ISSN Versión Impresa 1816-0719  
ISSN Versión en línea 1994-9073  
ISSN Versión CD ROM 1994-9081  
e Biologist (Lima), 2025, vol. 23 (2), 271-281  
o
VOL. 23.  
N
2, JUL-DIC 2025  
e Biologist (Lima)  
ORIGINAL ARTICLE / ARTÍCULO ORIGINAL  
SYSTEM WITH MOBILE AND INTELLIGENT MONITOR FOR THE STUDY  
OF ENVIRONMENTAL CARE  
SISTEMA CON MONITOR MÓVIL E INTELIGENTE PARA EL ESTUDIO DEL  
CUIDADO AMBIENTAL  
J. Alan Calderón-C.1,2,3*; Eliseo Benjamín Barriga-G.1, Julio César Tafur-S.1, Rusber A. Risco-O.2,5,  
L. Walter Utrilla-M.4, Robert W. Castillo-A.2,6, Diego Saldaña-V.2 & Facundo Gómez-S.2  
1
2
3
4
5
6
Applied Physics, Institute for Physics, Technical University of Ilmenau, Ilmenau 98693, Germany.  
Pontificia Universidad Católica del Perú, Mechatronic Master Program and Energy Laboratory, Lima, Perú.  
Aplicaciones Avanzadas en Sistema Mecatrónicos JACH S.A.C., Perú.  
Universidad Nacional San Antonio Abad del Cusco, Cusco, Perú.  
Universidad Nacional del Santa, Áncash, Perú.  
Universidad Nacional del Callao, UNC, Perú.  
* Corresponding and main author: alan.calderon@pucp.edu.pe  
J. Alan Calderón-C.: https://orcid.org/0000-0002-6486-5105  
Eliseo Benjamín Barriga-G.: https://orcid.org/0000-0002-7781-6177  
Julio César Tafur-S.: https://orcid.org/0000-0003-3415-1969  
Rusber A. Risco-O.: https://orcid.org/0000-0003-0194-169X  
L. Walter Utrilla-M.: https://orcid.org/0000-0001-8645-7324  
Robert W. Castillo-A.: https://orcid.org/0009-0002-5258-3319  
Diego Saldaña-V.: https://orcid.org/0000-0003-2058-7470  
Facundo Gómez-S.: https://orcid.org/0009-0002-2109-9399  
ABSTRACT  
is paper describes and proposes different engineering techniques for designing an intelligent system focused on  
environmental protection, based on stationary and mobile subsystems for monitoring physical variables. e variables  
selected for monitoring were water level, flow rate, pH, vibration, and surface water temperature, which are transmitted  
via radio frequency to an external monitoring center and a mobile unit. is system was designed using an optimized  
analysis of polynomial models to correlate each measured physical variable. Furthermore, this research also integrates the  
measurement subsystems through wireless data transmission between the sensors and the stationary and mobile receiving  
centers. is allows for the consolidation of all measured data to obtain a comprehensive view of the main target, such  
as a lake or river. Such bodies of water can cause serious damage in cities when adequate prevention measures are not in  
place, as occurs during the El Niño Southern Oscillation (ENSO) phenomenon in Peru. erefore, this research seeks  
Este artículo es publicado por la revista e Biologist (Lima) de la Facultad de Ciencias Naturales y Matemática, Universidad Nacional Federico Villarreal,  
Lima, Perú. Este es un artículo de acceso abierto, distribuido bajo los términos de la licencia Creative Commons Atribución 4.0 Internacional (CC BY  
4.0) [https:// creativecommons.org/licenses/by/4.0/deed.es] que permite el uso, distribución y reproducción en cualquier medio, siempre que la obra  
original sea debidamente citada de su fuente original.  
DOI: https://doi.org/10.62430/rtb20252321889  
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to contribute to prevention efforts through the described and designed system. A mechatronic system was designed,  
consisting of a drone as a mobile station data analyzer. e data collection point for the proposed monitoring center could  
be located at a site chosen by the community that would use the proposed system. For the experiments, prototype designs  
were prepared to collect the data measured by the sensors, most of which relied on nanostructures attached to the drone to  
measure temperature, pH, and water level. is data was processed by the drone’s controller and the monitoring system.  
e data was collected from the Rímac River in Peru between July 2024 and September 2025, with the expectation that  
the proposed research will be useful to the communities during future ENSO events.  
Keywords: Drones – ENSO – Environmental monitoring – Smart sensors – Wireless communication  
RESUMEN  
En este trabajo se describen y proponen diferentes técnicas de ingeniería para diseñar un sistema inteligente orientado  
al cuidado del medio ambiente, basado en subsistemas estacionarios y móviles para el monitoreo de variables físicas. Las  
variables seleccionadas para ser monitoreadas fueron el nivel de agua, caudal, pH, vibración y temperatura superficial  
del agua, las cuales se transmiten por Radio Frecuencia a un centro de monitoreo externo y a uno móvil. Este sistema  
fue diseñado mediante un análisis optimizado de modelos polinomiales para correlacionar cada variable física medida.  
Asimismo, esta investigación también integra los subsistemas de medición mediante transmisión inalámbrica de datos entre  
los sensores y los centros receptores estacionarios y móviles. Esto permite consolidar todos los datos medidos para obtener  
una visión panorámica del objetivo principal, como un lago o un río. Dichos cuerpos de agua pueden causar graves daños  
en las ciudades cuando no hay una prevención adecuada, como ocurre durante el fenómeno “El Niño Southern Oscillation  
(ENSO)” en el Perú. Por lo tanto, esta investigación busca contribuir a las tareas de prevención mediante el sistema descrito  
y diseñado. Se diseñó un sistema mecatrónico compuesto por un dron -analizador de datos de estación móvil-. Los datos  
medidos que se proponen para el centro de monitoreo podría ubicarse en un lugar decidido por la comunidad que podría  
utilizar este sistema propuesto. Para los experimentos, se prepararon diseños de prototipos para obtener los datos medidos  
por los sensores, la mayoría de los cuales se basaron en nanoestructuras fijadas al dron para medir la temperatura, el pH  
y el nivel del agua. Estos datos fueron procesados por el controlador del dron, así como por el sistema de monitoreo. Los  
datos medidos se obtuvieron del río Rímac, Perú, durante julio de 2024 a septiembre de 2025, con la expectativa de que  
la investigación propuesta pueda ser útil para las comunidades durante el futuro período del ENSO.  
Palabras claves: Comunicación Inalámbrica – DRONs – Energía solar – ENSO – Monitoreo Ambiental – Sensores  
inteligentes  
INTRODUCTION  
control system (Aström & Hägglund, 2012; Gonzales  
& Sánchez, 2012), which integrates every response from  
the other 2 DRON, it means the system is defined by 3  
DRON due to achieve a 3 dimension reconstruction for  
every physical variable over the selected surface by the 3  
DRON (smart monitoring system) (Corsi, 2014).  
In this proposed article are analyzed the applications of  
smart sensors according to be used in the measurement  
of physical variables that give information of the effect of  
natural disasters (such as ENSO). In this context, there  
are analyzed the temperature of the external conditions,  
flow and water level of the rivers and lakes. e described  
measurement is organized by sensors stored in DRON, also  
the mathematical analysis, as based on advanced polynomial  
modeling (Calderón et al., 2022), is a good advantage  
due to achieve optimal measurements consequently the  
correlation of the designed mathematical model with the  
identified system based on the offline identified parameters,  
this task needed more time for the execution of the main  
e following figure 1 shows a building used by some  
people to live, notwithstanding, there is a big disadvantage  
in its closeness to the river, which usually increase the  
water level when it is ENSO time (Lozada-Escorza, 2021).  
erefore, there is quite dangerous to not have a preventive  
system in order to care homes in from ENSO consequences,  
such as it is proposed in this research. (Farfán et al., 2024).  
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Figure 1. A building close to a river in Peru, without ENSO prevention.  
Moreover, every one of the DRONE is integrated by  
sensors based on nanostructures (DRONES were simple  
electromechanical systems adapted for the presented research),  
which help to get the physical variables measurements  
(temperature, water level, position) in short response time  
and high robustness (depending the operating range of work)  
(Bora, 2018; Johnson et al., 2021). Hence, this advantage to  
measure the physical variables over the river/lake surface in  
short response time let to execute intricate algorithms (based  
on adaptive Modulating Functions) according to achieve the  
report of the temperature, pressure, humidity, water level  
and flow over every chosen surface by the smart monitoring  
system (Person, 1995; Stetter, 2009; Hunter et al., 2010).  
e internal communication by every DRON is given by  
a combination of radio frequency (RF) and infrared (IR)  
signals, as well as all the matrix of the measured physical  
variables are sent by wireless to an external user, which i  
s
depicted by the fig. 2.  
Figure 2. Representation of the smart monitoring system.  
In the following paragraphs are described equations to  
obtain the optimal solutions for the sensors measurements  
data analysis, in spite of the system is multivariable. It  
is proposed a geometric surface by the function “f ” in  
dependence on the variables “x1” and “x2” that is given by  
the equation (1) (Gonzales & Sánchez, 2012).  
As well as, the equation (2) gives information of the  
bounding section “g” in dependence on the variables “x1”  
and “x2”  
푔(푥1, 푥2) = 0  
(2)  
It is proposed a geometric surface because of the intersections  
between “f ” and “g”, hence, it was necessary the derivatives  
in dependence on “x1” and “x2” for the function “f ”, which  
is described by the equation (3).  
푓(푥1, 푥2) = 0  
(1)  
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between sender and receptor. Nevertheless, in the proposed  
research, it was analyzed the interaction between every  
DRON with the user, for this reason it was studied the  
measurement as data transmitted, which also gave the  
outlook that this proposed research could be used for long  
distance communication tasks.  
(
)
(
)
휕푓 푥1, 푥2  
휕푓 푥1, 푥2  
(
)
푑푓 푥1, 푥2  
=
푑푥1 +  
푑푥2  
(3)  
휕푥1  
휕푥2  
By other side, for the function “g”, it is proposed a  
geometric surface because of the intersections between “f ”  
and “g”, thus, it was necessary the derivatives in dependence  
on “x1” and “x2”, which is described by the equation (4).  
e following equation is a solution for a wave equation  
(13), an electromagnetic wave equation “훹” under  
dependence of the position “r” and time “t”, a general  
amplitude “A”, frequency “f” and phase “훹”. (Feynman  
et al., 1962).  
(
)
(
)
휕푔 푥1, 푥2  
휕푔 푥1, 푥2  
(4)  
(
)
푑푔 푥1, 푥2  
=
푑푥1 +  
푑푥2  
휕푥1  
휕푥2  
From equation (3) and equation (4) are obtained the  
equations (5) and (6), which is based on the boundary  
conditions. (Landau & Lifshitz, 1959)  
+
( )  
휓 푟, 푡 = 퐴 sin (푤푡 훼)  
(13)  
−
(
)
푑푓 푥1, 푥2 = 0  
(5)  
(6)  
Even though for the described solution, the frequency  
“f” can be associated with the speed of light “C” and  
wavelength “λ” of the wave “훹”, which is described by  
the equation (14).  
(
)
푑푔 푥1, 푥2 = 0  
erefore, it was obtained the equation (7).  
퐶
푓 =  
(14)  
휆
(
)
(
)
휕푓 푥1, 푥2  
휕푓 푥1, 푥2  
(7)  
푑푥1 = −  
푑푥2  
휕푥1  
휕푥2  
As well as the equation (15), proportionate information  
of the general model for the speed of light that helped to  
generalize the equation of the wave data transmission, in  
which “C ” is the initial speed of light value, “C” is the  
o
speed of light under dependence of its initial value and the  
source speed “v” (Magueijo, 2003).  
And the equation (8).  
(
)
(
)
휕푔 푥1, 푥2  
휕푔 푥1, 푥2  
푑푥1 = −  
푑푥2  
(8)  
휕푥1  
휕푥2  
From equation (7) and equation (8), it was organized the  
equation (9):  
푣2  
퐶2  
(
휕푓 푥1, 푥2  
)
(
휕푔 푥1, 푥2  
)
(
휕푓 푥1, 푥2  
)
(
휕푔 푥1, 푥2  
)
퐶0 = 퐶 (1 +  
)
(15)  
−
푑푥1  
푑푥2 = −  
푑푥2  
푑푥1  
(9)  
휕푥1  
휕푥2  
휕푥2  
휕푥1  
By energy conservation, in the equation (16) can be  
proposed the equilibrium between the kinetic energy of  
the source at speed “v”, mass “m”, with its gravity energy.  
For which, the gravity constant is “G”, “M” is the mass  
of the gravitation interactive body for the wave source,  
as well as the position between the wave source with the  
gravitation interaction body is “r” (Medina, 2009).  
us, it was achieved the equation (10)  
(
)
휕푓 푥1, 푥2  
휕푥2  
(
휕푓 푥1, 푥2  
)
(
휕푔 푥1, 푥2  
)
−
= 0  
(10)  
(
)
휕푔 푥1, 푥2  
휕푥2  
휕푥1  
휕푥1  
As well as, by the same procedure that was obtained the  
equation (10), it was achieved the equation (11).  
(
)
휕푓 푥1, 푥2  
휕푥1  
1
2
퐺푚ꢀ  
푟
푚푣2  
=
(16)  
(
휕푓 푥1, 푥2  
)
(
휕푔 푥1, 푥2  
)
0 =  
−
(11)  
(
)
휕푔 푥1, 푥2  
휕푥1  
휕푥2  
휕푥2  
Hence, the minimal speed to escape from the gravitation  
interactive body is given by the equation (17), as a reduction  
of the equation (16) (Medina, 2010).  
erefore, it must be in the point (x*, y*) for (x, y),  
represented by “λ” in the equation (12).  
(
)
(
)
휕푓 푥1, 푥2  
휕푥1  
휕푓 푥1, 푥2  
휕푥2  
퐺푀  
푣2 = 2  
(17)  
=
= 휆  
(12)  
(
)
(
)
푟
휕푔 푥1, 푥2  
휕푥1  
휕푔 푥1, 푥2  
휕푥2  
It means that replacing the equation (17) in the equation  
(15), it was obtained the equation (18).  
Consequently, it was possible to identify the optimal  
sections of the physical variables measurements by wireless  
sensors of the DRON.  
퐺푀  
퐶0 = 퐶 (1 + 2  
)
(18)  
푟퐶2  
By other side, it is quite necessary to get understanding of  
the wireless data transmission that helps to interchange the  
measured variables, such as the information transmitted  
From the equations (14) and (18) replaced in the equation  
(13), it was obtained the equation (19).  
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System with mobile and intelligent monitor for the study of environmental care  
erefore, replacing equation (26) in (25)  
2휋  
휆
퐶0  
+
−
(
)
휓 푟, 푡 = 퐴 sin (  
푡
훼)  
(19)  
2
푣2  
퐶2  
푑
푚 푑ꢃ (푋0푒푖ꢂ ꢃ) + 푚푤2(푋0푒푖ꢂ ꢃ) = 푞퐸0푒푖ꢂꢃ  
(27)  
0
0
1 +  
2
Or by another hand, the equation (19) can be proposed  
through the equation (20).  
It was obtained the equation (28)  
ꢇꢈꢉ  
ꢄꢅ  
ꢆ
2
ꢊ(ꢂ −ꢂ )ꢆ  
0
푋0  
=
(28)  
2
ꢇꢈ  
ꢉ
0
0
2휋  
휆
퐶0  
+
−
(
)
휓 푟, 푡 = 퐴 sin (  
푡
훼)  
(20)  
퐺푀  
푟퐶2  
Replacing (28) in (26), it was obtained the equation (29)  
1 + 2  
ꢇꢈꢉ  
ꢄꢅ  
ꢆ
2
ꢊ(ꢂ −ꢂ  
0
erefore, by the equation (20) it was possible to generalize the  
communication signal, which can be under relativistic effects  
or long-distance separations among emitter and receivers.  
푋 =  
(29)  
2
0
)
is article aims to design and develop an environmental  
monitoring system based on drones and the use of advanced  
sensors, with a focus on measuring physical and chemical  
variables in bodies of water.  
e equation (21) shows the intensity “I”, and “Io” as its  
initial value, “x” is the wavelength, and “α” is the light  
absorbance coefficient.  
퐼 = 퐼0푒−ꢀꢁ  
(21)  
e electrical field intensity “E”, its initial value “Eo”,  
frequency “w” in the time domain “t”.  
MATERIALS AND METHODS  
Hence, the described equations above give the main reference  
to get understanding of the light absorbance of the designed  
solar cells, which also improve the optimal power transmission  
to the main designed mechanical system. ereby, there were  
prepared own transducers based on amorphous nanostructures  
due to get short response time and high robustness in the  
physical variable’s measurements (Ku et al., 2023).  
퐸 = 퐸0푒푖ꢂꢃ  
(22)  
e electrical force depending on the electrical charge “q”.  
퐹 = 푞퐸 (23)  
By Newton Second Law (Person, 1995)  
2
푑
2
( )  
푚 푑ꢃ2 푋 푡 + 푚푤 푋(푡) = 퐹  
(24)  
e figure 3 shows one of the prepared samples for the  
designed transducers based on nanoholes of Anodic  
Aluminum Oxide (AAO), there were deposited structures  
of Titanium Dioxide (TiO2) owing to prepare nanowires  
of this on the range 1000 nm to 10 000 nm, even though  
the prepared sample by anodization and electro chemical  
deposition contained more nanostructures in the range 8 000  
nm to 10 000 nm (Keller et al., 1953; Al-Kaysi et al., 2009).  
Replacing previous equations (22) and (23) in (24)  
2
푑
2
푖ꢂꢃ  
( )  
푚 푑ꢃ2 푋 푡 + 푚푤 푋(푡) = 푞퐸0푒  
(25)  
A proposed solution is given by the equation (26)  
ꢃ
푋 = 푋0푒푖ꢂ  
(26)  
0
Figure 3. Representation of the AAO nanoholes prepared to receive TiO2 as part of a real sample.  
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After to prepare the samples for the transducers, there  
were organized the position sensors, the water level sensor,  
the small solar cells in order to integrate to the pH sensor  
(from ARDUINO company), charger batteries, and the  
RF measurement data transmitter around a small DRON  
(Figure 4).  
Figure 4. DRON used to integrate the sensors.  
It was used, also, another DRON, which had the task to  
measure its position, as well as the water level of the river.  
In fact, the showed DRON had a better performance for  
the experimental tests outside the laboratory owing to it  
had robustness in front intense windy time (Figure 5).  
Figure 5. Aerial measurement system with robustness.  
Ethic aspects: is article has not ethical conflicts in the  
proposed research, which was cited every bibliographic  
reference for every analysis described.  
furthermore, this is restructured by the advanced sensors  
and a microcontroller due to prepare the main data matrix  
of the measurements (Andrade-Gutiérrez, 2022).  
e integrated system (DRON, the advanced sensors,  
the solar energy subsystem and the RF communication  
subsystem) starts the monitoring task activating the supplier  
energy charger subsystem (based in small solar cells based  
in nanostructures), in spite of the system keeps external  
chargeable system, but the DRON solar energy subsystem  
stores energy to supply on the microcontroller and advanced  
sensors while the DRON uses the energy from the main  
RESULTS AND DISCUSSION  
e described materials used for the experimental results  
helped to prepare position and water level smart sensor,  
which complement to the ARDUINO sensors in order  
to give the pH of the river. e matrix of data is analyzed  
to be sent to external users by RF from the DRON,  
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battery. e DRON activates the position sensor and the  
water level sensor while the DRON is over the river, as well  
as the pH sensor by periods (it happened when the DRON  
comes near the river according to keep in contact the pH  
sensor with the water), the position and the water level  
sensor work together owing to get a spatial recognition of  
the trajectory and place to measure the required physical  
variables.  
the filtered data that can help for users, which get the  
information to interpreted the presence of ENSO (Intrieri  
et al., 2012; Yovera-Puican, 2023).  
e following figure 6 shows the energy absorbance by the  
solar panels to store electrical energy on the batteries of the  
designed system, which get energy from the solar panels  
prepared by nanostructures (Lei et al., 2007). e quantity  
of saved energy got a steady state with approximated 0.4  
W, the used balance was among its energy conversion with  
its consume by the designed sensors during 30 minutes  
(Unzicker & Presuss, 2015).  
e communication system of the DRON operates with  
the measurement data achieved from every sensor as well  
as execute an advanced predictive/adaptive algorithm for  
Figure 6. Solar energy absorbance.  
e following figure 7 shows the position and water  
level measured by the DRONE, which was achieved by  
experiments around 20 meters up of water trajectory  
(along Rimac river, Santa Clara, Perú) during 20 minutes  
approximately as well as this measured data was sent by  
RF to an external user at 100 meters of distance (this value  
was evaluated previously by laboratory procedures). e  
blue color curve provides information on the water level  
measured from the drone in centimeters, while the red  
color curve provides information on the drone’s position  
in meters.  
Series2  
Series1  
Position and Water level  
12  
10  
8
6
4
2
0
0
5
10  
15  
20  
Time (minutes)  
Figure 7. Measured data by the designed system.  
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e figure 8 shows the temperature measured over the  
river surface by non-contact heat transmission. ese  
measurement results stem from the mathematical analysis  
discussed in previous chapters and are robust, based on  
the correlation between theoretical and experimental  
mathematical modeling. e advanced sensors on the  
DRONE, which use nanostructures, provide a short  
response time. is response time is much shorter than  
that of other electromechanical sensors, ensuring optimal  
data is gathered.  
Figure 8. Temperature of the river surface.  
e presented research addresses important issues related to  
the designed DRONE for monitoring applications, which  
arise from environmental tasks. e figure 9 illustrates  
the designed DRONE scheme for monitoring physical  
variables, as described in the preceding paragraphs. e  
DRONE flies along a river and avoids crossing a swirl. Its  
automatic response finds an optimal trajectory to leave  
the swirl after measuring environmental physical variables,  
for which the DRONE was designed and detailed in this  
article.  
Figure 9. Scheme of the DRONE in activity.  
In fact, the DRONE has the possibility to fly by its own  
controlled trayectory (autopilot), as well as figure 10  
illustrates the road from L1 to L3 that can be a chosen  
solution by the autopilot of the designed DRONE. is  
proposal can be optimal due to its return path is a half  
circumference, which is possible to get while the rust  
force “E” is perpendicular to the aerodynamic force  
“Fa” (geometrical property), meaning that keeping “b”  
at 90 degrees warrants the half circumference trajectory  
according to return from L1 to L3. However, while the  
swirl increase in intensity due to height, it is verifiable  
that the optimal angle between the plane of the path L1  
to L2 must be 45 degrees over the plane of the road L1 to  
L3. erefore, the proposed DRONE by its autopilot is a  
consequence of the algorithm designed by the mathematical  
analysis described in the presented article; furthermore,  
the advanced instrumentation (such as, for example, the  
sensors based on nanostructures) helped to get an optimal  
system that can be useful for communities in response to  
El Niño phenomena (Velásquez et al., 2024).  
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e Biologist Vol. 23, N 2, jul - dec 2025  
System with mobile and intelligent monitor for the study of environmental care  
Figure 10. Scheme of the DRONE in activity (geometrical interpretation).  
ACKNOWLEDGEMENT  
Authors contribution: CREdiT (Contributor Roles  
Taxonomy)  
It is expressed gratitude to colleagues from the nanostructures  
researching group of the Technische Universität Ilmenau,  
TUI, Deutschland, owing to their shared teachings during  
the posgrade time. It is expressed special thanks for Genaro  
Chauca Jimenez and Luisa Cordova Sallo due to their  
financial support to the development of the proposed  
article. It is expressed warm thankful to Miguel A. Badillo  
B. (He also helped to improve the format of figures), Bryan  
C. Bastidas R., Michael G. Ramirez M., César F. Pinglo  
A., Brandon A. Polo V., and Jose Espettia due to their  
support in the feedback analysis of the article, as well as  
the development of some prototypes for the experiments.  
It is expressed special thankful for the companies  
“MERQUITEX S.A.C”. and “PROYINOX S.A.C.” owing  
to their support for the development prototypes, which  
were quite important to validate the theoretical models also  
discussed in this proposed research. It is expressed special  
thankful for the companies “OPEN 3D, LABORATORIO  
DE MANUFACTURA DIGITAL” and “GICA E. I. R. L.”  
because of their support for the development prototypes,  
which was based in the support to prepare the experimental  
setup. It is expressed special grateful for the Laboratory of  
Design of the PUCP: Applied Mechanics, Oleo-hydraulics  
and Pneumatics, because of proportioning part of the place  
to evaluate part of the experimental analysis described in  
the presented research.  
JACC = Jesús Alan Calderón-Chavarri  
EBBG = Eliseo Benjamín Barriga-Gamarra  
JCTS = Julio César Tafur-Sotelo  
RARO = Rusber A. Risco-O.  
LWUM = L. Walter Utrilla-M.  
RWCA = Roberto W. Castillo-A.  
DSV = Diego Saldaña-V.  
FGS = Facundo Gómez-S.  
Conceptualization: JACC  
Data curation: JACC, LWUM, RARO, DSV  
Formal Analysis: JACC, EBBG, LWUM, DSV, RARO, FGS  
Funding acquisition: JACC, JCTS, LWUM, DSV, RWCA  
Investigation: JACC, EBBG, JCTS, LWUM, RARO, DSV  
Methodology: JACC, EBBG, LWUM, DSV, RWCA, FGS  
Project administration: JACC, EBBG, LWUM, RWCA, FGS  
Resources: JACC, JCTS, LWUM, RARO, DSV, RWCA  
Software: JACC  
Supervision: JACC, JCTS, LWUM, RARO, RWCA  
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Calderón et al.  
Validation: JACC, EBBG, LWUM, RARO, DSV, FGS  
Visualization: JACC, LWUM, RARO, RWCA, FGS  
Writing – original draft: JACC  
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(2012). Design and implementation of a landslide  
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