# Uncovering the Depletion Patterns of Inland Water Bodies via Remote Sensing, Data Mining, and Statistical Analysis

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## Abstract

**:**

## 1. Introduction

## 2. Study Area

^{2}, is located in southwestern Iran in the area circumscribed by a rectangle at 29.32°–29.55° N and 52.69°–52.90° E. While the basin normally (based on data from 1987 to 2016) experiences mild variations in rainfall and temperature (Figure 2), the southern part of the watershed receives an average of approximately 250 mm of precipitation annually, and the northern and central parts of the basin receive as much as 480 mm. The basin’s average annual rainfall is approximately 390 mm. Average annual temperatures range from 18 to 19 °C, with a regional average of 19 °C [26]. Due to the region’s semi-arid climate, the stream network of the region is ephemeral; in most cases, streams and other water bodies only appear during wet seasons [26].

^{2}[25]. This begs the question: are the recently observed fluctuating patterns in line with the historic hydro-climatic behavior of the lake? If not, at what point did such changes start revealing themselves?

## 3. Methodology

#### 3.1. Detection of Water Bodies

#### 3.2. Pettit Test

_{0}) is that there is no abrupt change in the given time series. However, an alternative hypothesis (HA) is a statistically significant monotonic change-point in the time series. For a time series of continuous data x

_{i}, the test statistic U

_{t}

_{,N}is calculated at the tth time step [27]:

_{N}) is:

#### 3.3. Mann–Kendall Test

_{MK}) is defined [28,29]:

_{MK}is assumed to be asymptotically normal with a variance (${\sigma}_{{S}_{MK}}^{2}$) equal to [17]:

_{MK}) is calculated as follows [28,29]:

_{MK}indicates an upward trend in the time series dataset; a negative value indicates a downward trend in the data.

#### 3.4. Sen’s Slope Estimator

#### 3.5. Spearman’s Rank Correlation

_{i}= the difference in the ranks of the values of the two given paired variables. The test statistic (Z

_{S}) is calculated by [45]:

_{S}| > Z

_{α}, the null hypothesis is rejected.

#### 3.6. Artificial Neural Network (ANN)

## 4. Results and Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**Average monthly variation of rainfall, temperature, and streamflow in the Maharlou Lake watershed from 1987 to 2016.

**Figure 3.**Overview of the topology of the two proposed MLP models. (

**a**) is MLP model using extraneous factors and (

**b**) MLP model using historical date of lake surface area.

**Figure 6.**Prediction results of the lake’s surface area obtained from MLP model I. (

**a**) Predictions for the lake’s surface area (km

^{2}) and (

**b**) Residual from the model.

Parameter | Model I | Model II |
---|---|---|

Input | Rainfall, temperature, streamflow, lake’s surface area from last time step | Last 24 steps of lake’s surface area data |

layers | 2 | 2 |

No. of neurons of 1st layer | 25 | 20 |

No. of neurons of 2nd layer | 10 | 15 |

Output | One step of lake’s surface area data | 12 steps of lake’s surface area data |

Activation function between hidden layers | Relu | Relu |

Activation function between hidden layers and output layers | Linear | Linear |

Learning rate | 0.01 | 0.01 |

Optimizer | Adamax | Adamax |

Loss function | Mean squared error | Mean squared error |

Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Jumping point | 2007 | 2007 | 2007 | 2007 | 2007 | 2007 | 2007 | 2007 | 2007 | 2007 | 2001 | 2006 |

Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Trend component | −3.98 | −3.31 | −4.00 | −3.84 | −5.45 | −7.47 | −8.71 | −8.99 | −8.69 | −8.11 | −5.66 | −5.09 |

Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Dobaneh | - | - | 1999 | - | - | - | - | - | - | - | - | - |

Ghalat | - | - | 1999 | - | - | - | - | - | - | - | - | - |

Mehrabad-Ramjerd | - | - | 1999 | - | - | - | - | - | - | - | - | - |

Sarvestan | - | - | 1999 | - | - | - | - | - | - | - | - | - |

Shiraz (Sazman-e-Ab) | - | - | 1999 | - | - | - | - | - | - | - | - | - |

Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Fasa | - | - | 1999 | 1998 | 1998 | 1997 | 2002 | 2000 | 2009 | 2000 | - | - |

Sad-e-Dorodzan | - | - | 1999 | - | - | 2005 | 2005 | - | - | 1998 | - | - |

Shiraz (Synoptic) | - | 1998 | 1999 | - | - | 1997 | - | - | - | 1996 | - | - |

Zarghan | - | - | 1999 | - | 1998 | 2005 | 2005 | - | - | 2000 | - | - |

Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Chenar | - | - | - | - | - | - | - | - | - | - | - | 1987 |

Chenar-Sokhteh | - | - | - | - | 1999 | - | - | - | - | - | - | - |

Pol-e-Fasa | 2006 | 2006 | 1999 | 1999 | 1999 | - | - | - | - | - | - | - |

Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Dobaneh | - | - | −1.75 | - | - | - | - | - | - | - | - | - |

Ghalat | - | - | −2.33 | - | - | - | - | - | - | - | 1.44 | - |

Mehrabad-Ramjerd | - | - | - | - | - | - | - | - | - | - | - | - |

Sarvestan | - | - | −1.40 | - | - | - | - | - | - | - | 0.20 | - |

Shiraz (Sazman-e-Ab) | - | - | −1.76 | - | - | - | - | - | - | - | 0.53 | - |

Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Fasa | - | 0.07 | 0.10 | 0.07 | 0.09 | 0.08 | 0.07 | 0.04 | 0.04 | 0.06 | - | - |

Sad-e-Dorodzan | - | 0.08 | - | - | 0.05 | 0.08 | 0.05 | - | - | 0.06 | - | - |

Shiraz (Synoptic) | - | 0.07 | 0.09 | - | - | 0.04 | - | - | - | - | - | - |

Zarghan | - | 0.05 | 0.08 | - | 0.07 | 0.05 | - | - | - | 0.05 | - | - |

Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Chenar | - | - | - | - | - | - | - | - | - | - | - | - |

Chenar-Sookhteh | - | - | - | - | −0.01 | - | - | - | - | - | - | - |

Pol-e-Fasa | −0.11 | −0.18 | −0.23 | −0.10 | −0.05 | - | - | - | - | - | - | −0.07 |

**Table 10.**Spearman’s test results to identify any correlation between lake’s surface area and hydro-climatic variables.

Temperature | Rainfall | Streamflow | |
---|---|---|---|

Jan | 0.672 | ||

Feb | 0.718 | ||

Mar | −0.614 | 0.484 | 0.758 |

Aar | 0.771 | ||

May | −0.624 | 0.759 | |

Jun | −0.619 | 0.525 | 0.708 |

Jul | −0.482 | 0.292 | |

Aug | |||

Sea | −0.447 | ||

Oct | −0.442 | ||

Nov | 0.420 | ||

Dec |

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**MDPI and ACS Style**

Zolghadr-Asli, B.; Naghdyzadegan Jahromi, M.; Wan, X.; Enayati, M.; Naghdizadegan Jahromi, M.; Tahmasebi Nasab, M.; Tiefenbacher, J.P.; Pourghasemi, H.R.
Uncovering the Depletion Patterns of Inland Water Bodies via Remote Sensing, Data Mining, and Statistical Analysis. *Water* **2023**, *15*, 1508.
https://doi.org/10.3390/w15081508

**AMA Style**

Zolghadr-Asli B, Naghdyzadegan Jahromi M, Wan X, Enayati M, Naghdizadegan Jahromi M, Tahmasebi Nasab M, Tiefenbacher JP, Pourghasemi HR.
Uncovering the Depletion Patterns of Inland Water Bodies via Remote Sensing, Data Mining, and Statistical Analysis. *Water*. 2023; 15(8):1508.
https://doi.org/10.3390/w15081508

**Chicago/Turabian Style**

Zolghadr-Asli, Babak, Mojtaba Naghdyzadegan Jahromi, Xi Wan, Maedeh Enayati, Maryam Naghdizadegan Jahromi, Mohsen Tahmasebi Nasab, John P. Tiefenbacher, and Hamid Reza Pourghasemi.
2023. "Uncovering the Depletion Patterns of Inland Water Bodies via Remote Sensing, Data Mining, and Statistical Analysis" *Water* 15, no. 8: 1508.
https://doi.org/10.3390/w15081508