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Smart-Water-Analytics

LTSM implementation for practical solution - The goal is to create a mathematical model, for each category of waterbody (acquifers, water springs, river, lake) to predict the amount of water in each unique waterbody for a set time interval, based on (noisy) data.

Data Description:

This competition uses nine different datasets, completely independent and not linked to each other. Each dataset can represent a different kind of waterbody. As each waterbody is different from the other, the related features as well are different from each other. So, if for instance we consider a water spring we notice that its features are different from the lake’s one. This is correct and reflects the behavior and characteristics of each waterbody. The Acea Group deals with four different type of waterbodies: water spring (for which three datasets are provided), lake (for which a dataset is provided), river (for which a dataset is provided) and aquifers (for which four datasets are provided).

Let’s see how these nine waterbodies differ from each other.

Waterbody: Auser

Type: Aquifer

Description: This waterbody consists of two subsystems, called NORTH and SOUTH, where the former partly influences the behavior of the latter. Indeed, the north subsystem is a water table (or unconfined) aquifer while the south subsystem is an artesian (or confined) groundwater.

The levels of the NORTH sector are represented by the values of the SAL, PAG, CoS and DIEC wells, while the levels of the SOUTH sector by the LT2 well.

Waterbody: Petrignano

Type: Aquifer

Description: The wells field of the alluvial plain between Ospedalicchio di Bastia Umbra and Petrignano is fed by three underground aquifers separated by low permeability septa. The aquifer can be considered a water table groundwater and is also fed by the Chiascio river. The groundwater levels are influenced by the following parameters: rainfall, depth to groundwater, temperatures and drainage volumes, level of the Chiascio river.

Waterbody: Doganella

Type: Aquifer

Description: The wells field Doganella is fed by two underground aquifers not fed by rivers or lakes but fed by meteoric infiltration. The upper aquifer is a water table with a thickness of about 30m. The lower aquifer is a semi-confined artesian aquifer with a thickness of 50m and is located inside lavas and tufa products. These aquifers are accessed through wells called Well 1, ..., Well 9. Approximately 80% of the drainage volumes come from the artesian aquifer. The aquifer levels are influenced by the following parameters: rainfall, humidity, subsoil, temperatures and drainage volumes.

Waterbody: Luco

Type: Aquifer

Description: The Luco wells field is fed by an underground aquifer. This aquifer not fed by rivers or lakes but by meteoric infiltration at the extremes of the impermeable sedimentary layers. Such aquifer is accessed through wells called Well 1, Well 3 and Well 4 and is influenced by the following parameters: rainfall, depth to groundwater, temperature and drainage volumes.

Waterbody: Amiata

Type: Water spring

Description: The Amiata waterbody is composed of a volcanic aquifer not fed by rivers or lakes but fed by meteoric infiltration. This aquifer is accessed through Ermicciolo, Arbure, Bugnano and Galleria Alta water springs. The levels and volumes of the four sources are influenced by the parameters: rainfall, depth to groundwater, hydrometry, temperatures and drainage volumes.

Waterbody: Madonna di Canneto

Type: Water spring

Description: The Madonna di Canneto spring is situated at an altitude of 1010m above sea level in the Canneto valley. It does not consist of an aquifer and its source is supplied by the water catchment area of the river Melfa.

Waterbody: Lupa

Type: Water spring

Description: this water spring is located in the Rosciano Valley, on the left side of the Nera river. The waters emerge at an altitude of about 375 meters above sea level through a long draining tunnel that crosses, in its final section, lithotypes and essentially calcareous rocks. It provides drinking water to the city of Terni and the towns around it.

Waterbody: Arno

Type: River

Description: Arno is the second largest river in peninsular Italy and the main waterway in Tuscany and it has a relatively torrential regime, due to the nature of the surrounding soils (marl and impermeable clays). Arno results to be the main source of water supply of the metropolitan area of Florence-Prato-Pistoia. The availability of water for this waterbody is evaluated by checking the hydrometric level of the river at the section of Nave di Rosano.

Waterbody: Bilancino

Type: Lake

Description: Bilancino lake is an artificial lake located in the municipality of Barberino di Mugello (about 50 km from Florence). It is used to refill the Arno river during the summer months. Indeed, during the winter months, the lake is filled up and then, during the summer months, the water of the lake is poured into the Arno river.

Each waterbody has its own different features to be predicted. The table below shows the expected feature to forecast for each waterbody.

It is of the utmost importance to notice that some features like rainfall and temperature, which are present in each dataset, don’t go alongside the date. Indeed, both rainfall and temperature affect features like level, flow, depth to groundwater and hydrometry some time after it fell down. This means, for instance, that rain fell on 1st January doesn’t affect the mentioned features right the same day but some time later. As we don’t know how many days/weeks/months later rainfall affects these features, this is another aspect to keep into consideration when analyzing the dataset.

A short, tabular description of the waterbodies is available also downloading all datasets.

More information about the behavior of each kind of waterbody can be found at the following links:

Aquifer: https://en.wikipedia.org/wiki/Aquifer

Spring_(hydrology): https://en.wikipedia.org/wiki/Spring_(hydrology)

River: https://en.wikipedia.org/wiki/River

Lake: https://en.wikipedia.org/wiki/Lake

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LSTM implementation for practical solution - The goal is to create a mathematical model, for each category of waterbody (acquifers, water springs, river, lake) to predict the amount of water in each unique waterbody for a set time interval, based on (noisy) data.

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