Estimating the concentration of physico chemical parameters in hydroelectric power plant reservoir

The United Nations Educational, Scientific and Cultural Organization (UNESCO) defines the amazon region and adjacent areas, such as the Pantanal, as world heritage territories, since they possess unique flora and fauna and great biodiversity. Unfortunately, these regions have increasingly been suffering from anthropogenic impacts. One of the main anthropogenic impacts in the last decades has been the construction of hydroelectric power plants. As a result, dramatic altering of these ecosystems has been observed, including changes in water levels, decreased oxygenation and loss of downstream organic matter, with consequent intense land use and population influxes after the filling and operation of these reservoirs. This, in turn, leads to extreme loss of biodiversity in these areas, due to the large-scale deforestation. The fishing industry in place before construction of dams and reservoirs, for example, has become much more intense, attracting large populations in search of work, employment and income. Environmental monitoring is fundamental for reservoir management, and several studies around the world have been performed in order to evaluate the water quality of these ecosystems. The Brazilian Amazon, in particular, goes through well defined annual hydrological cycles, which are very importante since their study aids in monitoring anthropogenic environmental impacts and can lead to policy and decision making with regard to environmental management of this area. The water quality of amazon reservoirs is greatly influenced by this defined hydrological cycle, which, in turn, causes variations of microbiological, physical and chemical characteristics. Eutrophication, one of the main processes leading to water deterioration in lentic environments, is mostly caused by anthropogenic activities, such as the releases of industrial and domestic effluents into water bodies. Physico-chemical water parameters typically related to eutrophication are, among others, chlorophyll-a levels, transparency and total suspended solids, which can, thus, be used to assess the eutrophic state of water bodies. Usually, these parameters must be investigated by going out to the field and manually measuring water transparency with the use of a Secchi disk, and taking water samples to the laboratory in order to obtain chlorophyll-a and total suspended solid concentrations. These processes are time- consuming and require trained personnel. However, we have proposed other techniques to environmental monitoring studies which do not require fieldwork, such as remote sensing and computational intelligence. Simulations in different reservoirs were performed to determine a relationship between these physico-chemical parameters and the spectral response. Based on the in situ measurements, empirical models were established to relate the reflectance of the reservoir measured by the satellites. The images were calibrated and corrected atmospherically. Statistical analysis using error estimation was used to evaluate the most accurate methodology. The Neural Networks were trained by hydrological cycle, and were useful to estimate the physicalchemical parameters of the water from the reflectance of visible bands and NIR of satellite images, with better results for the period with few clouds in the regions analyzed. The present study shows the application of wavelet neural network to estimate water quality parameters using concentration of the water samples collected in the Amazon reservoir and Cefni reservoir, UK. Sattelite imagens from Landsats and Sentinel-2 were used to train the ANN by hydrological cycle. The trained ANNs demonstrated good results between observed and estimated after Atmospheric corrections in satellites images. The ANNs showed in the results are useful to estimate these concentrations using remote sensing and wavelet transform for image processing. Therefore, the techniques proposed and applied in the present study are noteworthy since they can aid in evaluating important physico-chemical parameters, which, in turn, allows for identification of possible anthropogenic impacts, being relevant in environmental management and policy decision-making processes. The tests results showed that the predicted values have good accurate. Improving efficiency to monitor water quality parameters and confirm the reliability and accuracy of the approaches proposed for monitoring water reservoirs. This thesis contributes to the evaluation of the accuracy of different methods in the estimation of physical-chemical parameters, from satellite images and artificial neural networks. For future work, the accuracy of the results can be improved by adding more satellite images and testing new neural networks with applications in new water reservoirs

Adaptive Neuro-Fuzzy Inference System integrated with solar zenith angle for forecasting sub-tropical photosynthetically active radiation

Advocacy for climate mitigation aims to minimize the use of fossil fuel and to support clean energy adaptation. While alternative energies (e.g., biofuels) extracted from feedstock (e.g., micro‐algae) represent a promising role, their production requires reliably modeled photosynthetically active radiation (PAR). PAR models predict energy parameters (e.g., algal carbon fixation) to aid in decision‐making at PAR sites. Here, we model very short‐term (5‐min scale), sub‐tropical region's PAR with an Adaptive Neuro‐Fuzzy Inference System model with a Centroid‐Mean (ANFIS‐CM) trained with a non‐climate input (i.e., only the solar angle, θZ). Accuracy is benchmarked against genetic programming (GP), M5Tree, Random Forest (RF), and multiple linear regression (MLR). ANFIS‐CM integrates fuzzy and neural network algorithms, whereas GP adopts an evolutionary approach, M5Tree employs binary decision, RF employs a bootstrapped ensemble, and MLR uses statistical tools to link PAR with θZ. To design the ANFIS‐CM model, 5‐min θZ (01–31 December 2012; 0500H–1900H) for sub‐tropical, Toowoomba are utilized to extract predictive features, and the testing accuracy (i.e., differences between measurements and forecasts) is evaluated with correlation (r), root‐mean‐square error (RMSE), mean absolute error (MAE), Willmott (WI), Nash–Sutcliffe (ENS), and Legates & McCabes (ELM) Index. ANFIS‐CM and GP are equivalent for 5‐min forecasts, yielding the lowest RMSE (233.45 and 233.01μ mol m−2s−1) and MAE (186.59 and 186.23 μmol m−2s−1). In contrast, MLR, M5Tree, and RF yields higher RMSE and MAE [(RMSE = 322.25 μmol m−2s−1, MAE = 275.32 μmol m−2s−1), (RMSE = 287.70 μmol m−2s−1, MAE = 234.78 μmol m−2s−1), and (RMSE = 359.91 μmol m−2s−1, MAE = 324.52 μmol m−2s−1)]. Based on normalized error, ANFIS‐CM is considerably superior (MAE = 17.18% versus 19.78%, 34.37%, 26.39%, and 30.60% for GP, MLR, M5Tree, and RF models, respectively). For hourly forecasts, ANFIS‐CM outperforms all other methods (WI = 0.964 vs. 0.942, 0.955, 0.933 & 0.893, and ELM = 0.741 versus 0.701, 0.728, 0.619 & 0.490 for GP, MLR, M5Tree, and RF, respectively). Descriptive errors support the versatile predictive skills of the ANFIS‐CM model and its role in real‐time prediction of the photosynthetic‐active energy to explore biofuel generation from micro‐algae, studying food chains, and supporting agricultural precision

Very short-term photovoltaic power forecasting with cloud modeling: A review

This paper endeavors to provide the reader with an overview of the various tools needed to forecast photovoltaic (PV) power within a very short-term horizon. The study focuses on the specific application of a large scale grid-connected PV farm. Solar resource is largely underexploited worldwide whereas it exceeds by far humans' energy needs. In the current context of global warming, PV energy could potentially play a major role to substitute fossil fuels within the main grid in the future. Indeed, the number of utility-scale PV farms is currently fast increasing globally, with planned capacities in excess of several hundred megawatts. This makes the cost of PV-generated electricity quickly plummet and reach parity with non-renewable resources. However, like many other renewable energy sources, PV power depends highly on weather conditions. This particularity makes PV energy difficult to dispatch unless a properly sized and controlled energy storage system (ESU) is used. An accurate power forecasting method is then required to ensure power continuity but also to manage the ramp rates of the overall power system. In order to perform these actions, the forecasting timeframe also called horizon must be first defined according to the grid operation that is considered. This leads to define both spatial and temporal resolutions. As a second step, an adequate source of input data must be selected. As a third step, the input data must be processed with statistical methods. Finally, the processed data are fed to a precise PV model. It is found that forecasting the irradiance and the cell temperature are the best approaches to forecast precisely swift PV power fluctuations due to the cloud cover. A combination of several sources of input data like satellite and land-based sky imaging also lead to the best results for very-short term forecasting

Optimization of windspeed prediction using an artificial neural network compared with a genetic programming model

The precise prediction of windspeed is essential in order to improve and optimize wind power prediction. However, due to the sporadic and inherent complexity of weather parameters, the prediction of windspeed data using different patterns is difficult. Machine learning (ML) is a powerful tool to deal with uncertainty and has been widely discussed and applied in renewable energy forecasting. In this chapter, the authors present and compare an artificial neural network (ANN) and genetic programming (GP) model as a tool to predict windspeed of 15 locations in Queensland, Australia. After performing feature selection using neighborhood component analysis (NCA) from 11 different metrological parameters, seven of the most important predictor variables were chosen for 85 Queensland locations, 60 of which were used for training the model, 10 locations for model validation, and 15 locations for the model testing. For all 15 target sites, the testing performance of ANN was significantly superior to the GP model

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management

Computational intelligence techniques for maritime and coastal remote sensing

The aim of this thesis is to investigate the potential of computational intelligence techniques for some applications in the analysis of remotely sensed multi-spectral images. In particular, two problems are addressed. The first one is the classification of oil spills at sea, while the second one is the estimation of sea bottom depth. In both cases, the exploitation of optical satellite data allows to develop operational tools for easily accessing and monitoring large marine areas, in an efficient and cost effective way. Regarding the oil spill problem, today public opinion is certainly aware of the huge impact that oil tanker accidents and oil rig leaks have on marine and coastal environment. However, it is less known that most of the oil released in our seas cannot be ascribed to accidental spills, but rather to illegal ballast waters discharge, and to pollutant dumping at sea, during routine operations of oil tankers. For this reason, any effort for improving oil spill detection systems is of great importance. So far, Synthetic Aperture Radar (SAR) data have been preferred to multi-spectral data for oil spill detection applications, because of their all-weather and all-day capabilities, while optical images necessitate of clear sky conditions and day-light. On the other hand, many features make an optical approach desirable, such as lower cost and higher revisit time. Moreover, unlike SAR data, optical data are not affected by sea state, and are able to reduce false alarm rate, since they do not suffer from the main false alarm source in SAR data, that is represented by the presence of calm sea regions. In this thesis the problem of oil spill classification is tackled by applying different machine learning techniques to a significant dataset of regions of interest, collected in multi-spectral satellite images, acquired by MODIS sensor. These regions are then classified in one of two possible classes, that are oil spills and look-alikes, where look-alikes include any phenomena other than oil spills (e.g. algal blooms...). Results show that efficient and reliable oil spill classification systems based on optical data are feasible, and could offer a valuable support to the existing satellite-based monitoring systems. The estimation of sea bottom depth from high resolution multi-spectral satellite images is the second major topic of this thesis. The motivations for dealing with this problem arise from the necessity of limiting expensive and time consuming measurement campaigns. Since satellite data allow to quickly analyse large areas, a solution for this issue is to employ intelligent techniques, which, by exploiting a small set of depth measurements, are able to extend bathymetry estimate to a much larger area, covered by a multi-spectral satellite image. Such techniques, once that the training phase has been completed, allow to achieve very accurate results, and, thanks to their generalization capabilities, provide reliable bathymetric maps which cover wide areas. A crucial element is represented by the training dataset, which is built by coupling a number of depth measurements, located in a limited part of the image, with corresponding radiances, acquired by the satellite sensor. A successful estimate essentially depends on how the training dataset resembles the rest of the scene. On the other hand, the result is not affected by model uncertainties and systematic errors, as results from model-based analytic approaches are. In this thesis a neuro-fuzzy technique is applied to two case studies, more precisely, two high resolution multi-spectral images related to the same area, but acquired in different years and in different meteorological conditions. Different situations of in-situ depths availability are considered in the study, and the effect of limited in-situ data availability on performance is evaluated. The effect of both meteorological conditions and training set size reduction on the overall performance is also taken into account. Results outperform previous studies on bathymetry estimation techniques, and allow to give indications on the optimal paths which can be adopted when planning data collection at sea

Statistical Assessment Of Terra Modis Aerosol Optical Depth (C051) Over Coastal Regions

Produk aerosol dari Spektrometer Pengimejan Resolusi Sederhana (MODIS) telah digunakan secara meluas untuk menangani alam sekitar dan isu-isu berkaitan perubahan dengan liputan global setiap hari. Kedalaman optic aerosol (AOD) yang diambil oleh algoritma yang berbeza berdasarkan permukaan piksel, menentukan antara tanah dan laut. Produk MODIS-Terra dan Global Aerosol Robotik Network (AERONET) boleh didapati daripada Multi-sensor Aerosol Product Sampling System (MAPSS) bagi kawasan-kawasan pantai sepanjang tahun 2000-2010. Dengan menggunakan data yang dikumpul daripada 83 stesen pantai dan 158 bukan pantai di seluruh dunia dari AERONET 2000-2010, penilaian ketepatan dibuat untuk kedalaman optic aerosol pantai (AOD) diambil dari MODIS di atas satelit Terra. Tujuan utama penilaian statistik AOD di kawasan pantai adalah untuk melahirkan MODIS AOD terubahsuai dengan ralat yang minimum berbanding dengan nilai rujukan yang diberikan oleh AERONET. Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products have been widely used to address environment and climate change issues with daily global coverage. Aerosol optical depth (AOD) is retrieved by different algorithms based on the pixel surface, determining between land and ocean. MODIS-Terra and Global Aerosol Robotic Network (AERONET) products can be obtained from the Multi-sensor Aerosol Products Sampling System (MAPSS) for coastal regions during 2000-2010. Using data collected from 83 coastal and 158 non-coastal stations worldwide from AERONET from 2000-2010, accuracy assessments are made for coastal aerosol optical depth (AOD) retrieved from MODIS aboard the Terra satellite. The main aim of this statistical assessment of AOD over coastal regions is to produce modified MODIS AOD with minimum error when compared with the reference value given by AERONET

An innovative metaheuristic strategy for solar energy management through a neural networks framework

Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development

Estimation of near-surface Air temperature during day and night-time from MODIS over Different LC/LU Using machine learning methods in Berlin

Urbanization is manifest by changes in the physical structure of the land surface, owing to extensive construction features such as buildings, street canyons, changes in the thermal structure because of materials of different thermal properties and also intensive human activities. Urban areas are generally also characterized by higher surface air temperatures as compared to the rural surroundings. This temperature excess can be up to 10-12°C and more and is referred to as the urban heat island(UHI)phenomenon. Since residents living in cities are especially affected by extreme temperature events, urban climate studies are gaining in importance. Currently, more than half of the world s population already lives in urban areas, which accentuates the major role agglomerations must play in mitigation and adaptation to climate change. Recommendations regarding behavioural patterns during heat stress situations and urban planning measures require a comprehensive understanding of the inner urban temperature distribution including the identification of thermal hot spots. Both very cold and very hot temperatures could affect the human health. Excessive exposure to heat is referred to as heat stress and excessive exposure to cold is referred to as cold stress. Urban temperature data (2 m temperature data) is very important for all investigations on the urban heat island (UHI) effect, human health. They are usually either based on remote sensing techniques or air temperature measurements or from models. Remote sensing data like infra-red surface temperature from airborne measuring instruments may have a very high spatial resolution and are presently available for many urban areas, but only in clear sky cases. This spatial resolution is appropriate to exhibit typical urban structures that are expected to cause the UHI effect. Nevertheless, information on surface temperature cannot replace air temperature data, since beside the problem that the former is typically only available for single days, there is no fixed relation between surface and air temperatures. Especially for systematic analyses of the relationship between urban structures and 2m temperatures for different weather situations a large data basis is desirable. Air temperature data can be obtained from mobile measurements and measurement at permanent or temporary weather stations. On the one hand, the use of weather stations provides high data accuracy using a well-known standard technology. On the other hand, the spatial representation of weather station data within the urban environment, which is characterized by the surface composition including buildings, infrastructure and different types of land use, is very limited. Consequently, since the beginning of the 20th century, many efforts have been made to identify temperature patterns in urban areas with high spatial resolution instead of only using single point information. In this regard, in this study Air temperature (T2m) or Tair measurements from 20 ground weather stations in Berlin were used to estimate the relationship between air temperature and the remotely sensed land surface temperature (LST) measured by Moderate Resolution Imaging Spectroradiometer over different land-cover types (LCT). Knowing this relationship enables a better understanding of the magnitude and pattern of Urban Heat Island (UHI), by considering the contribution of land cover in the formation of UHI. In order to understand the seasonal behaviour of this relationship, the influence of the normalized difference vegetation index (NDVI) as an indicator of degree of vegetation on LST over different LCT was investigated. Next to it, to evaluate the influence of LCT, a regression analysis between LST and NDVI was made. The results demonstrate that the slope of regression depends on the LCT. It depicts a negative correlation between LST and NDVI over all LCTs. Our analysis indicates that the strength of correlations between LST and NDVI depends on the season, time of day, and land cover. This statistical analysis can also be used to assess the variation of the LST– T2m relationship during day- and night-time over different land covers. The results show that LSTDay and LSTNight are correlated significantly (p = 0.0001) with T2mDay(daytime air temperature) and T2mNight(night-time air temperature). The correlation (r) between LSTDay and TDay is higher in cold seasons than in warm seasons. Moreover, during cold seasons over every LCT, a higher correlation was observed during daytime than during night-time. In contrast, a reverse relationship was observed during warm seasons. It was found that in most cases, during daytime and in cold seasons, LST is lower than T2m. In warm seasons, however, a reverse relationship was observed over all land-cover types. In every season, LSTNight was lower than or close to T2mNight . Air temperature (Tair or T2m) is an important climatological variable for forest biosphere processes and climate change research. Due to the low density and the uneven distribution of weather stations, traditional ground-based observations cannot accurately capture the spatial distribution of Tair. Therefore, it is necessary to develop a method for the estimation of air temperature with reasonable accuracy and spatial and temporal resolution in the urban areas with low temperature gauge density. But since the estimation of meteorological variables using various statistical techniques (such as linear regression models or combined regression and kriging techniques for T interpolation) have been examined by many researchers and they came to conclusion that an appropriate machine learning technique could be a robust computational technique which has been used for the estimation of meteorological data as a function of the corresponding data of one or more reference stations. In this research, Tair in Berlin is estimated during the day and night-time over six land cover/land use (LC/LU) types by satellite remote sensing data over a large domain and a relatively long period (7 years). Aqua and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) data and meteorological data for the period from 2007 to 2013 were collected to estimate Tair. Twelve environmental variables (land surface temperature (LST), normalized difference vegetation index (NDVI), Julian day, latitude, longitude, Emissivity31, Emissivity32, altitude, albedo, wind speed, wind direction and air pressure) were selected as predictors. Moreover, a comparison between LST from MODIS Terra and Aqua with daytime and night-time air temperatures (TDay , TNight) was done respectively and in addition, the spatial variability of LST and Tair relationship by applying a varying window size on the MODIS LST grid was examined. An analysis of the relationship between the observed Tair and the spatially averaged remotely sensed LST, indicated that 3 × 3 and 1 × 1 pixel size was the optimal window size for the statistical model estimating Tair from MODIS data during the day and night time, respectively. Three supervised learning methods (Adaptive Neuro Fuzzy Inference system (ANFIS), Artificial Neural Network (ANN) and Support vector machine (SVR)) were used to estimate Tair during the day and night-time, and their performances were validated by cross-validation for each LC/LU. by applying each technique, a estimator model of air temperature had been generated. The comparison between these methods has been done and finally we evaluated the accuracy of each model and choose the best one for the high-resolution temperature estimation. Moreover, tuning the hyper parameters of some models like SVR and ANN were investigated. For tuning the hyper parameters of SVR, Simulated Annealing (SA) was applied (SA-SVR model) and a multiple-layer feed-forward (MLF) neural networks with three layers and variable nodes in hidden layers had been applied with Levenberg-Marquardt back-propagation (LM-BP), in order to achieve higher accuracy in the estimation of Tair . Results indicated that the ANN model achieved better accuracy (RMSE=2.16°C, MAE =1.69°C, R2 =0.95) than SA-SVR model (RMSE= 2.50°C, MAE =1.92°C, R2=0.91) and ANFIS model (RMSE=2.88°C, MAE=2.2°C, R2=0.89) over six LC/LU during the day and night time. The Q-Q diagram of SA-SVR, ANFIS and ANN show that all three models slightly tended to underestimate and overestimate the extreme and low temperatures for all LC/LU classes during the day and night-time. The weak performance in the extreme and low temperatures are a consequence of the small numbers of data in these temperatures. These satisfactory results indicate that this approach is proper for estimating air temperature and spatial window size is an important factor that should be considered in the estimation of air temperature. Moreover, for better understanding the relationship between LST and Tair in Berlin during day and night-time, over six land LC/LU types namely airport, agriculture, urban area, forest, industrial and needle leaf trees, two input variable selection methods were applied. Input variable selection is an essential step in environmental, biological, industrial and climatological applications. One approach which help us in better understanding data, decreasing computation effort, the impact of curse of dimensionality and improving the estimator performance. Through input variable selection the irrelevant or redundant variables will be to eliminated therefore a suitable subset of variables is identified as the input of a model. Meanwhile, the complexity of the model structure is simplified, and the computational efficiency is improved. In this work, the two input variable selection methods, including brute force search and greedy best search algorithm using artificial neural network (ANN) were considered for estimating of near surface air temperature from MODIS over six LC/LU types. The motivation behind this research was to formulate a more efficient way of choosing input variables using ANN models of environmental processes. Moreover, AIC, BIC and RMSE are considered for ranking the features and finding a subset of potential variables which improves the overall estimation performance. In this study, Aqua and Terra MODIS data and meteorological data for the period from 2007 to 2013 were collected to estimate Tair .Moreover, twelve environmental variables LST, normalized difference vegetation index (NDVI), Julian day, latitude, longitude, Emis31, Emis32, altitude, albedo, wind speed, wind direction and air pressure were selected as predictors. The results show that the LC/LU has a key factor in the relationship between Tair and LST. The results show that the effectiveness of optimal models in estimation Tair is varied in different LC/LU because of the specific heat capacities of different LC/LU. Air temperature mainly rely on the heat transfer process which was significantly affected by the local radiation budget. Generally, air is heated much quicker over barren land than forest because, barren land has lower heat capacity than forest. Vegetation can cause to latent heat flux, such as enhancing or reducing transpiration and cool the Tair in forests. In this study, the cooling effect was not taken into account because of roughly distribution of meteorological stations across different vegetation types. Therefore, it was difficult to consider the vegetation type in our models. However, land cover also affected land surface albedo, thus, the influence of LU/LC on estimating Tair was conditional and time dependent because different variables are selected for the same LU/LC during day and night-time. Moreover, another issue that we tried to find an answer was, what is the pitfall of using the global model and what is the advantage of features selection? It has been debated that inferencing from a model with all the features which thought to be important is simple and avoid the complications of model selection.Urbanisierung stellt eine Veränderungen in der physikalischen Struktur der Landoberfläche durch umfangreiche Konstruktionsmerkmale, wie Gebäude und Straßenschluchten, dar. Die damit verbundenen Änderungen der thermischen Struktur durch Verwendung von Materialien mit unterschiedlichen thermischen Eigenschaften sowie intensive menschliche Aktivität spielen hierbei eine wichtige Rolle. Urbane Gebiete sind im Allgemeinen durch eine höhere Oberflächentemperatur im Vergleich zur ländlichen Umgebung gekennzeichnet. Der Temperaturüberschuss kann bis zu 10-12°C und mehr betragen und wird als Phänomen der städtischen Wärmeinsel (UHI) bezeichnet. Da in Städten lebende Menschen besonders stark von extremen Temperaturereignissen betroffen sind, gewinnen Studien zum urbanen Klima vermehrt an Bedeutung. Derzeit lebt mehr als die Hälfte der Weltbevölkerung in urbanen Gebieten. Dies unterstreicht die wichtige Rolle die Ballungsräume in Bezug auf Minderung und Anpassung an den Klimawandel darstellt. Empfehlungen bezüglich des Verhaltens während Hitzestresssituationen sowie städtebauliche Maßnahmen erfordern ein umfangreiches Verständnis der innerstädtischen Temperaturverteilung einschließlich der Identifizierung von thermischen Hotspots. Sehr kalte wie auch sehr heiße Temperaturen können gleichermaßen die menschliche Gesundheit beeinträchtigen. Übermäßige Hitzebelastung wird als Hitzestress bezeichnet, übermäßige Kältebelastung als Kältestress. Urbane Temperaturdaten (2m Temperaturdaten) sind wichtig für alle Untersuchungen bezüglich des urbanen Wärmeinseleffekts (UHI), der menschlichen Gesundheit. Normalerweise basieren die Daten entweder auf Fernerkundungstechniken oder auf Messungen oder Simulationen der Lufttemperatur. Fernerkundungsdaten, wie die der Infrarot-Oberflächentemperatur von satellitengestützen Messinstrumenten können eine sehr hohe räumliche Auflösung haben und sind gegenwärtig für viele urbane Gebiete zugänglich, doch nur im Fall von wolkenfreiem Himmel. Die räumliche Auflösung ist dafür geeignet typische urbane Strukturen zu erkennen, die den UHI-Effekt auslösen. Dennoch können Informationen der Oberflächentemperatur, die Lufttemperaturdaten nicht ersetzen, da neben dem Problem, dass die Oberflächentemperatur in der Regel nur für einzelne Tage zur Verfügung steht, es keinen festen Zusammenhang zwischen Oberflächentemperatur und Lufttemperatur besteht. Besonders für systematische Analysen der Zusammenhänge zwischen urbanen Strukturen und der 2m-Temperatur unterschiedlicher Wettersituationen ist eine hohe Datenbasis wünschenswert. Daten der Lufttemperatur können von mobilen Messungen und permanenten Messstationen oder von temporären Wetterstationen erhalten werden. Einerseits bieten die Wetterstationen eine hohe Datenqualität durch Verwendung von bekannten Standard-Technologien; andererseits ist die räumliche Verteilung der Wetterstationsdaten in der urbanen Umgebung, die durch die oberflächliche Komposition von Gebäuden, Infrastruktur und verschiedenen Landnutzungsklassen charakterisiert ist, sehr eingeschränkt. Seit Beginn des 20. Jahrhunderts konnten somit viele Vorzüge bei der Identifizierung von Temperaturmustern in urbanen Gebieten mit hoher räumlicher Auflösung erzielt werden anstatt nur einzelne Punktinformationen zu nutzen. Folglich werden für diese Studie Lufttemperatur (T2m) oder Tair Messungen von 20 Bodenwetterstationen in Berlin verwendet, um den Zusammenhang zwischen Lufttemperatur und Fernerkundungsdaten der Oberflächentemperatur (LST) gemessen vom Moderate Resolution Imaging Spectroradiometer (MODIS) über verschiedene Landnutzungstypen (LCT). Die Kenntnis über diesen Zusammenhang ermöglicht ein besseres Verständnis der Stärke und Muster von urbanen Wärmeinseln (UHI) durch Beachtung der Verteilung der Oberflächenbeschaffenheit bei Ausbildung von UHI. Um das saisonale Verhalten dieses Zusammenhangs zu verstehen, wurde der Einfluss des normalisierten Differenzvegetationsindex (NDVI) als ein Indikator für den Vegetationsgrad auf LST über verschiedene LCT untersucht. Darüber hinaus wurde eine Regressionsanalyse zwischen der LST und dem NDVI durchgeführt, um den Einfluss der LCT zu bewerten. Die Ergebnisse zeigen, dass die Steigung der Regressionsgeraden von der LST abhängt. Es besteht eine negative Korrelation zwischen LST und NDVI über alle LCTs. Unsere Analyse signalisiert, dass die Stärke der Korrelation zwischen LST und NDVI von der Jahreszeit, der Tageszeit sowie der Landnutzung abhängig ist. Die statistische Analyse kann auch verwendet werden, um die Variation der LST-T2m-Beziegung während der Tages- und Nachtzeit über verschiedene Bodenbedeckungen zu bewerten. Die Ergebnisse zeigen eine signifikante Korrelation (p=0.0001) von LSTday und LSTnight mit der T2mDay (Lufttemperatur tagsüber) und der T2mNight (Lufttemperatur nachts). Zwischen LSTday und Tday ist die Korrelation (r) in der kalten Jahreszeit höher als in der warmen. Darüber hinaus wurde eine höhere Korrelation während der kalten Jahreszeit über alle LCTs am Tag beobachtet als in der Nacht. In der warmen Jahreszeit wurde im Gegensatz dazu ein umgekehrter Zusammenhang festgestellt. Es wurde beobachtet, dass in den meisten Fällen, tagsüber und in kalten Jahreszeiten, die LST niedriger ist als die T2m. In warmen Jahreszeiten wurde jedoch ein umgekehrter Zusammenhang über alle Landbedeckungsarten beobachtet. In jeder Saison war die LSTNight niedriger oder fast gleich wie die T2mNight. Die Lufttemperatur (Tair oder T2m) ist eine wichtige klimatologischen Variable für Prozesse der Waldbiosphäre und die Erforschung des Klimawandels. Aufgrund der geringen Dichte und der ungleichmäßigen Verteilung von Wetterstationen können herkömmliche bodengebundene Beobachtungen die räumliche Verteilung von Tair nicht genau erfassen. Daher ist es notwendig, eine Methode zur Abschätzung der Lufttemperatur mit angemessener Genauigkeit sowie räumlicher und zeitlicher Auflösung in urbanen Gebieten mit niedriger Temperaturmessdichte zu entwickeln. Da aber die Abschätzung meteorologischer Variablen mit verschiedenen statistischen Techniken (wie linearen Regressionsmodellen und kombinierten Regressions- und Krigingtechniken für die T-Interpolation) von vielen Forschern untersucht wurde, kamen sie zu dem Schluss, dass eine geeignete ‚machine learning‘ Technik eine robuste Rechentechnik sein könnte, die für die Abschätzung meteorologischer Daten in Abhängigkeit von entsprechenden Daten einer oder mehrerer Referenzstationen verwendete. In dieser Studie wird Tair in Berlin tagsüber sowie nachts über sechs Landbedeckungs/Landnutzungsarten (LC/LU) mittels Satelliten-Fernerkundungsdaten über einen großen Bereich und einem relativ langen Zeitraum (7Jahre) geschätzt. Daten des ‚Terra und Aqua MODIS‘ (Moderate Resolution Imaging Spectroradiometer) und meteorologische Daten für den Zeitraum von 2007 bis 2013 wurden gesammelt, um Tair zu bestimmen. Als Prädikatoren wurden zwölf Umweltvariablen (Landoberflächentemperatur (LST), normalisierter Differenzvegetationsindex (NDVI), Julianischer Tag, Breitengrad, Längengrad, Emissionsgrad 31, Emissionsgrad 32, Höhe, Albedo, Windgeschwindigkeit, Windrichtung und Luftdruck) ausgewählt. Drüber hinaus wurde ein Vergleich zwischen LST von MODIS Terra und Aqua mit Tages- und Nachtlufttemperaturen (TDay , TNight) durchgeführt bzw. zusätzlich die räumliche Variabilität des Zusammenhangs von LST und Tair durch Anwendung einer variierenden Fenstergröße auf das MODIS LST-Gitter untersucht. Eine Analyse der Beziehung zwischen der beobachteten Tair und dem räumlich gemittelten Fernerkundungs-LST ergab, dass die Größe 3 x 3 und 1 x 1 Pixel die optimale Fenstergröße für das statistische Modell war, das Tair aus den MODIS-Daten während Tages- bzw. Nachtzeit schätzte. Drei überwachte Lernmethoden (Adaptive Neuro Fuzzy Inference system (ANFIS), künstliches neuronales Netzwerk (ANN) und Support vector machine (SVR)) wurden verwendet, um Tair währen des Tages und der Nacht zu schätzen. Die Leistungen wurden durch Kreuzvalidierung für jede LC/LU validiert. Durch die Anwendung jeder Technik wurde ein Schätzmodell ausgewertet und das Beste für die hochauflösende Temperaturschätzung ausgewählt. Darüber hinaus wurde die Einstellung der Hyperparameter einiger Modelle wie SVR und ANN untersucht. Für die Einstellung der Hyperparameter von SVR wurde ‚Simulated Annealing‘ (SA) angewendet (SA-SVR Modell). Mit der Levenberg-Marquardt Backpropagation (LM-BP) wurde ein mehrschichtiges Feed-forward (MLF) neuronales Netzwerk mit drei Schichten und variablen Knoten in versteckten Schichten angewendet, um eine höhere Genauigkeit bei der Schätzung von Tair zu erreichen. Die Ergebnisse zeigten, dass das ANN-Modell über sechs LC/LU, tags sowie nachts, eine höhere Genauigkeit erreichte (RMSE=2.16°C, MAE =1.69°C, R2 =0.95) als das SA-SVR-Modell (RMSE= 2.50°C, MAE =1.92°C, R2=0.91) und das ANFIS-Modell (RMSE=2.88°C, MAE=2.2°C, R2=0.89). Das Q-Q-Diagramm von SA-SVR, ANFIS und ANN zeigt, dass alle drei Modelle die extrem hohen und niedrigen Temperaturen für alle LC/LU-Klassen tagsüber sowie nachts leicht unterschätzen und überschätzen. Die schwache Leistung bei extrem hohen und niedrigen Temperaturen ist eine Folge der geringen Datenmenge bei diesen Temperaturen. Um den Zusammenhang zwischen LST und Tair in Berlin bei Tag und Nacht besser verstehen zu können, wurden über sechs Land-LC/LU-Typen (Flughafen, Landwirtschaft, urbanes Gebiet, Wald-, Industrie- und Nadelblattbäume) zwei Auswahlmethoden für die Eingangsvariablen angewendet. Die Auswahl dieser Variablen ist ein wesentlicher Schritt in ökologischen, biologischen, industriellen und klimatologischen Anwendungen. Ein Ansatz, der uns hilft, Daten besser zu verstehen, den Rechenaufwand zu verringern, die Auswirkungen des Fluches der Dimensionalität und die Lei