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Using artificial neural networks to predict the reference evapotranspiration

Abo El-Magd Amal, Baraka Shaimaa M., Eid Samir F.M.

Journal of Water and Land Development

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2023

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no. 57

1--8

EN

Artificial neural network models (ANNs) were used in this study to predict reference evapotranspiration (ETo) using climatic data from the meteorological station at the test station in Kafr El-Sheikh Governorate as inputs and reference evaporation values computed using the Penman-Monteith (PM) equation. These datasets were used to train and test seven different ANN models that included different combinations of the five diurnal meteorological variables used in this study, namely, maximum and minimum air temperature (Tmax and Tmin ), dew point temperature (Tdw), wind speed (u), and precipitation (P), how well artificial neural networks could predict ETo values. A feed-forward multi-layer artificial neural network was used as the optimization algorithm. Using the tansig transfer function, the final architected has a 6-5-1 structure with 6 neurons in the input layer, 5 neurons in the hidden layer, and 1 neuron in the output layer that corresponds to the reference evapotranspiration. The root mean square error (RMSE) of 0.1295 mm∙day -1 and the correlation coefficient (r) of 0.996 are estimated by artificial neural network ETo models. When fewer inputs are used, ETo values are affected. When three separate variables were employed, the RMSE test values were 0.379 and 0.411 mm∙day -1 and r values of 0.971 and 0.966, respectively, and when two input variables were used, the RMSE test was 0.595 mm∙day -1 and the r of 0.927. The study found that including the time indicator as an input to all groups increases the prediction of ETo values significantly, and that including the rain factor has no effect on network performance. Then, using the Penman-Monteith method to estimate the missing variables by using the ETo calculator the normalised root mean squared error (NRMSE) reached about 30% to predict ETo if all data except temperature is calculated, while the NRMSE reached about of 13.6% when used ANN to predict ETo using variables of temperature only.

2

Contribution of GIS in the Mapping, Interpolation and Modeling of Isohypses of the Pliocene Aquifuge of the Free Water Table of the Fez-Meknes Basin

El Fakir Rabia, Mili El Mostafa

Ecological Engineering & Environmental Technology

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2022

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Vol. 23, iss. 5

272--284

EN

In order to model the water flow of the free Quaternary aquifer of the Fez-Meknes basin, it is essential to determine the precise geometric limits of the aquifer. Indeed, the characterization and representation of the underground structure of the Miocene marl top which forms the aquiclude of the aquifer, constitutes the fundamental step to study and understand its influence on the groundwater flow. This study is facilitated by the available data, which allow to represent the underground formations on isohypses maps. The data base is formed by reconnaissance drillings, and the extraction of marl altitudes from previous geological works. During this work the generation of the marl top elevation map was based on the test of four interpolation methods, which correspond to : Kriging, IDW method, Natural Neighbors, and Topo to Raster, in order to choose the most reliable and best suited to the study area. On the one hand, the calculation of the conformity index between the values measured in the field and the estimated values for each method was successively (0.9796, 0.9848, 0.9814, 0.9842). On the other hand the values of the root mean square error (RMSE) were successively (13.59, 7.42, 21.27, 14.01). The comparison of these results allowed us to choose the IDW interpolation as the most reliable and suitable to interpolate the top of the aquiclude of the free water table of the Fez Meknes basin with a compliance index the highest and a RMSE the lowest compared to other methods.

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Performance evaluation of satellite precipitation estimation with ground monitoring stations over southern Himalayas in Bhutan

Adhikari Kirtan, Choden Yeshi, Cheki Tshering, Gurung Lily, Denka Tshering, Gupta Vivek

Acta Geophysica

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2020

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Vol. 68, no. 3

933--943

EN

High-resolution remote sensing-based hydro-meteorological data products are being increasingly used for various scientifc studies throughout the world. As such, it is important to evaluate the quality of the data retrieved by the means of remotesensing especially for the regions characterized by the drastic variation of topography such as the Himalayas. This work focuses on the comparison and evaluation of the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA–3B43v7) with point-based ground observations recorded by Department of Hydro-Met Services (DHMS) in Bhutan. For this study, 32 rain gauge stations over Southern Himalayas in Bhutan (SHB) were selected and precipitation data for a period of 19 years (from 1998 to 2017) were compared with the TRMM precipitation product. The comparison revealed that the coefcient of correlation between satellite data and ground observation is statistically signifcant at a 95% confdence level. Furthermore, the coefcient of correlation is near unity in some stations and an average of 0.814 over the entire SHB region for 19 years. This fnding imparts that the TRMM can capture the rainfall trend over the SHB. As per the fndings, the average root-mean-square error was 219.1 mm per month considering the entire duration and 344.7 mm per month considering only the rainy season. Similarly, the average Bias was computed at 0.27 for all seasons and 0.32 for the rainy season, indicating TRMM underestimates the precipitation over SHB. The satellite estimate provides a piece of good information on the distribution of rainfall over the SHB. Nevertheless, it is still advisable to correct the bias of the satellite product, as the relative root-mean-square error is larger than 50% for 96% of the evaluated stations.

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Ocena dokładności położenia szczegółów sytuacyjnych pozyskanych z map numerycznych

Dąbrowski W., Doskocz A.

Technical Sciences. Supplement / University of Warmia and Mazury in Olsztyn

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2005

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suppl. 2

309--320

PL

Przedstawiono ocenę dokładności położenia szczegółów sytuacyjnych I grupy dokładnościowej, pozyskanych z wielkoskalowych map numerycznych. Rozpatrywano cztery metody pozyskiwania danych numerycznych: nowy pomiar tachimetrem elektronicznym, przeliczenie wyników wcześniejszych pomiarów bezpośrednich (ortogonalnych i biegunowych), manualną wektoryzację rastrowego obrazu ortofotomapy oraz przetworzenie graficzno-numeryczne map analogowych. Badania wykonano na podstawie trzech rodzajów szczegółów sytuacyjnych: punktów załamania konturu budynków (oznaczonych literą B), punktów granicznych (oznaczonych literą G) oraz punktów armatury uzbrojenia naziemnego (oznaczonych literą U). Przeprowadzone badania wykazały istotne ograniczenia (wynikające z tytułu dokładności położenia szczegółów) w możliwościach wykorzystania cyfrowych danych sytuacyjnych pozyskanych różnymi metodami. Ponadto uzyskane wyniki potwierdziły konieczność wykonywania ocen dokładności map numerycznych zrealizowanych na podstawie różnych i niejednorodnych danych źródłowych.

EN

The paper presents estimation positional accuracy of geometric well-defined points (geometric feature points of 1st class of accuracy) acquired from large-scale digital maps. The four methods of producing digital map data was analyzed: new total station survey, entry earlier field survey results (linear and polar), manual vectorisation raster picture of orthophotomap and graphic-digital processing of analogue maps. The examination performed by three kinds of geometric features: corner points of buildings contour (marked by letter B), boundary points of parcels (marked by letter G) and points of technical utilities (marked by letter U). Carried out investigations showed essential limitations (consequential with title positional accuracy of features) in practical usefulness of geometric digital map data produced with different methods. Obtained results confirmed that there is a necessity of carrying out estimations of the accuracy of digital maps made from different and heterogeneous source data.