### 1. Introduction

### 2. Materials and Methods

### 2.1. Study Area

^{2}. The average elevation of Sylhet district is 35 m. The climate of Sylhet is torrid monsoon with predominantly thermal and moist summer and a relatively cold winter. The annual average highest temperatures are 23°C (Aug-Oct) and the average lowest temperature is 7°C (Jan) because the settlement is in the monsoon climate zone. In between May and September, approximately 80% of the annual average precipitation (3,334 mm) occurs.

^{2}which indicates a tremendous pressure as it is the major freshwater source.

### 2.2. Data Collection

### 2.3. Methods of Analysis

#### 2.3.1. Mann-Kendall (MK) test

*S*, is assumed to be 0 (for examples, no trend).

*S*is incremented by 1 if the later time period of a date value is higher than another previous time period of the data value. Contrarily,

*S*is incremented by 1, if the data value from an additional time period is lower than the previous time period. The procedure to compute this probability is stated in [27–28].

#### 2.3.2. Sen’s estimator of slope

*Q*is the slope between data points

*x*

*and*

_{j}*x*

*,*

_{k}*x*

*is the data measurement at time*

_{j}*j*,

*x*

*is the data measurement at time*

_{k}*k*and

*j*is the time after time

*k*.

### 2.4. Geostatistical Method

### 2.5. Spatial Prediction Method

#### 2.5.1. Kriging

*z*, of the landscape as a function of the geographic location) at unsighted whereabouts from the regard of its value at nearby locations [31]. This method is best suitable for normally distributed data. The data need to be resolved into normally distributed data using the transformation methods; if they are not normally distributed. The most usual transformation type is a logarithmic method because of its simplicity. The log transformation is as follows:

*Z*(

*s*) > 0 Where

*Z*(

*s*) is observed data,

*Y*(

*s*) is transformed normal data and in is the natural logarithm. Detailed discussions of kriging methods and their descriptions can be found in [30]. In kriging method, the most commonly used variogram models are spherical, exponential and Gaussian.

##### (4)

$$\mathbf{Exponential}\hspace{0.17em}\mathbf{model:}\hspace{0.17em}\gamma (h)={c}_{0}+{c}_{1}\left\{1-\text{exp}\left(\frac{h}{a}\right)\right\}$$##### (5)

$$\mathbf{Gaussian}\hspace{0.17em}\mathbf{model:}\hspace{0.17em}\gamma (h)={c}_{0}+{c}_{1}\left\{1-\text{exp}\left(\frac{{h}^{2}}{{a}^{2}}\right)\right\}$$##### (6)

$$\begin{array}{l}\mathbf{Spherical}\hspace{0.17em}\mathbf{modle:}\hspace{0.17em}\gamma (h)=\left[{c}_{0}+{c}_{1}\left\{\frac{3h}{2a}-\frac{1}{2}{\left(\frac{h}{a}\right)}^{3}\right\}\right]\hspace{0.17em}\text{for\hspace{0.17em}}0<0<a\\ {c}_{0}+{c}_{1}\hspace{0.17em}\text{for\hspace{0.17em}}h\ge a\end{array}$$*γ*(

*h*) is semi variance,

*h*is lag,

*a*is the range,

*C*

*is Nugget variance,*

_{o}*c*

_{0}+

*c*

_{1}= sill.

### 2.6. Generation of Best Fitted Models

#### 2.6.1. Root Mean Square Error (RMSE)

#### 2.6.2. Mean Square Error (MSE)

#### 2.6.3. Root Mean Square Standardized Effect (RMSSE)

##### (9)

$$RMSSE=\sqrt{\frac{1}{n}{\sum}_{i=1}^{n}{[\{\tilde{Z}(Xi)-Z(Xi)\}/{\tilde{\sigma}}^{2}(Xi)]}^{2}}$$*X*

_{i}) is the Kriging variance for location

*X*

_{i}. So, using ordinary kriging method each groundwater quality parameters can be generated.

### 3. Results and Discussion

### 3.1. Result of Mann-Kendall Test and Sen’s Estimator of Slope

*Z*ranging from −0.50 to 6.85. The trend is said to be decreasing if

*Z*is negative and the computed probability is greater than the level of significance and increasing if

*Z*is positive and the computed probability is greater than the level of significance. If the computed probability is less than the level of significance, there is no trend. From Table 2, it is evident that some of the stations in the Sylhet districts show no significant trend. However, Balaganj (GT9108002) show a very pronounced positive trend (increasing groundwater table depth) at

*Z*equals to 6.85. Gowainghat and Jaintapur show relatively weaker negative trends at −3.53 and −1.65, respectively. Although there are many stations in Table 2 that showed relatively large upwards trends (increasing water table depth), only four are statistically significant. These Balaganj (GT9108001) (0.062 m/y), Balaganj (GT9108002) (0.063 m/y), Bishwhanath (GT9120006) (0.041 m/y), Golabganj (GT9138010) (0.088 m/y), Gowainghat (GT9141015) (0.0250 m/y).