City Of Tetouan Research Articles

Google Earth Engine and Machine Learning for Flash Flood Exposure Mapping—Case Study: Tetouan, Morocco

Recently, the earth’s climate has changed considerably, leading to several hazards, including flash floods (FFs). This study aims to introduce an innovative approach to mapping and identifying FF exposure in the city of Tetouan, Morocco. To address this problem, the study uses different machine learning methods applied to remote sensing imagery within the Google Earth Engine (GEE) platform. To achieve this, the first phase of this study was to map land use and land cover (LULC) using Random Forest (RF), a Support Vector Machine (SVM), and Classification and Regression Trees (CART). By comparing the results of five composite methods (mode, maximum, minimum, mean, and median) based on Sentinel images, LULC was generated for each method. In the second phase, the precise LULC was used as a related factor to others (Stream Power Index (SPI), Topographic Position Index (TPI), Slope, Profile Curvature, Plan Curvature, Aspect, Elevation, and Topographic Wetness Index (TWI)). In addition to 2024 non-flood and flood points to predict and detect FF susceptibility, 70% of the dataset was used to train the model by comparing different algorithms (RF, SVM, Logistic Regression (LR), Multilayer Perceptron (MLP), and Naive Bayes (NB)); the rest of the dataset (30%) was used for evaluation. Model performance was evaluated by five-fold cross-validation to assess the model’s ability on new data using metrics such as precision, score, kappa index, recall, and the receiver operating characteristic (ROC) curve. In the third phase, the high FF susceptibility areas were analyzed for two-way validation with inundated areas generated from Sentinel-1 SAR imagery with coherent change detection (CDD). Finally, the validated inundation map was intersected with the LULC areas and population density for FF exposure and assessment. The initial results of this study in terms of LULC mapping showed that the most appropriate method in this research region is the use of an SVM trained on a mean composite. Similarly, the results of the FF susceptibility assessment showed that the RF algorithm performed best with an accuracy of 96%. In the final analysis, the FF exposure map showed that 2465 hectares were affected and 198,913 inhabitants were at risk. In conclusion, the proposed approach not only allows us to assess the impact of FF in this study area but also provides a versatile approach that can be applied in different regions around the world and can help decision-makers plan FF mitigation strategies.

Forecast of wind speed based on MLP network model using Levenberg Marquardt and gradient descent algorithms in Tetouan city, Northern Morocco

This study aims to find the most powerful algorithm between LM and GD, applying them to the multilayer neural network (MLP) to predict the wind speed of the city of Tetouan. To achieve this we will use the meteorological data of this city from 31/07/2017 to 31/08/2022. The MLP adopted for our study is composed of two hidden layers, 30 neurons in the first layer and 15 in the second, 7 inputs and one output. The data is divided into 80% for training and 20% for testing. The results obtained showed that the Levenberg-Marquardt (LM) algorithm is more efficient than the gradient descent (GD) algorithm with a correlation coefficient R = 0.988102 and a mean square error MSE = 0. 0458. These results will allow us to accurately predict the wind speed of August for the year 2022 in this city.

A novel wind power prediction model using graph attention networks and bi-directional deep learning long and short term memory

<span lang="EN-US">Today, integrating wind energy forecasting is an important area of research due to the erratic nature of wind. To achieve this goal, we propose a new model of wind speed prediction based on graph attention networks (GAT), we added a new attention mechanism and a learnable adjacency matrix to the GAT structure to obtain attention scores for each weather variable. The results of the GAT-based model are merged with the bi-directional deep learning long and short-term memory (BiLSTM) layer to take advantage of the geographic and temporal properties of historical weather data. The experiments and analyzes are carried out using precise meteorological data collected from wind farms in the Moroccan city of Tetouan. We show that the proposed model can learn complex input-output correlations of meteorological data more efficiently than previous wind speed prediction algorithms. Due to the resulting attention weights, the model also provides more information about the main weather factors for the evaluated forecast work.</span>

Long-Term Power Load Forecasting Using LSTM-Informer with Ensemble Learning

Accurate power load forecasting can facilitate effective distribution of power and avoid wasting power so as to reduce costs. Power load is affected by many factors, so accurate forecasting is more difficult, and the current methods are mostly aimed at short-term power load forecasting problems. There is no good method for long-term power load forecasting problems. Aiming at this problem, this paper proposes an LSTM-Informer model based on ensemble learning to solve the long-term load forecasting problem. The bottom layer of the model uses the long short-term memory network (LSTM) model as a learner to capture the short-term time correlation of power load, and the top layer uses the Informer model to solve the long-term dependence problem of power load forecasting. In this way, the LSTM-Informer model can not only capture short-term time correlation but can also accurately predict long-term power load. In this paper, a one-year dataset of the distribution network in the city of Tetouan in northern Morocco was used for experiments, and the mean square error (MSE) and mean absolute error (MAE) were used as evaluation criteria. The long-term prediction of this model is 0.58 and 0.38 higher than that of the lstm model based on MSE and MAE. The experimental results show that the LSTM-Informer model based on ensemble learning has more advantages in long-term power load forecasting than the advanced baseline method.

Climate risk assessment of the Tangier-Tetouan-Al Hoceima coastal Region (Morocco)

Coastal zones occupy a prominent position in the sustainable management perspective of Tangier-Tetouan-Al Hoceima (TTA) region because they accommodate a majority of the region’s population and contribute to the economic wellbeing of local communities. High demographic and economic pressures associated to climatic and environmental factors increase and intensify the vulnerability of these coastal areas. The latter are severely affected by climate change implications in the course of time, which lead to damage and loss in coastal low-lying zones. In practice, the risks related to climate change, as coastal risks, are frequently addressed, and assessing coastal risk in the context of climate change is a research priority. The aim of this work is to evaluate the flooding risk of TTA coasts, taking into account the inundation level related to the conditions of extreme wave (return period of 100-year) and extreme sea level rise. We firstly defined the coastal hazard zone that corresponds to the maximum inundation level of 13 m for the whole area. We then applied a Coastal Risk Index application at the Local Scale (CRI-LS) methodology to calculate forcing, vulnerability, exposure and risk indices using nineteen physical, environmental and socioeconomical variables. Findings show that coastal hazard zone of Tangier-Tetouan-Al Hoceima region is extremely exposed to coastal forcing. More than 50% of the hazard zones indicates highly vulnerable zones and highest exposure is generally focused on the most populated urban zones. In the light of this, the coastal risk mapping shows hotspot zones in terms of climate change-related coastal risks located at Tangier Bay, south of Tangier city, Fnideq and Martil coasts, Tetouan city and north of Ksar El Kbir city. This paper corresponds to a useful support that can help policy-makers in decision-making to quietly follow coastal planning and management processes and participate in preserving coastal areas for future generations, which support the sustainable development goals of Agenda 2030 started by the United Nations with the goal of eradicating all kinds of poverty.

A novel discrete deep learning-based intelligent methodology for energy consumption classification

Energy consumption classification is one of the most widely-used approaches in the energy area that is applied in various applications such as household, commercial, urban, rural, industrial, etc. Energy consumption due to its substantial positive influence on the quality of made decisions, production and distribution management, and cost reduction, has considerable literature. In the classification literature, numerous statistical, intelligence, and hybrid methods have been developed and proposed in order to yield more accurate results. The common point of all these models is that whole of techniques follow a prevalent and repetitive procedure of learning process based on continuous distance-based cost function. While the objective function of classification is discrete. While the mismatch of the continuous cost function in classification problems with the discrete objective function causes them to be illogical or inefficient. Therefore, in this paper, a novel discrete learning process is offered to eliminate the inconsistency between the cost function and the classification objective function. The main difference between the proposed learning methodology rather than conventional versions is its cost function. In the proposed learning methodology, a mismatching function is considered as a cost function, which is dissimilar to previously developed ones, which are continuous functions based on distance, is a discrete function based on direction. In this way, in the proposed learning process, unknown parameters are discretely adjusted and at once jumped to the target. This is in contrast to conventional continuous learning algorithms in which the unknown parameters are continuously adjusted and step-by-step near the target. In this paper, deep multilayer neural networks (DMNs) have been exemplarily applied to implement the proposed learning algorithm. Although more consistency of goal and cost functions will logically has not a negative effect on the classification rate of classifiers. However, in this paper, to show the superiority of the proposed discrete deep multilayer neural network (DDMN) over conventional continuous-based classifiers, four energy consumption benchmark data sets have been employed. These data sets include the Tamilnadu Electricity Board Hourly Readings, Tetouan City Energy Consumption, Household Electric Power consumption, and steel industry Energy Consumption. Empirical results indicate that the proposed DDMN model, as pre-expected, can yield better performance in all cases than the DMN model. The DDMN model can achieve a 93.27% classification rate on average and upgrade its classic version approximately by 4.77%.

A NEW APPROACH FOR MAPPING LAND USE / LAND COVER USING GOOGLE EARTH ENGINE: A COMPARISON OF COMPOSITION IMAGES

Abstract. In view of the increase in human activities, climate change and related hazards, land use and land cover (LULC) mapping is becoming a fundamental part of the process of any development or hazard prevention project. From this perspective, we propose a new approach for mapping LULC using Machine learning algorithms by comparing the result of five composition methods based on Google Earth Engine in the city of Tetouan - Morocco. To achieve this goal, considering the Sentinel S2 L2 imageries as a source data , five datasets were derived to make the classification generating by aggregating functions (median , mean , max , min and mode). Then based on the very high resolution (VHR) satellite images provided by Google Earth comes the next step that involves selecting samples that are divided into five classes (barren land, water surface, vegetation, forest, and urban areas), which will be further split into two parts: 70% as a training data -used to feed the machine learning algorithms (support vector machine (SVM), random forest (RF) and classification and regression trees (CART))- and 30% as a testing data for evaluating the models using accuracy assessments. The results for all datasets indicate that the SVM algorithm has the highest accuracy and its performance is better than the other algorithms (RF and CART). The average overall accuracy of SVM , RF , and CART was 87.99% , 87.81% and 84.72% , respectively. Furthermore, for each algorithm, the comparison between the results of the different composites indicates that the use of the mean composite is the most suitable for LULC mapping. Finally, GEE has proven to be an effective and rapid method for LULC mapping, especially with the use of compositional imagery that can assist decision makers in future planning or risk prevention.

Modeling and forecasting air temperature in Tetouan (Morocco) using SARIMA model

Abstract The past decades have seen a growing concern to understand the impact of climate change at global and regional levels. In particular, air temperature has been considered as a key factor in climate impact studies on agricultural, ecological, environmental and economic sectors. In this study, a seasonal ARIMA model is developed through the use of the Box and Jenkins method (1970) to predict the long-term air temperature in the city of Tetouan. Indeed, over the period of 1980 to 2022 from Sania Ramel station of the city of Tetouan, the monthly mean air temperature data are used to build and verify the model. Four basic chronological steps, namely: Identification, Estimation, Validation and Prediction are established during the model development. The validity of the model is tested using the standardized residuals plots given by Box and Jenkins. After carrying out the necessary checks, the ARIMA (1,0,0)(1,1,0)[12] model proved to be the most effective for predicting future air temperature. Keywords: Air temperature, Time Series Forecasting, Box and Jenkins, Seasonal ARIMA.

Aerosols in Northern Morocco-2: Chemical Characterization and PMF Source Apportionment of Ambient PM2.5

Insufficient data on the chemical composition of PM2.5 and its emission sources in the southwestern (SW) Mediterranean area has been identified. Ambient PM2.5 samples were collected in an urban area of Tetouan city, northern Morocco. Chemical mass closure calculations and positive matrix factorization were performed for the comprehensive dataset of PM2.5 chemical analyses. Mass closure improved when multipliers (1.2 and 0.23, respectively) were used for the conversion of organic carbon (OC) and calcium ion (Ca2+) into particulate organic matter (POM) and mineral dust masses, respectively. The mass closure model performed well in this SW Mediterranean region, with a significant correlation (r2 = 0.97) obtained between gravimetrically measured and chemically determined PM2.5 mass. The one-year average concentration of PM2.5 was 17.96 μg/m3, and the major chemical components were POM (34%), secondary inorganic aerosol (SIA) (28%), and black carbon (18%), while unidentified mass was 4%. The mass concentration and most of the chemical components of PM2.5 showed clear seasonal variations, with a summer-high and winter-low pattern for SIA, dust, and BC. In the winter months, POM was the dominant component. Source apportionment analysis revealed that PM2.5 emission sources, regarding their typical tracers, were ammonium sulfate (SO42−, NH4+, K+, NO3−), road traffic and biomass burning emissions (OC, BC), fresh sea salt (Cl−, K+, NO3−), aged sea salt (Mg2+, Na+, Ca2+), and oxalate-rich (oxalate, NO3−) factors. Further, it is hoped that these findings help to improve the scientific understanding of SW Mediterranean aerosols.

Effects of meteorological droughts on water resources in Northern Morocco: Case study of the Martil watershed

Recent droughts in the Northern Morocco have highlighted the increasing vulnerability of water resources to this natural hazard, with reduced potential for drinking water supply in the city of Tetouan and its coastal area. Therefore, the main objectives of this work are: the identification of dry events that affected the Martil watershed on the one hand, and the study of the effects of meteorological droughts on water resources on the other hand. For this purpose, a meteorological-hydrological modelling framework was adopted, using standardized precipitation index (SPI) values and the volume of surface water resources recorded at the Torreta station. The results obtained show that the last decade of the 20th century and the beginning of the 21st century have seen the heaviest and most critical droughts in the study area, namely the drought of 1994-95, 1998-99, 2004-05, 2006-07, 2011-12 and 2013-14. Also, water resources have been reduced by 62% to 82% during critical dry years compared to the long-term average for the period 1970-71/2014-15. Finally, this study showed that the rainfall variability is the determining factor in the fluctuations of surface water resources at the Martil watershed because it explains 84% of their total variance.

Distribution and Ecology of Phlebotomine Sand Flies (Diptera: Psychodidae) in Endemic and Nonendemic Area of Leishmaniasis in Northern Morocco.

Northern Morocco is endemic for cutaneous and visceral leishmaniasis. Our entomological investigations aim to evaluate the risk of Leishmania transmission by determining the species composition, the density, and seasonal fluctuation of sand fly populations in endemic and nonendemic areas of leishmaniasis in Tetouan province (North-Western Morocco). Using Sticky-paper traps, 8,370 specimens were collected between May and November 2015 in two localities: peri-urban area of Tetouan city, where leishmaniasis is endemic and that of the Oued Laou village where no cases of leishmaniasis have been recorded. Six sand fly species were identified. The genus Phebotomus was represented by five species: Phlebotomus ariasi, Phlebotomus. longicuspis, Phlebotomus perniciosus, Phlebotomus kazeruni, and Phlebotomus sergenti, while the genus Sergentomyia was represented by only one species Sergentomyia minuta. Phlebotomus perniciosus was dominant in the nonendemic area (47%) while Phlebotomus sergenti was dominant in the endemic area (51%). The spatio-temporal distribution of sand fly populations is discussed according to biotic and abiotic variables. Seasonal fluctuation in sand fly density showed a bimodal pattern for the subgenus Larroussius and a unimodal pattern for the subgenus Paraphlebotomus in Tetouan city. But, in Oued Laou village, a unimodal density distribution for species of the Larroussius subgenus and a bimodal seasonal distribution for species of the subgenus Paraphlebotomus were identified. We affirm the coexistence, in the study area, of vectors of both cutaneous and visceral leishmaniasis, namely P. ariasi, P. longicuspis and P. perniciosus vectors of Leishmania infantum and P. sergenti vector of L. tropica. However, the geographic distribution, the specific abundance, and the activity reveal significant differences between endemic and nonendemic areas in the region.

Relative Humidity Forecasts in Tetouan (Morocco) with SARIMA model.

Abstract In this study, a seasonal ARIMA model is built using the Box and Jenkins method (1970) to predict the long-term relative humidity in the city of Tetouan. To this end, the monthly average relative humidity data over the period of 1990 to 2022 from the Sania Ramel station are used to build and verify the model. The methodology used in this work toward the development of the model includes five steps: exploratory analysis, model identification, parameter estimation, model validation and prediction. The validity of the model is tested by using the standardized residual plots along with the appropriate statistical tests given by Box and Jenkins. In a second validation step, the predicted values of monthly relative humidity are verified through the use of real data series. After carrying out the necessary checks, the ARIMA(2,1,1)(2,0,0)[12] model proved to be the most effective. Keywords: Time Series, Seasonal ARIMA, Relative Humidity, Forecast.

Intelligent energy management for micro-grid based on deep learning LSTM prediction model and fuzzy decision-making

In this paper, we present a new intelligent system based on multi-agent system for energy management in micro-grid with grid-connected mode and mainly based on wind energy. The main contribution of this work is to present a model for wind power prediction for the next hour using the wind speed values of previous hours. It is a model based on a deep recurrent neural network with Long Short Term Memory which takes as input a number of previous values of the wind speed and as output the wind power produced for the next hour. The proposed model is compared to a Multi-Layer Perceptron with the same inputs and output. Both models aim to find a hidden correlation between historical weather data (previous wind speeds) and the next wind power produced. Using as a case study the dataset from a real site located in Tetouan city in Morocco, experimental results show that Long Short Term Memory based prediction model yields good results and successfully achieve high accuracy compared to Multi-Layer Perceptron based model. To establish a robust energy management strategy, a fuzzy logic controller is used as decision-making tool to manage the different energy flows in the micro-grid and to control the amount of electricity delivered or taken from the distribution network in order to reduce the costs and maximize the profits.

PERFORMANCE OF MACHINE LEARNING ALGORITHMS FOR MAPPING AND FORECASTING OF FLASH FLOOD SUSCEPTIBILITY IN TETOUAN, MOROCCO

Abstract. Since the industrial revolution, the world is experiencing a huge change in its climate, which causes many imbalances such as flash floods (FF). The aim of this study is to propose a new approach for detection and forecasting of flash flood susceptibility in the city of Tetouan, Morocco. For this regard, support vector machine (SVM), logistic regression (LR), random forest (RF), Naïve Bayes (NB) and Artificial neural network (ANN) are used based on 1101 points (680 flood points and 421 non-flood points) and 9 flash-flood predictors (Elevation , Slope , Aspect , LU/LC , Stream Power Index , Plan curvature , Profile Curvature , Topographic Position Index and Topographic Wetness Index ) that were extracted from the DEM (10m resolution) and satellite imagery (Sentinel 2B) of the study area . Models were trained on 70% and tested on 30% of this dataset also they were evaluated using several metrics such as the Receiver Operating Characteristic (ROC) Curve, precision, recall, score and kappa index. The result demonstrated that RF (AUC = 0.99, Accuracy = 96%, Kappa statistics = 0.92) has the highest performance, followed by ANN (AUC = 0.98, Accuracy = 95%, Kappa statistics = 0.89) and SVM (AUC = 0.96, Accuracy = 92%, Kappa statistics = 0.80). The proposed approach is an effective tool for forecasting and predicting FF that can help reduce the severity of this disaster.

Energy consumption prediction model with deep inception residual network inspiration and LSTM

Predicting electricity consumption is not an easy task depending on many factors that affect energy consumption. Therefore, electricity utilities and governments are always searching for intelligent models to improve the accuracy of prediction and recently, deep learning becomes the most used field in prediction. In this paper, we introduce a deep learning model based on deep feedforward neural networks and Long Short-Term Memory. The deep feedforward neural networks architecture was inspired by the Inception Residual Network v2, which achieved the highest accuracy in image classification. We compared our proposed model to other recent deep learning models in two different datasets: dataset from the Distribution Network Station of Tetouan city in Morocco and dataset from the North American Utility. The proposed model achieved the smallest error of Root Mean Square Error comparing to its counterparts.

English

Cyanobacteria thrive in eutrophic freshwaters and impose a serious problem for the management of water bodies. Some Cyanobacteria species impose even a risk for public health due to the production of intracellular toxins. This study is a qualitative approach to determine the degree of toxicity and the toxicological aspect of cyanotoxins in order to setup a monitoring program for cyanobacteria blooms and the management of cyanotoxins thriving in three water bodies in Northern Morocco. Water samples were collected from three major water reservoirs/dams near the city of Tetouan (SMIR, BELMEHDI and NAKHLA). These water samples were screened for possible Cyanobacteria using specific culture media (BG13 & Z8). Three cyanobacteria species (Microcystis aeruginosa, Pseudanabaena galeata and Oscillatoria tenuis) were isolated, purified and lyophilized. Using gas chromatography coupled with mass spectrometry, nine types of microcystins were characterized namely: (MC-LR); (MC-YR); (MC-LA); (MC-FR); (MC-RF), [Mser7]MC-LR; [Dha7]MC-LR; MC-YAba; and [Mser7]MC-YR. Our results strongly recommend and urge different stakeholders to consider the various health risks potentially generated by these toxins during water use and management. In addition, this study is a contribution to raise awareness of the toxicological aspect of the cyanobacteria inhabiting the water bodies of Northern Morocco. Key words: Blue algae, gas chromatography coupled to mass spectrum, bio-toxins, water dam.

Hygienic quality, study of enterobacteria isolated from table olives and antimicrobial activity of Olea europaea L from Tetouan, Morocco

88 samples of traditional and industrial table olives were collected from various outlets in the city of Tetouan. The physical-chemical controls concerned: pH , ORP (oxydo-reducing potential) and TDE (total dry extract). The microbiological side are: total aerobic mesophilic flora ( TAMB), total coliforms (TC), Staphylococcus aureus (SA) and lactic bacteria (LB) with an identification of the most predominant enterobacteria species and their sensitivity. Also to investigate the antimicrobial activity of Olive (Olea europaea L.) .The obtained results for traditional olives far exceed food hygiene standards, especially when 29.20% of traditional samples are too loaded in TAMB and TC and 16.5% are contaminated with SA. This shows that good hygiene practices are not respected. As for industrial olives, the averages in TAMB and SA are below the thresholds set by the standards, and only 19.8% are contaminated with TC which indicates the compliance of the majority of industrial type. Black table olives are the most contaminated. The identification results of coliforms extracted from traditional olives show the predominance of Citrobacter freundii and klebsiella pneumoniae .The sensitivity test shows that 80% of Enterobacteriaceae tested were sensitive to all antibiotics.The result showed that the ethanolic extrats was active against all bacteria.

A comparative study of thermal and acoustical insulations used for residential buildings

The aim of this paper is to study the thermal and acoustical insulation of four different buildings’ envelopes mostly used for residential buildings in Tetouan city. The thermal and the acoustical models were investigated during summer season, while a linear nonmonochromatic source of sound has been placed 1m away from the exterior side of each wall and it is emitted a low-frequency sound waves with a velocity of 200 m/s. The study shows that the double layers of hollow clay brick wall with a medium of air gap presents a good thermal resistance and soundproofing compared to the other walls under study, due to the insulation by air gap with a thickness of 0.06m. Moreover, the homogenous and thick walls are simultaneously performant as thermal and sound insulators.

One month-ahead forecasting of mean daily global solar radiation using time series models

Accurate forecasting’s of solar radiation are crucial to quantifying future solar power production or integration. The integration of solar energy into the electricity grid has become predominant among researchers and scientists due to the current demand for energy as well as the depletion of fossil fuel reserves and environmental impacts. This paper aims to introduce a method for multi month-ahead forecasting (one, two and three month ahead) of monthly mean daily global solar radiation time series and forecasting of a large scale solar radiation data driven. ARMA and ARIMA models are used to predict the coming value of the global solar radiation time series. Both models are applied to stationary and non-stationary time series of the solar irradiation data. Taken into account the advantage of ARMA and ARIMA models, the two models are compared in terms of goodness-of-fit value determining by the AIC and BIC criterion. As a result, the ARMA (2, 1) and ARIMA (0, 2, 1) are selected as optimal models due to the minimize values of AIC and BIC criterion. Based on the statistical error value, ARIMA (0, 2, 1) models give good accuracy in three period ahead forecasting consecutive in comparison with ARMA (2, 1). The lower value of the optimum model indicates that ARIMA (0, 2,1) is more suitable to forecast monthly mean daily global solar radiation for Tetouan city and may be for other locations of similar weather conditions.

Levenberg-Marquardt Training Function using on MLP, RNN and Elman Neural Network to Optimize Hourly Forecasting in Tetouan City (Northern Morocco)

Levenberg-Marquardt Training Function using on MLP, RNN and Elman Neural Network to Optimize Hourly Forecasting in Tetouan City (Northern Morocco)