3. Development of the land cover classification model 15 3.1. Conceptual structure of the land cover classification model 15 3.2. Pre-processing module 16 3.3. CNN based land cover classification module 17 3.4. Post processing module 22 3.4.1 Determination of land cover in a pixel unit 22 3.4.2 Aggregation of land cover to parcel boundary 24 Chapter 4. Verification of the land cover classification model 30 4.1. Study area and data acquisition 31 4.1.1. Training area 31 4.1.2. Verification area 32 4.1.3. Data acquisition 33 4.2. Training the land cover classification model 36 4.3. Verification method 37 4.3.1. The performance measurement methods of land cover classification model 37 4.3.2. Accuracy estimation methods of agricultural parcel boundary 39 4.3.3. Comparison of boundary based classification result with ERDAS Imagine 41 4.4. Verification of land cover classification model 42 4.4.1. Performance of land cover classification at the child subcategory 42 4.4.2. Classification accuracy of the aggregated land cover to main category 46 4.4.3. Classification accuracy of boundary based aggregation in agricultural area 57 Chapter 5. Conclusions 71 Reference 73 국 문 초 록 83Maste

Application of an Artificial Neural Network for the CPT-based Soil Stratigraphy Classification

Subsurface soil profiling is an essential step in a site investigation. The traditional methods for in situ investigations, such as SPT borings and sampling, have been progressively replaced by CPT soundings since they are fast, repeatable, economical and provide continuous parameters of the mechanical behaviour of the soils. However, the derived CPT-based stratigraphy profiles might present noisy thin layers, and its soil type description might not reflect a textural-based classification (i.e. Universal Soil Classification System, USCS). Thus, this paper presents a straightforward artificial neural network (ANN) algorithm, to classify CPT soundings according to the USCS. Data for training the model have been retrieved from SPT-CPT pairs collected after the 2011 Christchurch earthquake in New Zealand. The application of the ANN to case studies show how the method is a cost-effective and time-efficient approach, but more input parameters and data are needed for increasing its performance

Artificial neural networks as emulators of process-based models to analyse bathing water quality in estuaries

This study aims to provide a method for developing artificial neural networks in estuaries as emulators ofprocess-based models to analyse bathing water quality and its variability over time and space. Themethodology forecasts the concentration of faecal indicator organisms, integrating the accuracy andreliability offield measurements, the spatial and temporal resolution of process-based modelling, andthe decrease in computational costs by artificial neural networks whilst preserving the accuracy of re-sults. Thus, the overall approach integrates a coupled hydrodynamic-bacteriological model previouslycalibrated withfield data at the bathing sites into a low-order emulator by using artificial neural net-works, which are trained by the process-based model outputs. The application of the method to the EoEstuary, located on the northwestern coast of Spain, demonstrated that artificial neural networks areviable surrogates of highly nonlinear process-based models and highly variable forcings. The resultsshowed that the process-based model and the neural networks conveniently reproduced the measure-ments ofEscherichia coli(E. coli) concentrations, indicating a slightly betterfit for the process-basedmodel (R2¼0.87) than for the neural networks (R2¼0.83). This application also highlighted that dur-ing the model setup of both predictive tools, the computational time of the process-based approach was0.78 times lower than that of the artificial neural networks (ANNs) approach due to the additional timespent on ANN development. Conversely, the computational costs of forecasting are considerably reducedby the neural networks compared with the process-based model, with a decrease in hours of 25, 600,3900, and 31633 times for forecasting 1 h, 1 day, 1 month, and 1 bathing season, respectively. Therefore,the longer the forecasting period, the greater the reduction in computational time by artificial neuralnetworks

Sustainable marine ecosystems: deep learning for water quality assessment and forecasting

An appropriate management of the available resources within oceans and coastal regions is vital to guarantee their sustainable development and preservation, where water quality is a key element. Leveraging on a combination of cross-disciplinary technologies including Remote Sensing (RS), Internet of Things (IoT), Big Data, cloud computing, and Artificial Intelligence (AI) is essential to attain this aim. In this paper, we review methodologies and technologies for water quality assessment that contribute to a sustainable management of marine environments. Specifically, we focus on Deep Leaning (DL) strategies for water quality estimation and forecasting. The analyzed literature is classified depending on the type of task, scenario and architecture. Moreover, several applications including coastal management and aquaculture are surveyed. Finally, we discuss open issues still to be addressed and potential research lines where transfer learning, knowledge fusion, reinforcement learning, edge computing and decision-making policies are expected to be the main involved agents.Postprint (published version

Classification of hydro-meteorological conditions and multiple artificial neural networks for streamflow forecasting

Abstract. This paper presents the application of a modular approach for real-time streamflow forecasting that uses different system-theoretic rainfall-runoff models according to the situation characterising the forecast instant. For each forecast instant, a specific model is applied, parameterised on the basis of the data of the similar hydrological and meteorological conditions observed in the past. In particular, the hydro-meteorological conditions are here classified with a clustering technique based on Self-Organising Maps (SOM) and, in correspondence of each specific case, different feed-forward artificial neural networks issue the streamflow forecasts one to six hours ahead, for a mid-sized case study watershed. The SOM method allows a consistent identification of the different parts of the hydrograph, representing current and near-future hydrological conditions, on the basis of the most relevant information available in the forecast instant, that is, the last values of streamflow and areal-averaged rainfall. The results show that an adequate distinction of the hydro-meteorological conditions characterising the basin, hence including additional knowledge on the forthcoming dominant hydrological processes, may considerably improve the rainfall-runoff modelling performance

Coastal wetland mapping with sentinel-2 MSI imagery based on gravitational optimized multilayer perceptron and morphological attribute profiles.

Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery

Artificial neural network for non-intrusive electrical energy monitoring system

This paper discusses non-intrusive electrical energy monitoring (NIEM) system in an effort to minimize electrical energy wastages. To realize the system, an energy meter is used to measure the electrical consumption by electrical appliances. The obtained data were analyzed using a method called multilayer perceptron (MLP) technique of artificial neural network (ANN). The event detection was implemented to identify the type of loads and the power consumption of the load which were identified as fan and lamp. The switching ON and OFF output events of the loads were inputted to MLP in order to test the capability of MLP in classifying the type of loads. The data were divided to 70% for training, 15% for testing, and 15% for validation. The output of the MLP is either ‘1’ for fan or ‘0’ for lamp. In conclusion, MLP with five hidden neurons results obtained the lowest average training time with 2.699 seconds, a small number of epochs with 62 iterations, a min square error of 7.3872×10-5, and a high regression coefficient of 0.99050